diff --git "a/1422.jsonl" "b/1422.jsonl" new file mode 100644--- /dev/null +++ "b/1422.jsonl" @@ -0,0 +1,451 @@ +{"seq_id": "85678210", "text": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as mpatches\n\ndef plot_hist(data, value, title, xlabel, ylabel, ylim, colors, bin=10, metrics=True):\n \"\"\"\n \" data: list of dataframes\n \" value: value from dataframes to be plotted\n \" title: title of histogram\n \" xlabel: x axis label\n \" ylabel: y axis label\n \" ylim: y limits \n \" colors: list of colors for each dataframe's bars\n \" bin: number of histogram bins\n \" metrics: display mean/median on histogram\n \"\"\"\n\n metric_txt = \"\"\n fig, ax = plt.subplots()\n bins = np.linspace(-1, 1, bin)\n patches = []\n for i, d in enumerate(data):\n name = d.name\n mean = \"Mean: %.4f\" % d[value].mean()\n median = \"Median: %.4f\" % d[value].median()\n metric_txt = metric_txt + name + \"\\n \" + mean + \"\\n \" + median + \"\\n\"\n\n d.hist(column=value, bins=bins, grid=False, ax=ax, color=colors[i])\n patches.append(mpatches.Patch(color=colors[i], label=name))\n\n plt.title(title)\n plt.xlabel(xlabel)\n plt.ylabel(ylabel)\n plt.legend(handles=patches)\n\n ax.set_ylim([0,ylim])\n ax.text(0.05, 0.95, metric_txt, verticalalignment=\"top\", transform=ax.transAxes)\n\n plt.savefig(\"%s_%s.png\" % (title, value))", "sub_path": "utils/data_vis/hist.py", "file_name": "hist.py", "file_ext": "py", "file_size_in_byte": 1313, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "matplotlib.pyplot.subplots", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.patches.Patch", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}]} +{"seq_id": "471208309", "text": "import numpy as np\r\nimport matplotlib.pyplot as mp\r\nimport matplotlib.animation as ma\r\nimport random\r\n\r\nn_samples=100\r\n#开始时所有的值用0来填\r\n#其中2是两列的意思\r\ndata=np.zeros(n_samples,dtype=[\r\n ('position',float,2),\r\n ('size',float,1),\r\n ('growth',float,1),\r\n ('color',float,4)])\r\n#R.uniform(a,b)\t返回[a,b) 区间内的随机实数\r\ndata['position']=np.random.uniform(0,1,(n_samples,2))\r\ndata['size']=np.random.uniform(50,750,n_samples)\r\ndata['growth']=np.random.uniform(30,150,n_samples)\r\ndata['color']=np.random.uniform(0,1,(n_samples,4))\r\nmp.figure('Bubble',facecolor='lightgray')\r\nmp.title('Bubble',fontsize=20)\r\nmp.xlabel('x',fontsize=14)\r\nmp.ylabel('y',fontsize=14)\r\nmp.tick_params(labelsize=10)\r\nmp.grid(linestyle=\":\")\r\n#一方面要变，同时还要把变化的值告诉函数\r\nplot=mp.scatter(data['position'][:,0],data['position'][:,1],s=data['size'],c=data['color'])\r\n\r\n#gcf获取当前图\r\n\r\n#接收返回值以后就会变成进程级的返回值\r\ndef update(number):\r\n data['size']+=data['growth']\r\n #不能总让第一个破，所以循环让气泡从1-100循环破，使用模运算就行\r\n index=number%n_samples\r\n data['position'][index]=np.random.uniform(0,1,2)\r\n data['size'][index]=0\r\n data['growth'][index]=np.random.uniform(30.150)\r\n data['color'][index]=np.random.uniform(0,1,4)\r\n #变化后的信息都要告诉\r\n plot.set_offsets(data['position'])\r\n plot.set_sizes(data['size'])\r\n plot.set_facecolors(data['color'])\r\n\r\n\r\n#需要接收返回值，不然返回值会随语句的执行而消失\r\nanim=ma.FuncAnimation(mp.gcf(),update,interval=10)\r\nmp.show()\r\n\r\n", "sub_path": "bub.py", "file_name": "bub.py", "file_ext": "py", "file_size_in_byte": 1653, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.zeros", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 15, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 18, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "numpy.random.uniform", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 35, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 37, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 38, "usage_type": "attribute"}, {"api_name": "matplotlib.animation.FuncAnimation", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.animation", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}]} +{"seq_id": "169936381", "text": "from matplotlib import pyplot\n\n\n# Best optimizer\ndef find_optimizer(trainX, trainy, testX, testy):\n # create learning curves for different optimizers\n optimizer = ['sgd', 'rmsprop', 'adagrad', 'adam']\n for i in range(len(optimizer)):\n # determine the plot number\n plot_no = 220 + (i + 1)\n pyplot.subplot(plot_no)\n # fit Model and plot learning curves for an optimizer\n # fit_model(trainX, trainy, testX, testy, optimizer[i])\n # show learning curves\n pyplot.show()\n\n\n# Best learning rate\ndef find_learning_rate(trainX, trainy, testX, testy):\n # create learning curves for different learning rates\n learning_rates = [1E-0, 1E-1, 1E-2, 1E-3, 1E-4, 1E-5, 1E-6, 1E-7]\n for i in range(len(learning_rates)):\n # determine the plot number\n plot_no = 420 + (i + 1)\n pyplot.subplot(plot_no)\n # fit Model and plot learning curves for a learning rate\n # fit_model(trainX, trainy, testX, testy, learning_rates[i])\n # show learning curves\n pyplot.show()\n\n\n# Best momentum\ndef find_momentum(trainX, trainy, testX, testy):\n # create learning curves for different momentums\n momentums = [0.0, 0.5, 0.9, 0.99]\n for i in range(len(momentums)):\n # determine the plot number\n plot_no = 220 + (i + 1)\n pyplot.subplot(plot_no)\n # fit Model and plot learning curves for a momentum\n # fit_model(trainX, trainy, testX, testy, momentums[i])\n # show learning curves\n pyplot.show()\n", "sub_path": "optimizations.py", "file_name": "optimizations.py", "file_ext": "py", "file_size_in_byte": 1502, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "matplotlib.pyplot.subplot", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}]} +{"seq_id": "95899786", "text": "from plaid.errors import APIError, PlaidError\n\n\ndef test_from_response():\n response = {\n 'display_message': None,\n 'error_type': 'API_ERROR',\n 'error_code': 'INTERNAL_SERVER_ERROR',\n 'error_message': 'an unexpected error occurred',\n 'request_id': 'abc123',\n 'causes': [\n {\n 'error_type': 'API_ERROR',\n 'error_code': 'INTERNAL_SERVER_ERROR',\n 'error_message': 'an unexpected error occurred',\n 'item_id': '456',\n },\n ],\n }\n\n error = PlaidError.from_response(response)\n assert isinstance(error, APIError)\n assert error.code == 'INTERNAL_SERVER_ERROR'\n assert error.message == 'an unexpected error occurred'\n\n assert len(error.causes) == 1\n cause = error.causes[0]\n assert cause.type == 'API_ERROR'\n assert cause.code == 'INTERNAL_SERVER_ERROR'\n assert cause.message == 'an unexpected error occurred'\n assert cause.item_id == '456'\n", "sub_path": "tests/unit/test_errors.py", "file_name": "test_errors.py", "file_ext": "py", "file_size_in_byte": 995, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "plaid.errors.PlaidError.from_response", "line_number": 21, "usage_type": "call"}, {"api_name": "plaid.errors.PlaidError", "line_number": 21, "usage_type": "name"}, {"api_name": "plaid.errors.APIError", "line_number": 22, "usage_type": "argument"}]} +{"seq_id": "149879917", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.shortcuts import render, redirect\nfrom django.template.defaultfilters import slugify\nfrom django.contrib.auth.decorators import login_required\nfrom django.http import Http404\nfrom wines.models import Wine\nfrom wines.forms import WineForm\n\ndef index(request):\n wines = Wine.objects.all()\n return render(request, 'index.html', {'wines': wines,})\n\ndef wine_detail(request, slug):\n wine = Wine.objects.get(slug=slug)\n return render(request, 'wines/wine_detail.html', {'wine': wine,})\n\n@login_required\ndef edit_wine(request, slug):\n wine = Wine.objects.get(slug=slug)\n if wine.user != request.user:\n raise Http404\n form_class = WineForm\n if request.method == 'POST':\n form = form_class(data=request.POST, instance=wine)\n if form.is_valid():\n form.save()\n return redirect('wine_detail', slug=wine.slug)\n else:\n form = form_class(instance=wine)\n return render(request, 'wines/edit_wine.html', {'wine': wine,'form': form,})\n\ndef create_wine(request):\n form_class = WineForm\n if request.method == 'POST':\n form = form_class(request.POST)\n if form.is_valid():\n wine = form.save(commit=False)\n wine.user = request.user\n wine.slug = slugify(wine.name)\n wine.save()\n return redirect('wine_detail', slug=wine.slug)\n else:\n form = form_class()\n return render(request, 'wines/create_wine.html', {'form': form,})\n\n\ndef browse_by_name(request, initial=None):\n if initial:\n wines = Wine.objects.filter(name__istartswith=initial)\n wines = wines.order_by('name')\n else:\n wines = Wine.objects.all().order_by('name')\n\n return render(request, 'search/search.html', {'wines': wines, 'initial': initial,})\n", "sub_path": "jenswinery/wines/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1855, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "wines.models", "line_number": 12, "usage_type": "name"}, {"api_name": "wines.models.Wine.objects.all", "line_number": 12, "usage_type": "call"}, {"api_name": "wines.models.Wine.objects", "line_number": 12, "usage_type": "attribute"}, {"api_name": "wines.models.Wine", "line_number": 12, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 13, "usage_type": "call"}, {"api_name": "wines.models", "line_number": 13, "usage_type": "name"}, {"api_name": "wines.models.Wine.objects.get", "line_number": 16, "usage_type": "call"}, {"api_name": "wines.models.Wine.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "wines.models.Wine", "line_number": 16, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 17, "usage_type": "call"}, {"api_name": "wines.models.Wine.objects.get", "line_number": 21, "usage_type": "call"}, {"api_name": "wines.models.Wine.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "wines.models.Wine", "line_number": 21, "usage_type": "name"}, {"api_name": "django.http.Http404", "line_number": 23, "usage_type": "name"}, {"api_name": "wines.forms.WineForm", "line_number": 24, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 29, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 32, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 19, "usage_type": "name"}, {"api_name": "wines.forms.WineForm", "line_number": 35, "usage_type": "name"}, {"api_name": "django.template.defaultfilters.slugify", "line_number": 41, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 43, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 46, "usage_type": "call"}, {"api_name": "wines.models", "line_number": 51, "usage_type": "name"}, {"api_name": "wines.models.Wine.objects.filter", "line_number": 51, "usage_type": "call"}, {"api_name": "wines.models.Wine.objects", "line_number": 51, "usage_type": "attribute"}, {"api_name": "wines.models.Wine", "line_number": 51, "usage_type": "name"}, {"api_name": "wines.models", "line_number": 52, "usage_type": "name"}, {"api_name": "wines.models.order_by", "line_number": 52, "usage_type": "call"}, {"api_name": "wines.models", "line_number": 54, "usage_type": "name"}, {"api_name": "wines.models.Wine.objects.all", "line_number": 54, "usage_type": "call"}, {"api_name": "wines.models.Wine.objects", "line_number": 54, "usage_type": "attribute"}, {"api_name": "wines.models.Wine", "line_number": 54, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 56, "usage_type": "call"}, {"api_name": "wines.models", "line_number": 56, "usage_type": "name"}]} +{"seq_id": "284946918", "text": "# clears all the redis servers used according to a config file\n\nimport argparse\nimport json\nimport redis\n\nNUM_DBS = 16\n\ndef flush_db(config_file):\n with open(config_file) as f:\n j = json.loads(f.read())\n for pair in j.values():\n port = pair[0]\n db_num = pair[1]\n r = redis.StrictRedis(port=port, db=db_num)\n r.flushdb()\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description='Clear Redis DBs for a config file')\n parser.add_argument('config_file', help='either LOCAL.json or PROD.json')\n args = parser.parse_args()\n flush_db(args.config_file)\n", "sub_path": "flushdb.py", "file_name": "flushdb.py", "file_ext": "py", "file_size_in_byte": 593, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "json.loads", "line_number": 11, "usage_type": "call"}, {"api_name": "redis.StrictRedis", "line_number": 15, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "1397414", "text": "from django.shortcuts import render, HttpResponse, HttpResponseRedirect\nfrom .models import Book\nfrom django.utils import timezone\nfrom django.urls import reverse\n\n# Create your views here.\ndef index(request):\n return HttpResponse('欢迎进入图书管理系统')\n\ndef detail(request):\n book_list = Book.objects.order_by('-pub_date')\n context = {'book_list': book_list}\n return render(request, 'lib/detail.html', context)\n\ndef addBook(request):\n if request.method == 'POST':\n name = request.POST['name']\n author = request.POST['author']\n pub_house = request.POST['pub_house']\n\n temp_book = Book(name=name, author=author, pub_house=pub_house, pub_date=timezone.now())\n temp_book.save()\n\n return HttpResponseRedirect(reverse('lib:detail'))\n\n else:\n return render(request, 'lib/addbook.html')\n\n\ndef delBook(request, book_id):\n bookID = book_id\n Book.objects.filter(id=bookID).delete()\n return HttpResponseRedirect(reverse('lib:detail'))\n\ndef editBook(request, book_id):\n bookID = book_id\n bookID_obj = Book.objects.get(id=bookID)\n temp_book = Book.objects.filter(id=bookID)\n context = {'bookID_obj': bookID_obj}\n if request.method == 'POST':\n name = request.POST['name']\n author = request.POST['author']\n pub_house = request.POST['pub_house']\n\n temp_book.update(name=name, author=author, pub_house=pub_house, pub_date=timezone.now())\n\n return HttpResponseRedirect(reverse('lib:detail'))\n\n else:\n return render(request, 'lib/editbook.html', context)", "sub_path": "lib/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1582, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "django.shortcuts.HttpResponse", "line_number": 8, "usage_type": "call"}, {"api_name": "models.Book.objects.order_by", "line_number": 11, "usage_type": "call"}, {"api_name": "models.Book.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "models.Book", "line_number": 11, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 13, "usage_type": "call"}, {"api_name": "models.Book", "line_number": 21, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 21, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 21, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 24, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 24, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 27, "usage_type": "call"}, {"api_name": "models.Book.objects.filter", "line_number": 32, "usage_type": "call"}, {"api_name": "models.Book.objects", "line_number": 32, "usage_type": "attribute"}, {"api_name": "models.Book", "line_number": 32, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 33, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 33, "usage_type": "call"}, {"api_name": "models.Book.objects.get", "line_number": 37, "usage_type": "call"}, {"api_name": "models.Book.objects", "line_number": 37, "usage_type": "attribute"}, {"api_name": "models.Book", "line_number": 37, "usage_type": "name"}, {"api_name": "models.Book.objects.filter", "line_number": 38, "usage_type": "call"}, {"api_name": "models.Book.objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "models.Book", "line_number": 38, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 45, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 45, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 47, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 47, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "556472148", "text": "import json\r\nimport logging\r\nimport requests # pip install requests\r\nfrom setting.settings import SCC_URL\r\nfrom requests.exceptions import ConnectionError\r\n\r\nclass ApiRequest:\r\n def __init__(self):\r\n self.logger = logging.getLogger(\"scc\")\r\n self.url = SCC_URL\r\n self.headers = {\r\n 'Accept': 'application/json',\r\n 'Authorization': 'Basic aXB0djppcHR2QDEyMzs7',\r\n 'Cache-Control': 'no-cache',\r\n 'Content-Type': 'application/json'\r\n }\r\n\r\n def get(self):\r\n self.logger.warning(\"Get %s\"%(self.url))\r\n try:\r\n rsp = requests.get(self.url, headers=self.headers, timeout=5)\r\n self.logger.critical(\"status: %d, message: %s\"%(0, str(rsp.json())))\r\n except ConnectionError as e:\r\n self.logger.error(\"status: %d, message: %s\"%(1, str(e)))\r\n return None\r\n return rsp \r\n\r\n def put(self, data):\r\n self.logger.warning(\"message: Data post contain: %s\"%(str(data)))\r\n try:\r\n rsp = requests.put(self.url, data=data, headers=self.headers, timeout=5)\r\n self.logger.critical(\"status: %d, message: %s\"%(0, str(rsp.json())))\r\n except ConnectionError as e:\r\n self.logger.error(\"status: %d, message: %s\"%(1, str(e)))\r\n return None\r\n return rsp \r\n\r\n def post(self, data):\r\n try:\r\n rsp = requests.post(self.url, data=data, headers=self.headers, timeout=5)\r\n self.logger.critical(\"status: %d, message: %s\"%(0, str(rsp.json())))\r\n # print rsp.text\r\n except ConnectionError as e:\r\n self.logger.error(\"status: %d, message: %s\"%(1, str(e)))\r\n return None\r\n return rsp\r\n", "sub_path": "DAL/scc_request_v2.py", "file_name": "scc_request_v2.py", "file_ext": "py", "file_size_in_byte": 1740, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "setting.settings.SCC_URL", "line_number": 10, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 21, "usage_type": "call"}, {"api_name": "requests.exceptions.ConnectionError", "line_number": 23, "usage_type": "name"}, {"api_name": "requests.put", "line_number": 31, "usage_type": "call"}, {"api_name": "requests.exceptions.ConnectionError", "line_number": 33, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 40, "usage_type": "call"}, {"api_name": "requests.exceptions.ConnectionError", "line_number": 43, "usage_type": "name"}]} +{"seq_id": "53097561", "text": "from typing import List, Optional\nimport pandas as pd\nimport numpy as np\n\nfrom model_utils import (\n build_regressor,\n build_classifier,\n train_regressor,\n train_classifier,\n save_torch_model,\n subset_data,\n)\n\n\ndef fit(\n X: pd.DataFrame,\n y: pd.Series,\n output_dir: str,\n class_order: Optional[List[str]] = None,\n row_weights: Optional[np.ndarray] = None,\n **kwargs,\n) -> None:\n \"\"\"\n This hook must be implemented with your fitting code, for running drum in the fit mode.\n This hook MUST ALWAYS be implemented for custom tasks.\n For inference models, this hook can stick around unimplemented, and won’t be triggered.\n Parameters\n ----------\n X: pd.DataFrame - training data to perform fit on\n y: pd.Series - target data to perform fit on\n output_dir: the path to write output. This is the path provided in '--output' parameter of the\n 'drum fit' command.\n class_order : A two element long list dictating the order of classes which should be used for\n modeling. Class order will always be passed to fit by DataRobot for classification tasks,\n and never otherwise. When models predict, they output a likelihood of one class, with a\n value from 0 to 1. The likelihood of the other class is 1 - this likelihood. Class order\n dictates that the first element in the list will be the 0 class, and the second will be the\n 1 class.\n row_weights: An array of non-negative numeric values which can be used to dictate how important\n a row is. Row weights is only optionally used, and there will be no filtering for which\n custom models support this. There are two situations when values will be passed into\n row_weights, during smart downsampling and when weights are explicitly provided by the user\n kwargs: Added for forwards compatibility\n Returns\n -------\n Nothing\n \"\"\"\n # keep only numeric features\n X_train = subset_data(X)\n # Feel free to delete which ever one of these you aren't using\n if class_order:\n estimator, optimizer, criterion = build_classifier(X_train, len(class_order))\n train_classifier(X_train, y, estimator, optimizer, criterion)\n artifact_name = \"torch_class.pth\"\n else:\n estimator, optimizer, criterion = build_regressor(X_train)\n train_regressor(X_train, y, estimator, optimizer, criterion)\n artifact_name = \"torch_reg.pth\"\n\n # NOTE: We currently set a 10GB limit to the size of the serialized model\n save_torch_model(estimator, output_dir, artifact_name)\n\n\ndef transform(data, model):\n \"\"\"\n apply the same subsetting at prediction time as during fit\n\n Parameters\n ----------\n data : is the dataframe given to DRUM to make predictions on\n model : is the deserialized model loaded by DRUM or by `load_model`, if supplied\n\n Returns\n -------\n Transformed data\n \"\"\"\n return subset_data(data)\n", "sub_path": "task_templates/pipelines/python3_pytorch/custom.py", "file_name": "custom.py", "file_ext": "py", "file_size_in_byte": 2947, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pandas.DataFrame", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 17, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 19, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 19, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 20, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 20, "usage_type": "attribute"}, {"api_name": "model_utils.subset_data", "line_number": 49, "usage_type": "call"}, {"api_name": "model_utils.build_classifier", "line_number": 52, "usage_type": "call"}, {"api_name": "model_utils.train_classifier", "line_number": 53, "usage_type": "call"}, {"api_name": "model_utils.build_regressor", "line_number": 56, "usage_type": "call"}, {"api_name": "model_utils.train_regressor", "line_number": 57, "usage_type": "call"}, {"api_name": "model_utils.save_torch_model", "line_number": 61, "usage_type": "call"}, {"api_name": "model_utils.subset_data", "line_number": 77, "usage_type": "call"}]} +{"seq_id": "469039842", "text": "# uncompyle6 version 3.6.7\n# Python bytecode 3.6 (3379)\n# Decompiled from: Python 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)]\n# Embedded file name: build/bdist.macosx-10.7-x86_64/egg/airflow/contrib/operators/gcs_delete_operator.py\n# Compiled at: 2019-09-11 03:47:34\n# Size of source mod 2**32: 3200 bytes\nfrom airflow.contrib.hooks.gcs_hook import GoogleCloudStorageHook\nfrom airflow.models import BaseOperator\nfrom airflow.utils.decorators import apply_defaults\n\nclass GoogleCloudStorageDeleteOperator(BaseOperator):\n \"\"\"GoogleCloudStorageDeleteOperator\"\"\"\n template_fields = ('bucket_name', 'prefix', 'objects')\n\n @apply_defaults\n def __init__(self, bucket_name, objects=None, prefix=None, google_cloud_storage_conn_id='google_cloud_default', delegate_to=None, *args, **kwargs):\n self.bucket_name = bucket_name\n self.objects = objects\n self.prefix = prefix\n self.google_cloud_storage_conn_id = google_cloud_storage_conn_id\n self.delegate_to = delegate_to\n if not objects is not None:\n if not prefix is not None:\n raise AssertionError\n (super(GoogleCloudStorageDeleteOperator, self).__init__)(*args, **kwargs)\n\n def execute(self, context):\n hook = GoogleCloudStorageHook(google_cloud_storage_conn_id=(self.google_cloud_storage_conn_id),\n delegate_to=(self.delegate_to))\n if self.objects:\n objects = self.objects\n else:\n objects = hook.list(bucket=(self.bucket_name), prefix=(self.prefix))\n self.log.info('Deleting %s objects from %s', len(objects), self.bucket_name)\n for object_name in objects:\n hook.delete(bucket=(self.bucket_name), object=object_name)", "sub_path": "pycfiles/apache_ariatosca-0.2.0-py2-none-any/gcs_delete_operator.cpython-36.py", "file_name": "gcs_delete_operator.cpython-36.py", "file_ext": "py", "file_size_in_byte": 1759, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "airflow.models.BaseOperator", "line_number": 11, "usage_type": "name"}, {"api_name": "airflow.utils.decorators.apply_defaults", "line_number": 15, "usage_type": "name"}, {"api_name": "airflow.contrib.hooks.gcs_hook.GoogleCloudStorageHook", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "5277036", "text": "##############################################################################\n#\n# Usage example for the procedure loess_2d\n#\n# MODIFICATION HISTORY:\n# V1.0: Written by Michele Cappellari, Oxford 26 February 2014\n#\n##############################################################################\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom cap_loess_2d import loess_2d\nfrom cap_plot_velfield import plot_velfield\n\ndef test_loess_2d():\n \"\"\"\n Usage example for loess_2d\n\n \"\"\"\n n = 200\n x = np.random.uniform(-1,1,n)\n y = np.random.uniform(-1,1,n)\n z = x**2 - y**2\n sigz = 0.2\n zran = np.random.normal(z, sigz)\n\n zout, wout = loess_2d(x, y, zran)\n\n plt.clf()\n plt.subplot(131)\n plot_velfield(x, y, z)\n plt.title(\"Underlying Function\")\n\n plt.subplot(132)\n plot_velfield(x, y, zran)\n plt.title(\"With Noise Added\")\n\n plt.subplot(133)\n plot_velfield(x, y, zout)\n plt.title(\"LOESS Recovery\")\n plt.pause(1)\n\n#------------------------------------------------------------------------\n\nif __name__ == '__main__':\n test_loess_2d()\n", "sub_path": "loess/test_loess_2d.py", "file_name": "test_loess_2d.py", "file_ext": "py", "file_size_in_byte": 1098, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "numpy.random.uniform", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 26, "usage_type": "attribute"}, {"api_name": "cap_loess_2d.loess_2d", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "cap_plot_velfield.plot_velfield", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "cap_plot_velfield.plot_velfield", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "cap_plot_velfield.plot_velfield", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}]} +{"seq_id": "54146420", "text": "import sqlite3\n\nclass Mascota():\n def __init__(self, nombre, especie, edad):\n self.nombre = nombre\n self.especie = especie\n self.edad = edad\n\n def __str__(self):\n return \"Nombre de la mascota: {} Especie: {} Edad: {}\".format(self.nombre, self.especie, self.edad)\n\n def __eq__(self, taco):\n if Mascota.nombre != self.nombre:\n return False\n\n if Mascota.especie != self.especie:\n return False\n\n if Mascota.edad != self.edad:\n return False\n return True\n\nclass sqlite():\n def __init__(self, archivo):\n self.conn = sqlite3.connect(archivo)\n\n def save(self, Mascota):\n cur = self.conn.cursor()\n values = (Mascota.nombre, Mascota.especie, Mascota.edad)\n cur.execute(\"INSERT INTO mascotas (id,nombre, especie, edad) VALUES (null,?,?,?)\", values)\n self.connection.commit()\n\n def getAllMascota(self):\n cur = self.conn.cursor()\n mm = cur.execute(\"SELECT * FROM MASCOTA\").fetchall()\n\n mascota = []\n for m in mm:\n mascota.append(Mascota(m[1], m[2], m[3]))\n return mascota\n\n\ndef saveMascota(Mascota, db):\n db.save(Mascota)\n\ndef showMascota(db):\n mascota = db.getAllMascotas()\n return mascota\n\nif __name__ == \"__main__\":\n mascota1 = Mascota(\"Sorullo\", \"perro\", 10)\n mascota2 = Mascota(\"Sorulla\", \"perro\", 2)\n mascota3 = Mascota(\"Gigio\", \"perro\", 18)\n mascota4 = Mascota(\"Rex\", \"perro\", 10)\n mascota5 = Mascota(\"Gigin\", \"perro\", 12)\n mascota6 = Mascota(\"Taco\", \"perro\", 3)\n mascota7 = Mascota(\"Zeus\", \"perro\", 3)\n mascota8 = Mascota(\"Bodoque\", \"perro\", 3)\n\n db = sqlite(\"Mascotas.db\")\n # cur = db.conn.cursor()\n # cur.execute(\"CREATE TABLE TACOS (id INTEGER PRIMARY KEY AUTOINCREMENT,tortilla TEXT, guiso TEXT, salsa TEXT)\")\n\n\n saveMascota(mascota1, db)\n saveMascota(mascota2, db)\n saveMascota(mascota3, db)\n saveMascota(mascota4, db)\n saveMascota(mascota5, db)\n saveMascota(mascota6, db)\n saveMascota(mascota7, db)\n saveMascota(mascota8, db)\n\n print(*showMascota(db), sep=\"\\n\")", "sub_path": "ene-jun-2019/NoemiEstherFloresPardo/Practica7/Mascotas.py", "file_name": "Mascotas.py", "file_ext": "py", "file_size_in_byte": 2118, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sqlite3.connect", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "455942654", "text": "import json\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef parse_arguments():\n parser = argparse.ArgumentParser(description='')\n parser.add_argument('-o', '--output', type=float, default='phase_space.png', help='filename')\n\n return parser.parse_args()\n\ndef phase_space_plot():\n\n def _diff(dd):\n vc = np.array(dd['vc'])\n x = np.array(dd['x'])\n vdiff = np.linalg.norm(vc[:,0]-vc[:,1])\n xdiff = np.linalg.norm(x[:,0]-x[:,1])\n return xdiff, vdiff\n\n args = parse_arguments()\n\n data = json.load(open('logger.out', 'r'))\n xv = np.array([_diff(dd) for dd in data])\n plt.scatter(xv[:,0], xv[:,1], marker='x')\n plt.xlabel('x')\n plt.ylabel('v')\n plt.savefig(args.output)\n", "sub_path": "phase_space.py", "file_name": "phase_space.py", "file_ext": "py", "file_size_in_byte": 737, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "numpy.array", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 17, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}]} +{"seq_id": "553025952", "text": "__author__ = 'sevag'\n\nimport unittest\nimport io\nfrom collections import OrderedDict\n\nfrom transcriber.output import plot_from_dict\n\n\nclass OutputTests(unittest.TestCase):\n def test_plot_from_dict_produces_png(self):\n title = 'fake graph'\n data_dict = OrderedDict()\n data_dict[0] = 83\n data_dict[1] = 55\n\n png = plot_from_dict(title, data_dict)\n\n self.assertIsInstance(png, io.BytesIO)\n self.assertEqual(b'PNG', png.getvalue()[1:4])\n\n\nif __name__ == '__main__':\n unittest.main()\n", "sub_path": "tests/output_test.py", "file_name": "output_test.py", "file_ext": "py", "file_size_in_byte": 533, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "unittest.TestCase", "line_number": 10, "usage_type": "attribute"}, {"api_name": "collections.OrderedDict", "line_number": 13, "usage_type": "call"}, {"api_name": "transcriber.output.plot_from_dict", "line_number": 17, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 19, "usage_type": "attribute"}, {"api_name": "unittest.main", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "20472216", "text": "#\n# Copyright 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#\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, write to the Free Software\n# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA\n#\n# Refer to the README and COPYING files for full details of the license\n#\nimport nose.tools as nt\nfrom ovirtsdk.xml import params\n\nfrom ovirtlago import testlib\n\nimport test_utils\n\n\n# DC/Cluster\nDC_NAME = 'test-dc'\nCLUSTER_NAME = 'test-cluster'\n\n# Network\nMANAGEMENT_NET = 'ovirtmgmt'\n\nVLAN100_NET = 'VLAN100_Network'\nVLAN100_NET_IPv4_ADDR = '192.0.2.1'\nVLAN100_NET_IPv4_MASK = '255.255.255.0'\nVLAN100_NET_IPv6_ADDR = '2001:0db8:85a3:0000:0000:8a2e:0370:7331'\nVLAN100_NET_IPv6_MASK = '64'\n\n\ndef _get_networkattachment_by_network_id(host, network_id):\n # ovirtsdk requires '.' as a separator in multi-level filtering, we cannot\n # use kwargs directly\n # caveat: filtering by network.name is not supported by design (RH 1382341)\n # as only 'id' and 'href' properties are resolved for nested objects\n filter_args = {'network.id': network_id}\n attachment = host.networkattachments.list(**filter_args)[0]\n return attachment\n\n\ndef _set_network_required_in_cluster(api, network_name, cluster_name,\n required):\n network = api.clusters.get(cluster_name).networks.get(name=network_name)\n network.set_required(required)\n network.update()\n\n\ndef _get_mgmt_attachment(api, host):\n dc = api.datacenters.get(name=DC_NAME)\n mgmt_network_id = dc.networks.get(name=MANAGEMENT_NET).id\n mgmt_attachment = _get_networkattachment_by_network_id(\n host, mgmt_network_id)\n return mgmt_attachment\n\n\ndef _create_static_ip_configuration():\n ip_configuration = params.IpAddressAssignments(ip_address_assignment=[\n params.IpAddressAssignment(\n assignment_method='static',\n ip=params.IP(\n address=VLAN100_NET_IPv4_ADDR,\n netmask=VLAN100_NET_IPv4_MASK)),\n params.IpAddressAssignment(\n assignment_method='static',\n ip=params.IP(\n address=VLAN100_NET_IPv6_ADDR,\n netmask=VLAN100_NET_IPv6_MASK,\n version='v6'))\n ])\n\n return ip_configuration\n\n\ndef _create_dhcp_ip_configuration():\n ip_configuration = params.IpAddressAssignments(ip_address_assignment=[\n params.IpAddressAssignment(\n assignment_method='dhcp'),\n params.IpAddressAssignment(\n assignment_method='dhcp',\n ip=params.IP(version='v6'))\n ])\n\n return ip_configuration\n\n\ndef _attach_vlan_to_host(api, host, ip_configuration):\n mgmt_attachment = _get_mgmt_attachment(api, host)\n mgmt_nic_id = mgmt_attachment.get_host_nic().id\n mgmt_nic_name = host.nics.get(id=mgmt_nic_id).name\n\n vlan_network_attachment = params.NetworkAttachment(\n network=params.Network(name=VLAN100_NET),\n host_nic=params.HostNIC(name=mgmt_nic_name),\n ip_address_assignments=ip_configuration)\n\n attachment_action = params.Action(\n modified_network_attachments=params.NetworkAttachments(\n network_attachment=[vlan_network_attachment]),\n check_connectivity=True)\n\n host.setupnetworks(attachment_action)\n\n\ndef _modify_ip_config(api, host, ip_configuration):\n network_id = api.networks.get(name=VLAN100_NET).id\n attachment = _get_networkattachment_by_network_id(host, network_id)\n attachment.set_ip_address_assignments(ip_configuration)\n\n attachment_action = params.Action(\n modified_network_attachments=params.NetworkAttachments(\n network_attachment=[attachment]),\n check_connectivity=True)\n\n nt.assert_true(host.setupnetworks(attachment_action))\n\n\n#\n\n\n@testlib.with_ovirt_api\ndef attach_vlan_to_host_static_config(api):\n host = test_utils.hosts_in_cluster_v3(api, CLUSTER_NAME)[0]\n ip_configuration = _create_static_ip_configuration()\n _attach_vlan_to_host(api, host, ip_configuration)\n\n # TODO: currently ost uses v3 SDK that doesn't report ipv6. once available,\n # verify ipv6 as well.\n nt.assert_equals(\n host.nics.list(name='eth0.100')[0].ip.address,\n VLAN100_NET_IPv4_ADDR)\n\n\n@testlib.with_ovirt_api\ndef modify_host_ip_to_dhcp(api):\n host = test_utils.hosts_in_cluster_v3(api, CLUSTER_NAME)[0]\n ip_configuration = _create_dhcp_ip_configuration()\n _modify_ip_config(api, host, ip_configuration)\n\n # TODO: once the VLANs/dnsmasq issue is resolved,\n # (https://github.com/lago-project/lago/issues/375)\n # verify ip configuration.\n\n\n@testlib.with_ovirt_api\ndef detach_vlan_from_host(api):\n network_id = api.networks.get(name=VLAN100_NET).id\n host = test_utils.hosts_in_cluster_v3(api, CLUSTER_NAME)[0]\n\n def _detach_vlan_from_host():\n attachment = _get_networkattachment_by_network_id(host, network_id)\n\n removal_action = params.Action(\n removed_network_attachments=params.NetworkAttachments(\n network_attachment=[params.NetworkAttachment(\n id=attachment.id)]))\n\n host.setupnetworks(removal_action)\n\n def _host_is_detached_from_vlan_network():\n with nt.assert_raises(IndexError):\n _get_networkattachment_by_network_id(host, network_id)\n return True\n\n _set_network_required_in_cluster(api, VLAN100_NET, CLUSTER_NAME, False)\n _detach_vlan_from_host()\n\n nt.assert_true(_host_is_detached_from_vlan_network())\n\n\n_TEST_LIST = [\n attach_vlan_to_host_static_config,\n modify_host_ip_to_dhcp,\n detach_vlan_from_host\n]\n\n\ndef test_gen():\n for t in testlib.test_sequence_gen(_TEST_LIST):\n test_gen.__name__ = t.description\n yield t\n", "sub_path": "basic-suite-4.1/test-scenarios/003_basic_networking.py", "file_name": "003_basic_networking.py", "file_ext": "py", "file_size_in_byte": 6223, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "ovirtsdk.xml.params.IpAddressAssignments", "line_number": 68, "usage_type": "call"}, {"api_name": "ovirtsdk.xml.params", "line_number": 68, "usage_type": "name"}, {"api_name": "ovirtsdk.xml.params.IpAddressAssignment", "line_number": 69, "usage_type": "call"}, {"api_name": "ovirtsdk.xml.params", "line_number": 69, "usage_type": "name"}, {"api_name": "ovirtsdk.xml.params.IP", "line_number": 71, "usage_type": "call"}, {"api_name": "ovirtsdk.xml.params", "line_number": 71, "usage_type": "name"}, {"api_name": "ovirtsdk.xml.params.IpAddressAssignment", "line_number": 74, "usage_type": "call"}, {"api_name": "ovirtsdk.xml.params", "line_number": 74, "usage_type": "name"}, {"api_name": "ovirtsdk.xml.params.IP", "line_number": 76, "usage_type": "call"}, {"api_name": "ovirtsdk.xml.params", "line_number": 76, "usage_type": "name"}, {"api_name": "ovirtsdk.xml.params.IpAddressAssignments", "line_number": 86, "usage_type": "call"}, {"api_name": "ovirtsdk.xml.params", "line_number": 86, "usage_type": "name"}, {"api_name": "ovirtsdk.xml.params.IpAddressAssignment", "line_number": 87, "usage_type": "call"}, {"api_name": "ovirtsdk.xml.params", "line_number": 87, "usage_type": "name"}, {"api_name": "ovirtsdk.xml.params.IpAddressAssignment", "line_number": 89, "usage_type": "call"}, {"api_name": "ovirtsdk.xml.params", "line_number": 89, "usage_type": "name"}, {"api_name": "ovirtsdk.xml.params.IP", "line_number": 91, "usage_type": "call"}, {"api_name": "ovirtsdk.xml.params", "line_number": 91, "usage_type": "name"}, {"api_name": "ovirtsdk.xml.params.NetworkAttachment", "line_number": 102, "usage_type": "call"}, {"api_name": "ovirtsdk.xml.params", "line_number": 102, "usage_type": "name"}, {"api_name": "ovirtsdk.xml.params.Network", "line_number": 103, "usage_type": "call"}, {"api_name": "ovirtsdk.xml.params", "line_number": 103, "usage_type": "name"}, {"api_name": "ovirtsdk.xml.params.HostNIC", "line_number": 104, "usage_type": "call"}, {"api_name": "ovirtsdk.xml.params", "line_number": 104, "usage_type": "name"}, {"api_name": "ovirtsdk.xml.params.Action", "line_number": 107, "usage_type": "call"}, {"api_name": "ovirtsdk.xml.params", "line_number": 107, "usage_type": "name"}, {"api_name": "ovirtsdk.xml.params.NetworkAttachments", "line_number": 108, "usage_type": "call"}, {"api_name": "ovirtsdk.xml.params", "line_number": 108, "usage_type": "name"}, {"api_name": "ovirtsdk.xml.params.Action", "line_number": 120, "usage_type": "call"}, {"api_name": "ovirtsdk.xml.params", "line_number": 120, "usage_type": "name"}, {"api_name": "ovirtsdk.xml.params.NetworkAttachments", "line_number": 121, "usage_type": "call"}, {"api_name": "ovirtsdk.xml.params", "line_number": 121, "usage_type": "name"}, {"api_name": "nose.tools.assert_true", "line_number": 125, "usage_type": "call"}, {"api_name": "nose.tools", "line_number": 125, "usage_type": "name"}, {"api_name": "test_utils.hosts_in_cluster_v3", "line_number": 133, "usage_type": "call"}, {"api_name": "nose.tools.assert_equals", "line_number": 139, "usage_type": "call"}, {"api_name": "nose.tools", "line_number": 139, "usage_type": "name"}, {"api_name": "ovirtlago.testlib.with_ovirt_api", "line_number": 131, "usage_type": "attribute"}, {"api_name": "ovirtlago.testlib", "line_number": 131, "usage_type": "name"}, {"api_name": "test_utils.hosts_in_cluster_v3", "line_number": 146, "usage_type": "call"}, {"api_name": "ovirtlago.testlib.with_ovirt_api", "line_number": 144, "usage_type": "attribute"}, {"api_name": "ovirtlago.testlib", "line_number": 144, "usage_type": "name"}, {"api_name": "test_utils.hosts_in_cluster_v3", "line_number": 158, "usage_type": "call"}, {"api_name": "ovirtsdk.xml.params.Action", "line_number": 163, "usage_type": "call"}, {"api_name": "ovirtsdk.xml.params", "line_number": 163, "usage_type": "name"}, {"api_name": "ovirtsdk.xml.params.NetworkAttachments", "line_number": 164, "usage_type": "call"}, {"api_name": "ovirtsdk.xml.params", "line_number": 164, "usage_type": "name"}, {"api_name": "ovirtsdk.xml.params.NetworkAttachment", "line_number": 165, "usage_type": "call"}, {"api_name": "ovirtsdk.xml.params", "line_number": 165, "usage_type": "name"}, {"api_name": "nose.tools.assert_raises", "line_number": 171, "usage_type": "call"}, {"api_name": "nose.tools", "line_number": 171, "usage_type": "name"}, {"api_name": "nose.tools.assert_true", "line_number": 178, "usage_type": "call"}, {"api_name": "nose.tools", "line_number": 178, "usage_type": "name"}, {"api_name": "ovirtlago.testlib.with_ovirt_api", "line_number": 155, "usage_type": "attribute"}, {"api_name": "ovirtlago.testlib", "line_number": 155, "usage_type": "name"}, {"api_name": "ovirtlago.testlib.test_sequence_gen", "line_number": 189, "usage_type": "call"}, {"api_name": "ovirtlago.testlib", "line_number": 189, "usage_type": "name"}]} +{"seq_id": "630383506", "text": "\n#@tilte:操作销售出库\nimport time\nfrom PIL import ImageGrab\nfrom BDD.元素层.sellpages import SellPages\n\nclass DoSell:\n def __init__(self,dr):\n self.sp = SellPages(dr)\n\n def do_sell(self,barcode):\n time.sleep(2)\n self.sp.get_barcode().send_keys(barcode)\n self.sp.get_queryByBarCode().click()\n self.sp.get_submit().click()\n time.sleep(2)\n file = time.strftime(\"%Y%m%d_%H%M%S.png\")\n # 不能使用dr.get_screenshot_as_file截图，因为此时有弹框，焦点已经被切换\n # self.dr.get_screenshot_as_file(rf\"..\\公共层\\screenshot\\do_sell_{file}\")\n ImageGrab.grab().save(rf\"..\\公共层\\screenshot\\do_sell_{file}\")\n self.sp.get_alt_customer().accept()\n file = time.strftime(\"%Y%m%d_%H%M%S.png\")\n ImageGrab.grab().save(rf\"..\\公共层\\screenshot\\do_sell_{file}\")\n time.sleep(2)\n self.sp.get_alt_payment().accept()\n\n", "sub_path": "业务层/dosell.py", "file_name": "dosell.py", "file_ext": "py", "file_size_in_byte": 938, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "BDD.元素层.sellpages.SellPages", "line_number": 9, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 12, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 16, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 17, "usage_type": "call"}, {"api_name": "PIL.ImageGrab.grab", "line_number": 20, "usage_type": "call"}, {"api_name": "PIL.ImageGrab", "line_number": 20, "usage_type": "name"}, {"api_name": "time.strftime", "line_number": 22, "usage_type": "call"}, {"api_name": "PIL.ImageGrab.grab", "line_number": 23, "usage_type": "call"}, {"api_name": "PIL.ImageGrab", "line_number": 23, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "182523199", "text": "from django.db import models\nfrom django.template.defaultfilters import slugify\nfrom django.utils.translation import ugettext as _\nfrom django.contrib.auth.models import User\nfrom django.core.urlresolvers import reverse\nfrom django.conf import settings\n\nclass DatedModel(models.Model):\n created_time = models.DateTimeField(_(\"Created time\"), auto_now_add=True)\n modified_time = models.DateTimeField(_(\"Created time\"), auto_now=True)\n\n class Meta:\n abstract=True\n\n# Create your models here.\nclass Comic(DatedModel):\n \"\"\"\n Overall comic series. eg. Spider-Man, Batman\n \"\"\"\n id = models.AutoField(primary_key=True)\n slug = models.SlugField(editable=False)\n title = models.CharField(_(\"Comic Name\"), max_length=100,\n help_text=_(\"What's the name of your comic?\"))\n header = models.ImageField(_(\"Header Image\"),\n upload_to=\"comics/assets/headers\",\n help_text=_(\"This is the header image that will\"\n \" display behind the Comic title.\"),\n default=\"comic/assets/placeholder.png\")\n cover_image = models.ImageField(_(\"Cover Image\"),\n upload_to=\"comics/assets/covers\",\n help_text=_(\"This image is the front cover of \"\n \" the comic.\"), \n blank=True)\n\n thumbnail = models.ImageField(_(\"Thumbnail\"), blank=True, null=True,\n upload_to=\"comics/assets/thumbnails/\",\n default=\"comic/assets/placeholder.png\")\n\n author = models.ForeignKey(User, related_name=\"comics\",\n verbose_name=_(\"Who created this comic?\"))\n description = models.TextField(_(\"What's this comic about?\"),\n blank=True, default=None, null=True)\n chapter_count = models.PositiveIntegerField(default=0, editable=False)\n\n def __unicode__(self):\n return self.title\n\n def latest_chapter(self):\n \"\"\"\n Return the latest chapter of the comic.\n \"\"\"\n return self.chapters.latest()\n\n def save(self, *args, **kwargs):\n self.slug = slugify(self.title)\n super(Comic, self).save(*args, **kwargs)\n\n class Meta:\n ordering = ['slug']\n\n\nclass Chapter(DatedModel):\n \"\"\"\n A short story within a web-comic.\n \"\"\"\n id = models.AutoField(primary_key=True)\n slug = models.SlugField(editable=False)\n title = models.CharField(_(\"Chapter Name\"), max_length=100,\n help_text=_(\"What's the chapter name?\"))\n cover_image = models.ImageField(_(\"Cover Image\"),\n upload_to=\"comics/assets/covers\",\n help_text=_(\"This image is the front cover of \"\n \"the chapter.\"),\n blank=True)\n description = models.TextField(_(\"What's this chapter about?\"),\n blank=True, default=None, null=True)\n comic = models.ForeignKey('Comic', related_name=\"chapters\",\n verbose_name=_(\"Comic\"))\n pages_count = models.PositiveIntegerField(default=0, editable=False)\n ch_num = models.PositiveIntegerField(default=0, unique=True, \n help_text=_(\"The order of appearance \"\n \"that the chapter will show up in\"\n \" the comic. 1 is the first \"\n \"chapter.\"))\n\n def __unicode__(self):\n return self.title\n\n def latest_page(self):\n \"\"\"\n Get the latest page in the comic chapter.\n \"\"\"\n return self.pages.latest() or None\n\n def first_page(self):\n \"\"\"\n Get the first page in the comic.\n \"\"\"\n return self.pages.order_by('number')[0] # returns first page\n\n def save(self, *args, **kwargs):\n self.slug = slugify(self.title)\n super(Chapter, self).save(*args, **kwargs)\n\n class Meta:\n ordering = [\"ch_num\"]\n get_latest_by = \"ch_num\"\n\nclass Page(DatedModel):\n \"\"\"\n Individual comic book pages.\n \"\"\"\n id = models.AutoField(primary_key=True)\n next_page = models.ForeignKey('Page', null=True, blank=True, \n editable=False, related_name=\"next\", \n verbose_name=_(\"Next Page\"))\n prev_page = models.ForeignKey('Page', null=True, blank=True, \n editable=False, related_name=\"prev\", \n verbose_name=_(\"Previous Page\"))\n # order the page appears in in the chapter\n number = models.PositiveIntegerField(default=1, unique=True,\n help_text=_(\"This is the ordering to \"\n \"determine what order \"\n \"the page appears in the \"\n \"chapter. 1 is the first \"\n \"page.\"))\n chapter = models.ForeignKey('Chapter', related_name=\"pages\",\n verbose_name=_(\"Chapter\"))\n title = models.CharField(_(\"Page Title\"), max_length=50, \n blank=True, null=True,\n help_text=_(\"What's the title of this page?\") )\n image = models.ImageField(_(\"Page Image\"),\n upload_to=\"comics/assets/pages\",\n help_text=_(\"Page image shown to viewer\"),\n blank=True)\n author_notes = models.TextField(_(\"Author Notes\"), help_text=_(\"What did \"\n \"your viewers want to know about this \"\n \"page?\"), default='')\n \n class Meta:\n unique_together = ((\"chapter\", \"number\"),)\n ordering = [\"number\"]\n get_latest_by = \"number\"\n\n def __unicode__(self):\n return self.chapter.slug + \"-\" + str(self.number)\n\n def save(self, *args, **kwargs):\n self._update_links()\n super(Page, self).save(*args, **kwargs)\n\n def _update_links(self):\n \"\"\"\n Update the next/prev links to the next page.\n \"\"\"\n pages = self.chapter.pages.order_by('number')\n for i in range(1, len(pages)):\n pages[i-1].next_page = pages[i]\n pages[i].prev_page = pages[i-1]\n", "sub_path": "models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 6649, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "django.db.models.Model", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 9, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 9, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 9, "usage_type": "call"}, {"api_name": "django.db.models.DateTimeField", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models.AutoField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.SlugField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 22, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models.ImageField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 24, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.models.ImageField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 29, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.models.ImageField", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 35, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.models.ForeignKey", "line_number": 39, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 39, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 39, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 40, "usage_type": "call"}, {"api_name": "django.db.models.TextField", "line_number": 41, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 41, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 41, "usage_type": "call"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 43, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 43, "usage_type": "name"}, {"api_name": "django.template.defaultfilters.slugify", "line_number": 55, "usage_type": "call"}, {"api_name": "django.db.models.AutoField", "line_number": 66, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 66, "usage_type": "name"}, {"api_name": "django.db.models.SlugField", "line_number": 67, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 67, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 68, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 68, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 68, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 69, "usage_type": "call"}, {"api_name": "django.db.models.ImageField", "line_number": 70, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 70, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 70, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 72, "usage_type": "call"}, {"api_name": "django.db.models.TextField", "line_number": 75, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 75, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 75, "usage_type": "call"}, {"api_name": "django.db.models.ForeignKey", "line_number": 77, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 77, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 78, "usage_type": "call"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 79, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 79, "usage_type": "name"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 80, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 80, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 81, "usage_type": "call"}, {"api_name": "django.template.defaultfilters.slugify", "line_number": 102, "usage_type": "call"}, {"api_name": "django.db.models.AutoField", "line_number": 113, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 113, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 114, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 114, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 116, "usage_type": "call"}, {"api_name": "django.db.models.ForeignKey", "line_number": 117, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 117, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 119, "usage_type": "call"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 121, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 121, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 122, "usage_type": "call"}, {"api_name": "django.db.models.ForeignKey", "line_number": 127, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 127, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 128, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 129, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 129, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 129, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 131, "usage_type": "call"}, {"api_name": "django.db.models.ImageField", "line_number": 132, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 132, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 132, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 134, "usage_type": "call"}, {"api_name": "django.db.models.TextField", "line_number": 136, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 136, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 136, "usage_type": "call"}]} +{"seq_id": "230369071", "text": "import json\nimport urllib\nimport urllib2\n\n# should be made a class and class variables\n# oh well\n\nbase_api_url = 'http://access.alchemyapi.com/calls/'\napi_key_file = 'alchemyapikey.dat'\n\ndef loadApiKey(fname=api_key_file):\n f = open(fname, 'r')\n base_params[\"apikey\"] = f.read()\n f.close()\n\n\nbase_params = {\n \"apikey\": None,\n \"outputMode\": \"json\",\n \"linkedData\": 0\n}\n\n\n\ndef MakeApiCall(typ, function, payload, params=base_params):\n if params[\"apikey\"] == None:\n raise ValueError('apikey must be set / loaded before calling the api')\n url = base_api_url + typ + '/' + function + '?' + typ + \"=\" + urllib.quote_plus(payload)\n for k in params.keys():\n url = url + \"&\" + k + \"=\" + urllib.quote_plus(params[k])\n try:\n socket = urllib2.urlopen(url)\n raw_json = socket.read()\n socket.close()\n except (urllib2.HTTPError):\n raw_json = '{\"status\": \"No connection\"}'\n return json.loads(raw_json)\n\n\ndef URLGetRankedNamedEntities(url):\n return MakeApiCall(\"url\", \"URLGetRankedNamedEntities\", url)\n\ndef TextGetRankedNamedEntities(txt):\n return MakeApiCall(\"text\", \"TextGetRankedNamedEntities\", txt)\n", "sub_path": "Utils/AlchemyAPI.py", "file_name": "AlchemyAPI.py", "file_ext": "py", "file_size_in_byte": 1117, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "urllib.quote_plus", "line_number": 28, "usage_type": "call"}, {"api_name": "urllib.quote_plus", "line_number": 30, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 32, "usage_type": "call"}, {"api_name": "urllib2.HTTPError", "line_number": 35, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "271171384", "text": "import torch\r\nimport torch.nn as nn\r\nfrom torch.autograd import Variable\r\n\r\n\r\nclass PartUpdateEmbedding(nn.Module):\r\n\r\n # update_index come from num_special, \r\n # emb_update come from emb_special. \r\n # emb_fixed come from emb_word. emb_fixed will be not changed.\r\n def __init__(self, update_index, emb_update, emb_fixed):\r\n super(PartUpdateEmbedding, self).__init__()\r\n self.update_index = update_index\r\n self.emb_update = emb_update\r\n self.emb_fixed = emb_fixed\r\n self.should_update = True\r\n self.embedding_dim = emb_update.embedding_dim\r\n\r\n\r\n # It will update the emb_fixed if should_update is True\r\n def set_update(self, should_update):\r\n self.should_update = should_update\r\n\r\n\r\n\r\n def forward(self, inp):\r\n assert(inp.dim() == 2)\r\n # inp.clamp make the value of inp between 0 and 4 since self.update_index - 1 = 4\r\n # make sure the data into emb_upadate(emb for specials) is special index. \r\n r_update = self.emb_update(inp.clamp(0, self.update_index - 1))\r\n r_fixed = self.emb_fixed(inp)\r\n \r\n # compared to lt(self.update_index). if the element of inp < self.update_index, it will be true.\r\n # you will get the same shape of matrix as inp with boolean value. And then become 0 or 1.\r\n # unsqueeze(2) will add a one dimension in third index. So the index become: [*][*][0]\r\n # The shape of r_update is (time_step/sentence_length)*batch*opt.word_vec_size\r\n # The shape of self.update_index).float().unsqueeze(2) is (time_step/sentence_length)*batch*1, \r\n # and then expand to (time_step/sentence_length)*batch*opt.word_vec_size.\r\n # After expanded, data[*][*][i] equal to data[*][*][j] (0<=i,j<=opt.word_vec_size) and data = inp.data.lt(self.update_index).float().unsqueeze(2).expand_as(r_update)\r\n mask = Variable(inp.data.lt(self.update_index).float().unsqueeze(\r\n 2).expand_as(r_update), requires_grad=False)\r\n\r\n # mask only contain the value of 0 or 1.\r\n # After multipy mask, the (result = r_update + r_fixed) means the special token only come from emb_update. \r\n # And other words only come from emb_fixed\r\n r_update = r_update.mul(mask)\r\n r_fixed = r_fixed.mul(1 - mask)\r\n\r\n if self.should_update:\r\n return r_update + r_fixed\r\n else:\r\n return r_update + Variable(r_fixed.data, requires_grad=False)\r\n", "sub_path": "wikisql/table/modules/Embeddings.py", "file_name": "Embeddings.py", "file_ext": "py", "file_size_in_byte": 2460, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "torch.nn.Module", "line_number": 6, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 6, "usage_type": "name"}, {"api_name": "torch.autograd.Variable", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "377149207", "text": "\nimport synapse.lib.coro as s_coro\nimport synapse.tests.utils as s_t_utils\n\nclass CoroTest(s_t_utils.SynTest):\n\n async def test_coro_iscoro(self):\n\n async def agen():\n yield 42\n\n def genr():\n yield 'woot'\n\n async def woot():\n return 10\n\n item = woot()\n self.true(s_coro.iscoro(item))\n\n await item\n\n self.false(s_coro.iscoro(genr()))\n self.false(s_coro.iscoro(agen()))\n\n async def test_coro_genrhelp(self):\n\n @s_coro.genrhelp\n async def woot():\n yield 1\n yield 2\n yield 3\n\n self.none(await woot().spin())\n self.eq([1, 2, 3], await woot().list())\n", "sub_path": "synapse/tests/test_lib_coro.py", "file_name": "test_lib_coro.py", "file_ext": "py", "file_size_in_byte": 701, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "synapse.tests.utils.SynTest", "line_number": 5, "usage_type": "attribute"}, {"api_name": "synapse.tests.utils", "line_number": 5, "usage_type": "name"}, {"api_name": "synapse.lib.coro.iscoro", "line_number": 19, "usage_type": "call"}, {"api_name": "synapse.lib.coro", "line_number": 19, "usage_type": "name"}, {"api_name": "synapse.lib.coro.iscoro", "line_number": 23, "usage_type": "call"}, {"api_name": "synapse.lib.coro", "line_number": 23, "usage_type": "name"}, {"api_name": "synapse.lib.coro.iscoro", "line_number": 24, "usage_type": "call"}, {"api_name": "synapse.lib.coro", "line_number": 24, "usage_type": "name"}, {"api_name": "synapse.lib.coro.genrhelp", "line_number": 28, "usage_type": "attribute"}, {"api_name": "synapse.lib.coro", "line_number": 28, "usage_type": "name"}]} +{"seq_id": "421746739", "text": "# -*-coding: utf-8 -*-\nimport logging\nfrom PyQt5.QtWebEngineWidgets import QWebEngineView, QWebEnginePage\nfrom PyQt5.QtWidgets import QShortcut, QDialog, QGridLayout\nfrom PyQt5.QtCore import Qt, QUrl\nfrom optparse import OptionParser\n\n\nclass WebViewPlus(QWebEngineView):\n \"\"\"\n\tWebView 커스터마이징\n\t - inspector 추가\n\t - jsconsole 로그 추가\n\t - webview에서 document로 이벤트를 발생함.\n\t\"\"\"\n\n def __init__(self):\n super().__init__()\n self.setPage(WebPagePlus())\n\n\n #Keyboard shortcuts\n self.shortcut = {}\n\n #F5 - Page reloading\n self.shortcut['F5'] = QShortcut(self)\n self.shortcut['F5'].setKey(Qt.Key_F5)\n self.shortcut['F5'].activated.connect(self.reload)\n\n #Devtool setup\n def debuggingMode(self, port):\n #F12 - Development tool\n self.shortcut['F12'] = QShortcut(self)\n self.shortcut['F12'].setContext(Qt.ApplicationShortcut)\n self.shortcut['F12'].setKey(Qt.Key_F12)\n self.shortcut['F12'].activated.connect(self._toggleDevTool)\n\n self.devTool = QDialog(self)\n self.devTool.setWindowTitle(\"Development Tool\")\n self.devTool.resize(950, 400)\n\n self.devView = QWebEngineView()\n self.devView.setPage(QWebEnginePage(self.devView))\n \n self.devView.load(QUrl(\"http://localhost:\"+port))\n layout = QGridLayout()\n layout.setContentsMargins(0, 0, 0, 0)\n layout.addWidget(self.devView)\n self.devTool.setLayout(layout)\n\n def _toggleDevTool(self):\n \"\"\"\n F12키를 다시 누르면 \"개발자 도구\"가 사라짐\n \"\"\"\n self.devTool.setVisible(not self.devTool.isVisible())\n\nclass WebPagePlus(QWebEnginePage):\n \"\"\"\n\tjavascript 콘솔 메시지를 python logger에 출력\n\thttp://pyqt.sourceforge.net/Docs/PyQt4/qwebpage.html\n\t\"\"\"\n\n def __init__(self, logger=None):\n super().__init__()\n if not logger:\n logger = logging\n self.logger = logger\n\n def javaScriptConsoleMessage(self, level, msg, lineNumber, sourceID):\n self.logger.warning(\"console(%s:%d): %s\" % (sourceID, lineNumber, msg))\n", "sub_path": "plus/web.py", "file_name": "web.py", "file_ext": "py", "file_size_in_byte": 2153, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "PyQt5.QtWebEngineWidgets.QWebEngineView", "line_number": 9, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QShortcut", "line_number": 26, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.Key_F5", "line_number": 27, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 27, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QShortcut", "line_number": 33, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.ApplicationShortcut", "line_number": 34, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 34, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.Key_F12", "line_number": 35, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 35, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QDialog", "line_number": 38, "usage_type": "call"}, {"api_name": "PyQt5.QtWebEngineWidgets.QWebEngineView", "line_number": 42, "usage_type": "call"}, {"api_name": "PyQt5.QtWebEngineWidgets.QWebEnginePage", "line_number": 43, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QUrl", "line_number": 45, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QGridLayout", "line_number": 46, "usage_type": "call"}, {"api_name": "PyQt5.QtWebEngineWidgets.QWebEnginePage", "line_number": 57, "usage_type": "name"}]} +{"seq_id": "609926197", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\nresultsLocation = \"output/results.out\"\n\nf = open(resultsLocation, \"r\")\nlines = f.readlines()\n\n######################\n# A modifier\n#######\nsize = 100\nZ = np.zeros((size, size))\nnvals = np.zeros((size, size))\nxparam = 0\nyparam = 1\nzresult = 0\n\n\nx, y = 0, 0\nxprev, yprev = 0, 0\ni, j = -1, -1\nmaxval = 0\nlines.pop(0)\nfor line in lines:\n elmts = line.split(\" \")\n params = elmts[0].split(\"_\")\n xprev = x\n x = float(params[xparam])\n yprev = y\n y = float(params[yparam])\n if (xprev < x):\n i += 1\n elif (xprev > x):\n i = 0\n if (yprev < y):\n j += 1\n elif (yprev > y):\n j = 0\n Z[i, j] += float(elmts[1 + zresult])\n nvals[i, j] += 1\n if (maxval < Z[i, j]):\n maxval = Z[i, j]\nZ /= maxval\nnvals += 0.01\nZ = Z / nvals\n\n\nZ = Z / (Z.max(axis=0).max(axis=0) + np.spacing(0))\nplt.imshow(Z)\n\nplt.savefig(\"output/print.png\")\nplt.close()\n", "sub_path": "src/pythonScripts/PrintResults.py", "file_name": "PrintResults.py", "file_ext": "py", "file_size_in_byte": 942, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "numpy.zeros", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.spacing", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}]} +{"seq_id": "374652144", "text": "from pathlib import *\nimport sys\nimport requests\n\nurl = \"https://movie.douban.com/top250\"\nuser_agent = \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36 Edg/87.0.664.66\"\nheader = {\"user-agent\": user_agent}\n\ntry: \n response = requests.get(url, headers=header)\nexcept requests.exceptions.ConnectTimeout as e:\n print(f\"请求超时{e}\")\n sys.exit(1)\n\n# print(response.text)\n\npath = Path(__file__)\n\npath_parent = path.resolve().parent\n# 建立新的目录\nhtml_path = path_parent.joinpath('html')\n\nif not html_path.is_dir():\n Path.mkdir(html_path)\n # Path(html_path).mkdir(mode=0o777)\n\npage = html_path.joinpath('double.html')\n\n# 上下文管理\ntry:\n with open(page, 'w', encoding='utf-8') as f:\n f.write(response.text)\nexcept FileNotFoundError as e:\n print(f\"文件无法打开：{e}\")\nexcept IOError as e:\n print(f\"文件读写错误： {e}\")\nexcept Exception as e:\n print(e)", "sub_path": "week02/mod2_requests_v2.py", "file_name": "mod2_requests_v2.py", "file_ext": "py", "file_size_in_byte": 974, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "requests.get", "line_number": 10, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "36488467", "text": "import sys\nsys.path.append(\"../..\")\nimport scimpute\nimport Orange.data\nfrom Orange.data import Table\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.colors as colors\n\n\n\n# Izracunaj vse potrebno z uporabo modula\ndata_org = scimpute.generate()\ndat, mas, zero = scimpute.zero_inflate(data_org)\nsc = scimpute.ScImpute(dat)\nres = sc.median()\ncor, data = sc.compare(data_org, mas)\nprint(cor)\n# Plotaj vse potrebno\n# Primerjava bioloskih podatkov z imputiranimi vrednostmi\nfig, (ax0, ax1, ax2) = plt.subplots(3, 1)\nc = ax0.pcolormesh(data_org, norm=colors.LogNorm(vmin=np.amin(data_org)+1, vmax=np.amax(data_org)), cmap=plt.get_cmap(\"binary\"))\nfig.colorbar(c, ax=ax0)\nax0.set_title('Sintetični podatki')\n\nc = ax1.pcolormesh(res,norm=colors.LogNorm(vmin=np.amin(res)+1, vmax=np.amax(res)), cmap=plt.get_cmap(\"binary\"))\nax1.set_title('Imputirani sintetični podatki')\nfig.colorbar(c, ax=ax1)\n\nc = ax2.pcolormesh(data_org-res, norm=colors.LogNorm(vmin=np.amin(data_org)+1, vmax=np.amax(data_org)), cmap=plt.get_cmap(\"binary\"))\nax2.set_title('Razlika med sintetičnimi in imputiranimi podatki')\nfig.colorbar(c, ax=ax2)\n\nfig.tight_layout()\nfig.savefig('synthetic_and_imputed.png')\n\n# histogrami matrik\nfig, (ax0, ax1, ax2) = plt.subplots(3, 1)\n\nax0.hist(data_org.flatten(), bins = 20)\nax0.set_title('Histogram sintetičnih podatkov')\n\nax1.hist(res.flatten(), bins = 20)\nax1.set_title('Histogram imputiranih podatkov')\n\nax2.hist((data_org-res).flatten(), bins = 20)\nax2.set_title('Histogram razlike med sintetičnimi in imputiranimi podatki')\nax0.set_xlabel('Ekspresija genov')\nax1.set_xlabel('Ekspresija genov')\nax2.set_xlabel('Ekspresija genov')\nfig.tight_layout()\nfig.savefig('histogram_matrik.png')\n\n# Histogrami korelacij.\n\nx = data[0]\ny = data[1]\nz = data[2]\nv = data[3]\n\nfig, (axs1, axs2) = plt.subplots(2, 2)\n\naxs1[0].hist(data[0])\naxs1[0].set_title('Korelacija po profilih celic')\naxs1[1].hist(data[1])\naxs1[1].set_title('Korelacija po profilih genov')\naxs2[0].hist(data[2])\naxs2[0].set_title('Po maskiranih profilih celic')\naxs2[1].hist(data[3])\naxs2[1].set_title('Po maskiranih profilih genov')\nfig.tight_layout()\nfig.savefig('vrstice_stolpci.png')\n\n# np.savetxt(\"average.csv\", res, delimiter=\",\")\n\n# res = sc.average()\n# np.savetxt(\"average.csv\", res, delimiter=\",\")\n#\n# res = sc.median()\n# np.savetxt(\"median.csv\", res, delimiter=\",\")\n#\n# res = sc.WMean_chisquared()\n# np.savetxt(\"WMean_chisquared.csv\", res, delimiter=\",\")\n", "sub_path": "Examples/Synthetic_median0/run.py", "file_name": "run.py", "file_ext": "py", "file_size_in_byte": 2446, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "sys.path.append", "line_number": 2, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 2, "usage_type": "attribute"}, {"api_name": "scimpute.generate", "line_number": 13, "usage_type": "call"}, {"api_name": "scimpute.zero_inflate", "line_number": 14, "usage_type": "call"}, {"api_name": "scimpute.ScImpute", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.colors.LogNorm", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 22, "usage_type": "name"}, {"api_name": "numpy.amin", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.amax", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.get_cmap", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.colors.LogNorm", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 26, "usage_type": "name"}, {"api_name": "numpy.amin", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.amax", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.get_cmap", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.colors.LogNorm", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 30, "usage_type": "name"}, {"api_name": "numpy.amin", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.amax", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.get_cmap", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}]} +{"seq_id": "161158115", "text": "from flask import Flask\nfrom flask_mongoengine import MongoEngine\nfrom config import config\n\ndb = MongoEngine()\n\ndef create_app(config_name):\n app = Flask(__name__)\n\n app.config['MONGODB_DB'] = 'python'\n app.config['MONGODB_HOST'] = 'localhost'\n app.config['MONGODB_PORT'] = 27017\n app.config.from_object(config[config_name])\n config[config_name].init_app(app)\n db.init_app(app)\n\n\n from .main import main as main_blueprint\n app.register_blueprint(main_blueprint)\n return app", "sub_path": "app/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 504, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "flask_mongoengine.MongoEngine", "line_number": 5, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "config.config", "line_number": 13, "usage_type": "name"}, {"api_name": "config.config", "line_number": 14, "usage_type": "name"}, {"api_name": "main.main", "line_number": 19, "usage_type": "argument"}]} +{"seq_id": "287659435", "text": "import logging\nimport logging.handlers\nimport optparse\nimport os\nimport sys\nimport time\nimport re\nimport cPickle\nimport shutil\nfrom cookielib import logger\nfrom optparse import OptionParser, OptionGroup\n\nMODULE_PATH = os.path.dirname(__file__) or os.getcwd()\nTMSTAF_PID_FILE = os.path.join(MODULE_PATH, 'tmstaf.pid')\n\n\ndef addTmstafLibPath():\n sys.path.insert(0, os.path.dirname(MODULE_PATH))\n\n\ndef addStafLibPath():\n #Todo: we should not hardcode this path! 2009-10-14 camge\n if os.name == 'nt':\n sys.path.insert(0, r'C:\\staf\\bin')\n else:\n sys.path.insert(0, r'/usr/local/STAF/bin')\n\naddStafLibPath()\naddTmstafLibPath()\n\n\nimport tmstaf.processUtil\nimport secureCloud.agentBVT.testingClient\nimport secureCloud.agentBVT.util\nfrom tmstaf.testwareConfig import TestwareConfig\nfrom tmstaf.productSetting import ProductSetting\nfrom tmstaf.testRunner import BaseTestRunner\nfrom tmstaf.util import getException\nimport threading\nVERSION = 'v2.1.0'\n\nimport secureCloud.config.result_config\nimport secureCloud.scAgent.file\nlog_path = secureCloud.config.result_config.result_path\nchefLogger = secureCloud.config.result_config.chefLogger\nstafLogger = secureCloud.config.result_config.stafLogger\nerrorLogger = secureCloud.config.result_config.errorLogger\n\nclass TmstafMain:\n strVersion = VERSION\n strResumeDataFileName = 'resume.dat'\n strResumeDataFile = os.path.join(os.getcwd(), strResumeDataFileName)\n _dicLevel = {'critical': logging.CRITICAL,\n 'error': logging.ERROR,\n 'warning': logging.WARNING,\n 'info': logging.INFO,\n 'debug': logging.DEBUG}\n\n def __init__(self, intLogLevel=None):\n # set root logger's level to DEBUG\n self.resumed = False\n if intLogLevel:\n logging.getLogger().setLevel(intLogLevel)\n self.strCmdLine = ' '.join(sys.argv)\n \n\n\ndef main(options):\n retval = 0\n if options.keyword==False:\n errorLogger.error(\"[CHECKLOG] search keyword is required\")\n retval = 1\n return retval \n if options.file_path==False:\n errorLogger.error(\"[CHECKLOG] log file path is required\")\n retval = 1\n return retval\n if options.case_name==False:\n errorLogger.error(\"[CHECKLOG] test case name is required\")\n retval = 1\n return retval \n keyword=options.keyword\n agent_log_path = options.file_path \n case_name = options.case_name\n # Load global specific settings\n GLOBAL_SETTING = secureCloud.agentBVT.util.config(\"%s/product.ini\" % MODULE_PATH)\n sc_path = secureCloud.scAgent.Agent.get_sc_root()\n agent_vmGuid = secureCloud.agentBVT.testingClient.get_agent_vmGuid(sc_path)\n #keyword = r\"DEBUG [KMS - Status Update] Sending instance status to https://10.201.224.67/Agent/API.svc\"\n retval=secureCloud.agentBVT.testingClient.check_log(agent_log_path,unicode(keyword),5)\n if retval==0:\n stafLogger.critical('pass: %s' %options.case_name)\n else:\n stafLogger.critical('FAIL: %s' %options.case_name)\n stafLogger.debug('Can not find keyword in log file')\n secureCloud.agentBVT.testingClient.write_sctm_report(log_path,options.case_name,retval)\n return retval\n\n\ndef _handle_options(argv):\n usage_win = 'activate.py [action [parameters]]'\n usage_linux = 'python activate.py [action [parameters]]'\n if os.name == \"nt\":\n usage = usage_win\n else:\n usage = usage_linux\n\n parser = OptionParser(usage=usage)\n\n action_group = OptionGroup(parser, \"Actions\", \"\")\n action_group.add_option('-k', '--keyword', type='string', dest='keyword',\n default=False,metavar= 'keyword',\n help='keyword to find. ex: -k \"DEBUG scagent...\"')\n action_group.add_option('-f', '--file-path', type='string', dest='file_path',\n default=False, metavar= 'file_path',\n help='log file path. ex: -f \"/var/lib/secureCloud/agnet.log\"')\n action_group.add_option('-n', '--case-name', type='string', dest='case_name',\n default=False, metavar= 'file_path',\n help='test case name. ex: check_add_hosts_file')\n parser.add_option_group(action_group)\n\n (options, args) = parser.parse_args(argv)\n \n return options \n\n\n\nif __name__ == \"__main__\": \n __retval__ = 0\n stafLogger.critical('*** START CHECK LOG ***\\n') \n if __retval__ == 0:\n __options__ = _handle_options(sys.argv[1:])\n __retval__ = main(__options__)\n stafLogger.critical('*** EXIT WITH (%s) ***\\n' %str(__retval__))\n sys.exit(__retval__)", "sub_path": "lib/python/chef/check_log.py", "file_name": "check_log.py", "file_ext": "py", "file_size_in_byte": 4608, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.path.dirname", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sys.path.insert", "line_number": 18, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.name", "line_number": 23, "usage_type": "attribute"}, {"api_name": "sys.path.insert", "line_number": 24, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "sys.path.insert", "line_number": 26, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "secureCloud.agentBVT.testingClient.config", "line_number": 44, "usage_type": "attribute"}, {"api_name": "secureCloud.agentBVT.testingClient", "line_number": 44, "usage_type": "name"}, {"api_name": "secureCloud.agentBVT.testingClient.config", "line_number": 45, "usage_type": "attribute"}, {"api_name": "secureCloud.agentBVT.testingClient", "line_number": 45, "usage_type": "name"}, {"api_name": "secureCloud.agentBVT.testingClient.config", "line_number": 46, "usage_type": "attribute"}, {"api_name": "secureCloud.agentBVT.testingClient", "line_number": 46, "usage_type": "name"}, {"api_name": "secureCloud.agentBVT.testingClient.config", "line_number": 47, "usage_type": "attribute"}, {"api_name": "secureCloud.agentBVT.testingClient", "line_number": 47, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 52, "usage_type": "call"}, {"api_name": "logging.CRITICAL", "line_number": 53, "usage_type": "attribute"}, {"api_name": "logging.ERROR", "line_number": 54, "usage_type": "attribute"}, {"api_name": "logging.WARNING", "line_number": 55, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 56, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 57, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 63, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 64, "usage_type": "attribute"}, {"api_name": "secureCloud.agentBVT.testingClient.agentBVT.util.config", "line_number": 86, "usage_type": "call"}, {"api_name": "secureCloud.agentBVT.testingClient.agentBVT", "line_number": 86, "usage_type": "attribute"}, {"api_name": "secureCloud.agentBVT.testingClient", "line_number": 86, "usage_type": "name"}, {"api_name": "secureCloud.agentBVT.testingClient.scAgent.Agent.get_sc_root", "line_number": 87, "usage_type": "call"}, {"api_name": "secureCloud.agentBVT.testingClient.scAgent", "line_number": 87, "usage_type": "attribute"}, {"api_name": "secureCloud.agentBVT.testingClient", "line_number": 87, "usage_type": "name"}, {"api_name": "secureCloud.agentBVT.testingClient.agentBVT.testingClient.get_agent_vmGuid", "line_number": 88, "usage_type": "call"}, {"api_name": "secureCloud.agentBVT.testingClient.agentBVT", "line_number": 88, "usage_type": "attribute"}, {"api_name": "secureCloud.agentBVT.testingClient", "line_number": 88, "usage_type": "name"}, {"api_name": "secureCloud.agentBVT.testingClient.agentBVT.testingClient.check_log", "line_number": 90, "usage_type": "call"}, {"api_name": "secureCloud.agentBVT.testingClient.agentBVT", "line_number": 90, "usage_type": "attribute"}, {"api_name": "secureCloud.agentBVT.testingClient", "line_number": 90, "usage_type": "name"}, {"api_name": "secureCloud.agentBVT.testingClient.agentBVT.testingClient.write_sctm_report", "line_number": 96, "usage_type": "call"}, {"api_name": "secureCloud.agentBVT.testingClient.agentBVT", "line_number": 96, "usage_type": "attribute"}, {"api_name": "secureCloud.agentBVT.testingClient", "line_number": 96, "usage_type": "name"}, {"api_name": "os.name", "line_number": 103, "usage_type": "attribute"}, {"api_name": "optparse.OptionParser", "line_number": 108, "usage_type": "call"}, {"api_name": "optparse.OptionGroup", "line_number": 110, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 132, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 135, "usage_type": "call"}]} +{"seq_id": "470669405", "text": "# Tiedosto: ika.py\r\nfrom dateutil import relativedelta as datediff\r\nfrom datetime import date,datetime\r\nfrom debuggeri import debuggeri\r\nkysely = \"Anna syntymäaikasi muodossa 1.1.1970: \"\r\n\r\n@debuggeri\r\ndef ikalaskin(syntymaaika,toinen=\"\"):\r\n d1 = datetime.strptime(syntymaaika,'%d.%m.%Y').date()\r\n d2 = date.today()\r\n d = datediff.relativedelta(d2,d1)\r\n # tulos = \"{0.years} v {0.months} kk {0.days} pv\".format(d)\r\n tulos = f\"{d.years} v {d.months} kk {d.days} pv\"\r\n print(\"Ikä: \",tulos)\r\n return tulos\r\n\r\n@debuggeri\r\ndef ikakysely():\r\n valmis = False\r\n virhe = False\r\n ika = \"\"\r\n while not valmis:\r\n try:\r\n syntymaaika = input(kysely)\r\n datetime.strptime(syntymaaika,'%d.%m.%Y')\r\n ika = ikalaskin(syntymaaika)\r\n valmis = True\r\n except ValueError:\r\n print(\"Virhe\")\r\n virhe = True\r\n # finally:\r\n # if virhe:\r\n # print(f\"Kysely päättyi virheeseen.\")\r\n # else:\r\n # print(\"Kysely on valmis, ikä on \"+ika+\".\\n\")\r\n else:\r\n print(\"Kysely on valmis, ikä on \"+ika+\".\\n\")\r\n return ika\r\n\r\n# ikakysely()", "sub_path": "ika.py", "file_name": "ika.py", "file_ext": "py", "file_size_in_byte": 1186, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "datetime.datetime.strptime", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 9, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 10, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 10, "usage_type": "name"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 11, "usage_type": "call"}, {"api_name": "dateutil.relativedelta", "line_number": 11, "usage_type": "name"}, {"api_name": "debuggeri.debuggeri", "line_number": 7, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 25, "usage_type": "name"}, {"api_name": "debuggeri.debuggeri", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "446234963", "text": "'''\nCreated on Oct 9, 2014\n@author: Dibyendu\n\nLoads resources from databases which are used by the analyzers.\n'''\nimport sqlite3, os\n\ndef load_stop_words():\n '''Loads stop words from database and returns a list.'''\n dbase = sqlite3.connect(os.path.join(\"database\", 'sqlite.db'))\n cursor = dbase.cursor()\n cursor.execute('SELECT word FROM stop_words')\n word_list = cursor.fetchall()\n dbase.close()\n word_list = list(*zip(*word_list))\n return word_list", "sub_path": "analyzer/db_resources.py", "file_name": "db_resources.py", "file_ext": "py", "file_size_in_byte": 474, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "sqlite3.connect", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}]} +{"seq_id": "130896731", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Dec 11 15:54:45 2020\n\n@author: Administrator\n\"\"\"\n\nfrom pyecharts import options as opts\nfrom pyecharts.charts import Bar\nimport pandas as pd\n\ndata = pd.read_excel('info_movies.xls')\nscore = data['region'].value_counts().sort_index()\nx = score.index.tolist()\ny = score.tolist()\nbar = Bar()\nbar.add_xaxis(x)\nbar.add_yaxis('电影国家', y)\nbar.set_global_opts(\n title_opts={'text': \"电影国家分布\", 'subtext': \"来源于豆瓣电影top250排名\"},\n brush_opts=opts.BrushOpts(),\n toolbox_opts=opts.ToolboxOpts(),\n datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_=\"inside\")],\n tooltip_opts=opts.TooltipOpts(trigger=\"axis\", axis_pointer_type=\"cross\"),\n visualmap_opts=opts.VisualMapOpts(max_=120)\n)\nbar.render(\"templates/bar_region.html\")", "sub_path": "bar_region.py", "file_name": "bar_region.py", "file_ext": "py", "file_size_in_byte": 811, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "pandas.read_excel", "line_number": 12, "usage_type": "call"}, {"api_name": "pyecharts.charts.Bar", "line_number": 16, "usage_type": "call"}, {"api_name": "pyecharts.options.BrushOpts", "line_number": 21, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 21, "usage_type": "name"}, {"api_name": "pyecharts.options.ToolboxOpts", "line_number": 22, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 22, "usage_type": "name"}, {"api_name": "pyecharts.options.DataZoomOpts", "line_number": 23, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 23, "usage_type": "name"}, {"api_name": "pyecharts.options.TooltipOpts", "line_number": 24, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 24, "usage_type": "name"}, {"api_name": "pyecharts.options.VisualMapOpts", "line_number": 25, "usage_type": "call"}, {"api_name": "pyecharts.options", "line_number": 25, "usage_type": "name"}]} +{"seq_id": "462085283", "text": "from fabric import task\nimport sys\n\nimport config as cfg\n\n\n@task\ndef init(c):\n \"\"\"Initialize project.\"\"\"\n\n # create python virtual environment\n if not cfg.VENVDIR.exists():\n c.run(f'{sys.executable} -m venv {cfg.VENVDIR}', replace_env=False, pty=True)\n c.run(f'{cfg.PYTHON} -m pip install -U setuptools pip', replace_env=False, pty=True)\n\n # install project packages\n c.run(f'{cfg.PYTHON} -m pip install -r requirements.txt', replace_env=False, pty=True)\n\n\n@task\ndef run(c, path):\n \"\"\"Run python script\"\"\"\n c.run(f'{cfg.PYTHON} {path}', replace_env=False, pty=True)\n\n\n@task\ndef cloudsdk(c, cmdline):\n \"\"\"Dockerized Google CloudSDK wrapper.\"\"\"\n c.run(f'docker run -it --rm -v {cfg.GCPKEY}:/gcloud.json -v {cfg.BUILDDIR}:/{cfg.BUILDDIR.name} google/cloud-sdk '\n f'bash -c \"gcloud auth activate-service-account --key-file=/gcloud.json --project {cfg.PROJECT} && {cmdline}\"',\n replace_env=False, pty=True)\n\n\n@task\ndef cluster(c, command):\n \"\"\"Cluster management: create, delete.\"\"\"\n if command =='create':\n cloudsdk(c, f'gcloud dataproc clusters create {cfg.GCPCLUSTER} --region={cfg.GCPREGION} --worker-machine-type=n1-standard-2 --num-workers=2')\n elif command == 'delete':\n cloudsdk(c, f'gcloud dataproc clusters delete {cfg.GCPCLUSTER} --region={cfg.GCPREGION}')\n else:\n raise ValueError(f'Unsupported command: {command}')\n", "sub_path": "fabfile.py", "file_name": "fabfile.py", "file_ext": "py", "file_size_in_byte": 1407, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "config.VENVDIR.exists", "line_number": 12, "usage_type": "call"}, {"api_name": "config.VENVDIR", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sys.executable", "line_number": 13, "usage_type": "attribute"}, {"api_name": "config.VENVDIR", "line_number": 13, "usage_type": "attribute"}, {"api_name": "config.PYTHON", "line_number": 14, "usage_type": "attribute"}, {"api_name": "config.PYTHON", "line_number": 17, "usage_type": "attribute"}, {"api_name": "fabric.task", "line_number": 7, "usage_type": "name"}, {"api_name": "config.PYTHON", "line_number": 23, "usage_type": "attribute"}, {"api_name": "fabric.task", "line_number": 20, "usage_type": "name"}, {"api_name": "config.GCPKEY", "line_number": 29, "usage_type": "attribute"}, {"api_name": "config.BUILDDIR", "line_number": 29, "usage_type": "attribute"}, {"api_name": "config.PROJECT", "line_number": 30, "usage_type": "attribute"}, {"api_name": "fabric.task", "line_number": 26, "usage_type": "name"}, {"api_name": "config.GCPCLUSTER", "line_number": 38, "usage_type": "attribute"}, {"api_name": "config.GCPREGION", "line_number": 38, "usage_type": "attribute"}, {"api_name": "config.GCPCLUSTER", "line_number": 40, "usage_type": "attribute"}, {"api_name": "config.GCPREGION", "line_number": 40, "usage_type": "attribute"}, {"api_name": "fabric.task", "line_number": 34, "usage_type": "name"}]} +{"seq_id": "106835692", "text": "from django.shortcuts import render\nfrom groupapp.forms import GroupForm\nfrom django.http import HttpResponse\n\n# Create your views here.\n\ndef index(request):\n\treturn HttpResponse(\"hello world\")\n\ndef add_group(request):\n # A HTTP POST?\n if request.method == 'POST':\n form = GroupForm(request.POST)\n\n # Have we been provided with a valid form?\n if form.is_valid():\n # Save the new category to the database.\n form.save(commit=True)\n\n # Now call the index() view.\n # The user will be shown the homepage.\n return index(request)\n else:\n # The supplied form contained errors - just print them to the terminal.\n print(form.errors)\n else:\n # If the request was not a POST, display the form to enter details.\n form = GroupForm()\n\n # Bad form (or form details), no form supplied...\n # Render the form with error messages (if any).\n return render(request, 'add_group.html', {'form': form})\n", "sub_path": "groupapp/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1015, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "django.http.HttpResponse", "line_number": 8, "usage_type": "call"}, {"api_name": "groupapp.forms.GroupForm", "line_number": 13, "usage_type": "call"}, {"api_name": "groupapp.forms.GroupForm", "line_number": 28, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "261537099", "text": "# numerical libraries\nimport numpy as np\n\n# spatial libraries\nimport geopandas as gpd\n\n# pyinterpolate scripts\nfrom pyinterpolate.kriging.helper_functions.euclidean_distance import calculate_distance\n\n\ndef calculate_semivariance(data, lags, step_size):\n \"\"\"Function calculates semivariance of a given set of points.\n \n INPUT:\n :param data: array of coordinates and their values,\n :param lags: array of lags between points,\n :param step_size: distance which should be included in the gamma parameter which enhances range of interest.\n \n OUTPUT:\n :return: semivariance: numpy array of pair of lag and semivariance values where\n semivariance[0] = array of lags\n semivariance[1] = array of lag's values\n semivariance[2] = array of number of points in each lag.\n \n WARNING:\n Function will be deprecated in the final version of the library. Its properties will be covered by the\n Semivariance class object.\"\"\"\n \n distances_array = calculate_distance(data[:, :-1])\n smv = []\n semivariance = []\n\n # Calculate semivariances\n for h in lags:\n gammas = []\n distances_in_range = np.where(\n np.logical_and(\n np.greater_equal(distances_array, h - step_size), np.less_equal(distances_array, h + step_size)))\n for i in range(0, len(distances_in_range[0])):\n row_x = distances_in_range[0][i]\n row_x_h = distances_in_range[1][i]\n gp1 = data[row_x][2]\n gp2 = data[row_x_h][2]\n g = (gp1 - gp2) ** 2\n gammas.append(g)\n if len(gammas) == 0:\n gamma = 0\n else:\n gamma = np.sum(gammas) / (2 * len(gammas))\n smv.append([gamma, len(gammas)])\n\n # Selecting semivariances\n for i in range(len(lags)):\n if smv[i][0] > 0:\n semivariance.append([lags[i], smv[i][0], smv[i][1]])\n else:\n semivariance.append([lags[i], 0, 0])\n\n semivariance = np.vstack(semivariance)\n return semivariance\n \n \n\n\nclass Semivariance:\n\n def __init__(self, areal_data_file, lags=None, step_size=None, id_field='ID'):\n \"\"\"Class for calculation of Areal semivariance. It us used by the methods of Area-to-Area and Area-to-Point\n Kriging. Class has two main methods: semivariance_from_centroids and semivariance_from_areal_data.\n Class is initialized by:\n areal_data: the dictionary of areas and their values,\n population_data: vector files with population centroids derived from population blocks for each area.\n To maintain stability areas in the areal_data and population blocks in the population_data must have\n the same ID filed - id_field, which allows algorithm to merge those datasets.\n\n Calculation methods of the class:\n C1. centroids_semivariance(lags=self.lags, step_size=self.step_size)\n\n Visualization methods of the class:\n\n\n Private methods of the class and their relations to the specific class variables and methods:\n\n\n \"\"\"\n self.areal_data = areal_data_file\n self.geodataframe = gpd.read_file(areal_data_file)\n \n self.ranges = lags\n self.step = step_size\n\n self.id_field = id_field\n\n self.centroids = None # variable updated by the centroids_semivariance() method\n self.distances_dict = None # variable updated by the centroids_semivariance() method\n self.point_support_semivariance = None # variable updated by the centroids_semivariance() method\n self.g_dict = {}\n\n def centroids_semivariance(self, lags=None, step_size=None, update=True, data_column='DATA'):\n \"\"\"\n Function calculates semivariance of areal centroids and their values.\n :param lags: array of lags between points\n :param step_size: distance which should be included in the gamma parameter which enhances range of interest\n :param update: if True then class centroids and point_support_semivariance variables will be updated\n :param data_column: string with a name of column containing data values\n :return: semivariance: numpy array of pair of lag and semivariance values where\n semivariance[0] = array of lags\n semivariance[1] = array of lag's values\n semivariance[2] = array of number of points in each lag\n \"\"\"\n\n # Set lags\n if not lags:\n lags = self.ranges\n\n # Set step size\n if not step_size:\n step_size = self.step\n\n # Calculate centroids positions\n centroids = self._centroids_from_shp(data_column)\n\n # Calculate distances\n try:\n distance_array = calculate_distance(centroids)\n except TypeError:\n centroids = np.asarray(centroids)\n print('Given points array has been transformed into numpy array to calculate distance')\n distance_array = calculate_distance(centroids)\n\n self.distances = distance_array.copy()\n semivariance = self._calculate_semivars(lags, step_size, centroids, distance_array)\n \n # Update object\n if update:\n self.centroids = centroids\n self.point_support_semivariance = semivariance\n\n return semivariance\n\n @staticmethod\n def _calculate_semivars(lags, step_size, points_array, distances_array, rate=None):\n \"\"\"Method calculates semivariance.\n\n INPUT:\n :param lags: list of lags,\n :param step_size: step between lags,\n :param points_array: array with points and their values,\n :param distances_array: array with distances between points\n\n OUTPUT:\n :return semivariance: numpy array of pair of lag and semivariance values where\n semivariance[0] = array of lags\n semivariance[1] = array of lag's values\n semivariance[2] = array of number of points in each lag\"\"\"\n smv = []\n semivariance = []\n\n # Calculate semivariances\n for h in lags:\n gammas = []\n distances_in_range = np.where(\n np.logical_and(\n np.greater_equal(distances_array, h - step_size), np.less_equal(distances_array, h + step_size)))\n for i in range(0, len(distances_in_range[0])):\n row_x = distances_in_range[0][i]\n row_x_h = distances_in_range[1][i]\n if rate is not None:\n gp1 = rate[0]\n gp2 = rate[1]\n else:\n gp1 = points_array[row_x][2]\n gp2 = points_array[row_x_h][2]\n g = (gp1 - gp2) ** 2\n gammas.append(g)\n if len(gammas) == 0:\n gamma = 0\n else:\n gamma = np.sum(gammas) / (2 * len(gammas))\n smv.append([gamma, len(gammas)])\n\n # Selecting semivariances\n for i in range(len(lags)):\n if smv[i][0] > 0:\n semivariance.append([lags[i], smv[i][0], smv[i][1]])\n else:\n semivariance.append([lags[i], 0, 0])\n\n semivariance = np.vstack(semivariance)\n return semivariance\n \n def _centroids_from_shp(self, data_column):\n \"\"\"Method calculates centroids of areas from the given polygon file and returns numpy array with coordinates\n and values for each centroid\n\n INPUT:\n :param data_column: Column with data values (usually rates)\n\n OUTPUT:\n :return centroids_and_vals: numpy array in the form of [[coordinate x,\n coordinate y,\n value of a given area]]\"\"\"\n\n centroids_and_vals = self._get_posx_posy(self.geodataframe, data_column, areal=True, dropna=True)\n\n return centroids_and_vals\n\n def _get_posx_posy(self, geo_df, value_column_name, areal=True, dropna=False, points_type=False):\n \"\"\"Function prepares array for distances calculation from the centroids of areal blocks\n \n INPUT:\n :param geo_df: dataframe with spatial data - areas or set of points,\n :param value_column_name: name of the column with value which is passed as the last column of the\n output array,\n :param areal: default it is True. If data is areal type then centroids of area's are computed\n otherwise geometry column should store a Points,\n :param dropna: default is False. If True then all areas (points) of unknown coordinates or values\n are dropped from the analysis.\n \n OUTPUT:\n :return pos_and_vals: numpy array of the form [[coordinate x1, coordinate y1, value1],\n [coordinate x2, coordinate y2, value2],\n [...., ...., ....],]\n \"\"\"\n geo_dataframe = geo_df.copy()\n\n col_x = 'centroid_pos_x'\n col_y = 'centroid_pos_y'\n \n if areal:\n geo_dataframe[col_x] = geo_dataframe['geometry'].apply(lambda _: _.centroid.x)\n geo_dataframe[col_y] = geo_dataframe['geometry'].apply(lambda _: _.centroid.y)\n else:\n try:\n geo_dataframe[col_x] = geo_dataframe['geometry'].apply(lambda _: _.x)\n geo_dataframe[col_y] = geo_dataframe['geometry'].apply(lambda _: _.y)\n except AttributeError:\n geo_dataframe[col_x] = geo_dataframe['geometry'].apply(lambda _: _[0].x)\n geo_dataframe[col_y] = geo_dataframe['geometry'].apply(lambda _: _[0].y)\n\n\n columns_to_hold = [col_x, col_y, value_column_name, self.id_field]\n columns = list(geo_dataframe.columns)\n\n # remove rows with nan\n if dropna:\n geo_dataframe.dropna(axis=0, inplace=True)\n\n # remove unwanted columns\n for col in columns:\n if col not in columns_to_hold:\n geo_dataframe.drop(labels=col, axis=1, inplace=True)\n\n # set order of columns\n geo_dataframe = geo_dataframe[columns_to_hold]\n \n if not points_type:\n self.geodataframe = geo_dataframe.copy()\n geo_dataframe.set_index(self.id_field, inplace=True)\n geodict = geo_dataframe.to_dict(orient='index')\n self.g_dict = geodict.copy()\n\n pos_and_vals = np.asarray(geo_dataframe.values)\n return pos_and_vals\n", "sub_path": "pyinterpolate/kriging/semivariance_base.py", "file_name": "semivariance_base.py", "file_ext": "py", "file_size_in_byte": 10589, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pyinterpolate.kriging.helper_functions.euclidean_distance.calculate_distance", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.greater_equal", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.less_equal", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 59, "usage_type": "call"}, {"api_name": "geopandas.read_file", "line_number": 87, "usage_type": "call"}, {"api_name": "pyinterpolate.kriging.helper_functions.euclidean_distance.calculate_distance", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 127, "usage_type": "call"}, {"api_name": "pyinterpolate.kriging.helper_functions.euclidean_distance.calculate_distance", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.greater_equal", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.less_equal", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 263, "usage_type": "call"}]} +{"seq_id": "3158021", "text": "import traceback\n\nimport argparse\nimport sys\n\nimport tl.exceptions\n\n\ndef parser():\n return {\n 'help': 'Match the context values to the properties and add the score of the match.'\n }\n\n\ndef add_arguments(parser):\n # input file\n parser.add_argument('input_file', nargs='?', type=argparse.FileType('r'), default=sys.stdin)\n parser.add_argument('--context-file', type=str, dest='context_file', required=False,\n help=\"The file is used to look up context values for matching.\")\n parser.add_argument('--debug', action='store_true',\n help=\"if set, an kgtk debug logger will be saved at home directory. \"\n \"Debug adds two new columns to the output denoting the properties matched and the \"\n \"respective similarities.\")\n parser.add_argument('--similarity-string-threshold', action='store', type=float, dest='similarity_string_threshold',\n default=0.75,\n help='The minimum threshold for similarity with input context for string matching. '\n 'Default: 0.75')\n parser.add_argument('--similarity-quantity-threshold', action='store', type=float,\n dest='similarity_quantity_threshold', default=0.85,\n help='The minimum threshold for similarity with input context for quantity matching. '\n 'Default: 0.85')\n parser.add_argument('--custom-context-file', type=str, dest='custom_context_file', required=False,\n help=\"The file is used to look up context values for matching.\") \n parser.add_argument('--string-separator', action = 'store', type=str, dest = 'string_separator', default = \",\", \n help = \"Any separators to separate from in the context substrings.\")\n\n # output\n parser.add_argument('-o', '--output-column-name', action='store', dest='output_column', default=\"context_score\",\n help='The output column is the named column of the score for the matches '\n 'computed for the context.')\n\n\ndef run(**kwargs):\n try:\n from tl.features.context_match import MatchContext\n input_file_path = kwargs.pop(\"input_file\")\n context_file_path = kwargs.pop(\"context_file\")\n custom_context_file_path = kwargs.pop(\"custom_context_file\")\n string_separator = kwargs.pop(\"string_separator\")\n output_column_name = kwargs.pop(\"output_column\")\n similarity_string_threshold = kwargs.pop(\"similarity_string_threshold\")\n similarity_quantity_threshold = kwargs.pop(\"similarity_quantity_threshold\")\n obj = MatchContext(input_file_path, similarity_string_threshold, similarity_quantity_threshold, \n string_separator, output_column_name, context_file_path, custom_context_file_path)\n result_df = obj.process_data_by_column()\n result_df.to_csv(sys.stdout, index=False)\n except:\n message = 'Command: context-match\\n'\n message += 'Error Message: {}\\n'.format(traceback.format_exc())\n raise tl.exceptions.TLException(message)\n", "sub_path": "tl/cli/context-match.py", "file_name": "context-match.py", "file_ext": "py", "file_size_in_byte": 3198, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "argparse.FileType", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 17, "usage_type": "attribute"}, {"api_name": "tl.features.context_match.MatchContext", "line_number": 53, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 56, "usage_type": "attribute"}, {"api_name": "traceback.format_exc", "line_number": 59, "usage_type": "call"}, {"api_name": "tl.exceptions.exceptions.TLException", "line_number": 60, "usage_type": "call"}, {"api_name": "tl.exceptions.exceptions", "line_number": 60, "usage_type": "attribute"}, {"api_name": "tl.exceptions", "line_number": 60, "usage_type": "name"}]} +{"seq_id": "484874077", "text": "import pygame\npygame.init()\n\ndisplay = pygame.display.Info()\nwindowWidth, windowHeight = display.current_w, display.current_h\n\nframerate = 60\n\nblack = (0, 0, 0)\nwhite = (255, 255, 255)\nred = (255, 0, 0)\ngreen = (0, 255, 0)\nblue = (0, 0, 255)\n\nfont1 = pygame.font.SysFont(\"arial\", 46, False, False)\nfont1Italic = pygame.font.SysFont(\"arial\", 46, False, True)\nfont2 = pygame.font.SysFont(\"arial\", 28, False, False)\nfont2Italic = pygame.font.SysFont(\"arial\", 28, False, True)\n\nwindow = pygame.display.set_mode((800, 800)) # Definira pygame prozor i njegove dimenzije\n\nbackgroundTrack = pygame.mixer.music.load('Multimedia/Music/BackgroundTrack.wav') # Import pozadinske melodije\npygame.mixer.music.set_volume(0.3) # Razina zvuka pozadinske melodije\n\ndef loadData():\n pygame.display.set_caption(\"Rock, Paper, Scissors\") # Postavlja naslov pygame prozora\n\n backgroundImage = pygame.image.load('Multimedia/Images/Background.jpeg') # Importira sliku pozadine\n window.blit(backgroundImage, (0, 0))\n\n textBackgroundBar = pygame.Rect(0, 430, windowWidth, 120)\n pygame.draw.rect(window, (32, 32, 32), textBackgroundBar)\n\n welcomeTextTitle = font1.render(\"Rock, Paper, Scissors\", True, white)\n window.blit(welcomeTextTitle, (400 - welcomeTextTitle.get_width() // 2, 440))\n\n pressEnterToStartText = font2Italic.render(\"~ Press ENTER to start ~\", True, white)\n window.blit(pressEnterToStartText, (400 - pressEnterToStartText.get_width() // 2, 440 + welcomeTextTitle.get_height() + 10))\n", "sub_path": "Configurator.py", "file_name": "Configurator.py", "file_ext": "py", "file_size_in_byte": 1504, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "pygame.init", "line_number": 2, "usage_type": "call"}, {"api_name": "pygame.display.Info", "line_number": 4, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 4, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 16, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 18, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 20, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.load", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.set_volume", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 26, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 28, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 31, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 32, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 32, "usage_type": "attribute"}]} +{"seq_id": "444954431", "text": "import discord\nimport asyncio\nimport sys\nimport os\nfrom subprocess import call\n\nclient = discord.Client()\n\n@client.event\nasync def on_message(message):\n # Bot shall not react to its own messages\n if message.author.id != client.user.id:\n # WTF\n if client.user in message.mentions:\n await client.send_message(message.channel, '{} What the fuck did you just fucking say about me, you little bitch?'.format(message.author.mention))\n \n # Shintel\n if 'intel' in message.content.lower() and 'shintel' not in message.content.lower():\n await client.send_message(message.channel, '{} That\\'s a strange way to spell Shintel...'.format(message.author.mention))\n \n # noVideo\n if 'nvidia' in message.content.lower():\n await client.send_message(message.channel, '{} That\\'s a strange way to spell noVideo...'.format(message.author.mention))\n \n # AyyMD\n if 'amd' in message.content.lower():\n await client.send_message(message.channel, 'ayy lmao')\n \n # Privileged commands\n if message.author.id == '348876272979410954':\n # Clean chat\n if message.content.lower() == 'purge' and message.channel.id != '462663167068340236':\n deleted = await client.purge_from(message.channel)\n await client.send_message(message.channel, 'I deleted {} messages.\\nThank MrDestructoid!!'.format(len(deleted))) \n \n # Update and restart\n if 'order66' in message.content.lower():\n await client.logout()\n call(['git', 'pull'])\n os.execl(sys.executable, sys.executable, *sys.argv)\n\nclient.run(open('token','r').readline().strip())\n", "sub_path": "mrdestructoid.py", "file_name": "mrdestructoid.py", "file_ext": "py", "file_size_in_byte": 1769, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "discord.Client", "line_number": 7, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 39, "usage_type": "call"}, {"api_name": "os.execl", "line_number": 40, "usage_type": "call"}, {"api_name": "sys.executable", "line_number": 40, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 40, "usage_type": "attribute"}]} +{"seq_id": "149007751", "text": "# Viz.py\n# Class with functions for creating visualizations of Kitchen Nightmares data\n# Arjun Srinivasan, May 21 2020\n# \n# Here's an exmaple to demostrate how to use it.\n# \n# myviz = Viz() # instantiate a Viz object\n# myviz.viz_user_spending(data) # call the appropriate viz method (could use one of other three)\n# # Do something with \"viz.png\" # note that visualization is saved as \"viz.png\" in the current directory\n#\n# Imports for basic visualizations\nimport re\nimport matplotlib\nmatplotlib.use(\"TkAgg\")\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n__name__ = \"viz\"\n\n# Ended up not using map-based visualizations:\n\n# Imports for map-based viz - 1\n# import geopandas as gpd\n\n# Imports for map-based viz - 2\n# import geoviews as gv\n# import geoviews.title_sources as gvts\n# from geoviews import dim, opts\n# gv.extension('bokeh')\n\n# Constants for maps\n# get longitude, latitude\n# loc_foco = [43.703022, -72.291034]\n# loc_hop = []\n# loc_lous = [43.702674, -72.289859]\n# loc_pine = [43.702224, -72.289156]\n\nclass Viz:\n pass\n\n # Generic object initialization\n def __init__(self):\n pass\n\n '''\n Function to parse JSON string and return X and Y lists representing data values.\n Input:\n jsonstring - JSON string with the relevant data\n type - 'f' if float data, 'i' if int data\n Output: \n X = X data (RestaurantNames or FoodNames)\n Y = Y data (MoneySpent, MealCount, FoodCount, or FoodRevenue)\n '''\n def customJSONparse(self, jsonstring, type):\n X = []\n Y = []\n if (jsonstring[0] != \"[\"):\n print(\"JSON Parse error: no open bracket [\")\n else:\n items = re.split(\"{\",jsonstring)\n for i in range(0,len(items)):\n attrs = re.split(\",\", items[i])\n for j in range(0,len(attrs)):\n if ((i > 0) and ((j == 0) or (j == 1))):\n ones = re.split(\"'\",attrs[j])\n if j == 0:\n if type == 'r':\n ys = re.findall(\"\\d+\",ones[2])\n Y.append(ys[0])\n else:\n X.append(ones[3])\n elif j == 1:\n if type == 'f':\n ys = re.findall(\"\\d+\",ones[2])\n elif type == 'r':\n X.append(ones[3])\n else:\n ys = re.findall(\"\\d+\",ones[2])\n if type != 'r':\n Y.append(ys[0])\n print(X,Y)\n # print(type(Y[0]))\n return X,Y\n\n '''\n Function to create bar graph (\"viz.png\") of restaurants weighted by sum of money spent at each restaurant.\n Input: \n X = restaurants\n Y = sum of money spent at each restaurant \n Output: Bar graph (see above)\n '''\n # Input:\n def viz_user_spending(self, data):\n\n # Convert JSON data to Python list\n X,Y = self.customJSONparse(data,'f')\n\n # Plot\n sns.set_palette(\"Paired\")\n ax = sns.barplot(X, Y)\n ax.set(ylabel='Money Spent')\n\n #plt.show()\n plt.savefig('viz.png',bbox_inches='tight') # Save the final pie chart\n plt.clf()\n\n return\n\n '''\n Function to create bar graph (\"viz.png\") of restaurants weighted by frequency of meals at each restaurant.\n Input: \n X = restaurants\n Y = number of meals at each restaurant \n Output: Bar graph (above)\n '''\n def viz_user_freq(self, data):\n\n # Convert JSON data to Python list (fill in later)\n X,Y = self.customJSONparse(data,'i')\n\n # Plot\n sns.set_palette(\"Paired\")\n ax = sns.barplot(X, Y)\n ax.set(ylabel='No. of Meals')\n\n #plt.show()\n plt.savefig('viz.png',bbox_inches='tight') # Save the final pie chart\n plt.clf()\n\n return\n\n '''\n Function to create visualization (\"viz.png\") of food items weighted by number purchased.\n Input: \n X = food items\n Y = number of purchases of each food item \n Output: Pie chart percent of revenue each food item brings in\n '''\n def viz_rez_quantity(self, data):\n\n # Convert JSON data to Python list\n labels,wedges = self.customJSONparse(data, 'i')\n\n # Consider removing foods that never have been purchased\n\n sns.set_palette(\"Paired\")\n plt.pie(wedges, labels=labels, labeldistance=None, shadow=True, startangle=90)\n # Removed options: autopct=lambda p: '{:.1f}%'.format(round(p)) if p > 0 else ''\n plt.axis('equal') # Equal aspect ratio (pie drawn as a circle)\n plt.legend(loc=\"best\")\n plt.title(\"Relative quantities of each food purchased\")\n\n #plt.show()\n plt.savefig('viz.png',bbox_inches='tight') # Save the final pie chart\n plt.clf()\n\n return \n\n '''\n Function to create visualization (\"viz.png\") of food items weighted by total sum of money spent on food item.\n Input: \n X = food items\n Y = total sum of money spent on that food item \n Output: Pie chart percent of revenue each food item brings in\n '''\n def viz_rest_money(self, data):\n\n # Convert JSON data to Python list\n labels,wedges = self.customJSONparse(data, 'f')\n\n # Consider removing foods that have zero money spent on them\n\n sns.set_palette(\"Paired\")\n plt.pie(wedges, labels=labels, labeldistance=None, shadow=True, startangle=90)\n # Removed options: autopct=lambda p: '{:.1f}%'.format(round(p)) if p > 0 else ''\n plt.axis('equal') # Equal aspect ratio (pie drawn as a circle)\n plt.legend(loc=\"best\")\n plt.title(\"Share of money spent on each food\")\n\n #plt.show()\n plt.savefig('viz.png',bbox_inches='tight') # Save the final pie chart\n plt.clf()\n\n return \n\n# # Testing!\n\n# # Raw data to visualize\n# data_1 = [[\"Indian Rest 1\", \"Indian Rest 2\", \"Indian Rest 3\"], [100.35,22.67,36.34]]\n# data_2 = [[\"Indian Rest 1\", \"Indian Rest 2\", \"Indian Rest 3\"], [30,25,69]]\n# data_3 = [[\"Naan\",\"Dosa\",\"Paneer\",\"Bhatura\",\"Butter chicken\",\"Chana masala\",\"Chaat\"],[1,5,3,4,0,2,6]]\n# data_4 = [[\"Naan\",\"Dosa\",\"Paneer\",\"Bhatura\",\"Butter chicken\",\"Chana masala\",\"Chaat\"],[85.30,60.25,12.53,4.56,0,40.30,20.05]]\n\n# # JSON strings passed into viz.py\n# array of [ { RestaurantName, MoneySpent } ... ]\n# array of [ { RestaurantName, MealCount } ... ]\n# array of [ { FoodName, FoodCount } ... ]\n# array of [ { FoodName, FoodRevenue } ... ]\n\n# # Example JSON string data to visualize\n# data_1 = [{'RestaurantName': 'Courtyard Cafe', 'MoneySpent': 14.5}, {'RestaurantName': 'Foco', 'MoneySpent': 7.75}]\n# data_2 = \"[{u'RestaurantName': u'Courtyard Cafe', u'MealCount': 13}, {u'RestaurantName': u'Foco', u'MealCount': 20}]\"\n# data_3 = \"[{u'FoodName': u'Pizza', u'FoodCount': 13}, {u'FoodName': u'Hamburger', u'FoodCount': 20}]\"\n# data_4 = \"[{u'FoodName': u'Pizza Cafe', u'FoodRevenue': 13.87}, {u'FoodName': u'Hamburger', u'FoodRevenue': 84.28}]\"\n\n# myviz = Viz() # instantiate a Viz object\n# # myviz.viz_user_spending(data_1) # call viz method 1\n# myviz.viz_user_freq(data_2) # call viz method 2\n# myviz.viz_rez_quantity(data_3) # call viz method 3\n# myviz.viz_rest_money(data_4) # call viz method 4", "sub_path": "frontend/viz.py", "file_name": "viz.py", "file_ext": "py", "file_size_in_byte": 7458, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "matplotlib.use", "line_number": 14, "usage_type": "call"}, {"api_name": "re.split", "line_number": 59, "usage_type": "call"}, {"api_name": "re.split", "line_number": 61, "usage_type": "call"}, {"api_name": "re.split", "line_number": 64, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 67, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 73, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 77, "usage_type": "call"}, {"api_name": "seaborn.set_palette", "line_number": 98, "usage_type": "call"}, {"api_name": "seaborn.barplot", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "seaborn.set_palette", "line_number": 121, "usage_type": "call"}, {"api_name": "seaborn.barplot", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "seaborn.set_palette", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pie", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 148, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 149, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 149, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 150, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 150, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 153, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "name"}, {"api_name": "seaborn.set_palette", "line_number": 172, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pie", "line_number": 173, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 173, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 175, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 175, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 176, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 176, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 177, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 180, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 180, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 181, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 181, "usage_type": "name"}]} +{"seq_id": "180710647", "text": "import json\nimport util\nimport constantes\nimport datetime\nimport random\nfrom servidor_udp import Servidor\n\nclass ServidorNomes(Servidor):\n\n def __init__(self, port):\n super(ServidorNomes, self).__init__(port)\n\n with open(constantes.SERVERS_OPERATION_FILE, 'r') as arq:\n self.__servers = json.loads(arq.read())\n\n print('\\nATENÇÃO\\nO servidor de nomes não garante e nem verifica se ' +\n 'os servidores de operacão estão online.\\n\\n')\n\n def novaRequisicao(self, data, client_address):\n\n msg = util.get_format(data.decode())\n\n if (msg == None):\n return\n\n operacao = None\n\n try:\n print('Enviando os endereços de servidores de %s conhecidos para %s na porta %d...' %\n (constantes.Operacoes(int(msg)).name, client_address[0], client_address[1]))\n\n operacao = self.__servers[constantes.Operacoes(int(msg)).name.lower()]\n\n dados = None\n\n if (len(operacao) == 0):\n dados = constantes.NO_COMPATIBLE_SERVER_COD_ERROR\n else:\n dados = json.dumps(operacao)\n\n except KeyError:\n dados = constantes.NO_COMPATIBLE_SERVER_COD_ERROR\n\n status = 'FAIL'\n\n if random.choice([True, False]):\n self.send(util.set_format(dados), client_address)\n status = 'OK'\n\n print(status + '. (' + str(datetime.datetime.now()) + ')')\n\nif __name__ == '__main__':\n ServidorNomes(constantes.SERVER_PORT).start()\n", "sub_path": "RPC/Servidor de Nomes - UDP/Servidor Nomes/servidor_nomes.py", "file_name": "servidor_nomes.py", "file_ext": "py", "file_size_in_byte": 1527, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "servidor_udp.Servidor", "line_number": 8, "usage_type": "name"}, {"api_name": "constantes.SERVERS_OPERATION_FILE", "line_number": 13, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 14, "usage_type": "call"}, {"api_name": "util.get_format", "line_number": 21, "usage_type": "call"}, {"api_name": "constantes.Operacoes", "line_number": 30, "usage_type": "call"}, {"api_name": "constantes.Operacoes", "line_number": 32, "usage_type": "call"}, {"api_name": "constantes.NO_COMPATIBLE_SERVER_COD_ERROR", "line_number": 37, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 39, "usage_type": "call"}, {"api_name": "constantes.NO_COMPATIBLE_SERVER_COD_ERROR", "line_number": 42, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 46, "usage_type": "call"}, {"api_name": "util.set_format", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 50, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 50, "usage_type": "attribute"}, {"api_name": "constantes.SERVER_PORT", "line_number": 53, "usage_type": "attribute"}]} +{"seq_id": "349083595", "text": "import pandas as pd\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.preprocessing import StandardScaler\r\nfrom sklearn.neighbors import KNeighborsClassifier\r\nfrom sklearn.metrics import classification_report,confusion_matrix\r\nimport pickle\r\n\r\ndf = pd.read_csv('BBFinalDataset.csv')\r\n\r\nX = df.drop('GROUP',axis=1)\r\ny = df['GROUP']\r\n\r\nscaler = StandardScaler() \r\nscaler.fit(df.drop('GROUP',axis=1))\r\n\r\nout = scaler.transform(df.drop('GROUP',axis=1))\r\ndf_scal = pd.DataFrame(out,columns=df.columns[:-1])\r\n\r\nX_train, X_test, y_train, y_test = train_test_split(df_scal,df['GROUP'],test_size=0.30,random_state=101)\r\n\r\nknn = KNeighborsClassifier(n_neighbors=1)\r\nknn.fit(X,y)\r\n\r\npickle.dump(knn, open('model.pkl','wb'))\r\n\r\nmodel = pickle.load(open('model.pkl','rb'))\r\n\r\nfrom werkzeug.wrappers import Request, Response\r\nfrom flask import Flask, render_template, Response, request, redirect, url_for,jsonify \r\nfrom flask_cors import cross_origin\r\nimport random\r\n\r\n# app\r\napp = Flask(__name__)\r\n\r\n@app.route('/')\r\n# routes\r\ndef home():\r\n return render_template(\"index.html\")\r\n\r\ndef contestants(output):\r\n\r\n Dict = {1 : [\"Oviya\",\"Riythvika\",\"Mugen\",\"Losliya\",\"Aari Arjunan\",\"Tharshan\",\"Raju\",\"Ciby\"], 2 : \r\n [\"Gayathri\",\"Mahat\",\"Vanitha\",\"Archana\",\"Niroop\",\"Snehan\",\"Aishwarya\",\"Suresh\",\"Abishek\"], 3 :\r\n [\"Ramya\",\"Sandy\",\"Gabriella\",\"Velmurugan\",\"Aajeedh\",\"Isaivani\",\"Chinnaponnu\",\"Iykki\",\"Imman\"], 4 :\r\n [\"Juliana\",\"Sakthi\",\"Madhumitha\",\"Meera\",\"Anitha\",\"Vaishnavi\",\"Ganja\",\"Suja\",\"Thamarai\"], 5 :\r\n [\"Aarava\",\"Sanam\",\"Ramya Pandian\",\"Balaji Murugadoss\",\"Rio\",\"Priyanka\",\"Varun\",\"Shariq\"], 6 :\r\n [\"Anuya\",\"Nadia\",\"Anantha Vaithiyanadhan\",\"Mamathi\",\"Nithya\",\"Mohan\",\"Fathima\",\"Suchitra\",\"Vaiyapuri\"], 7 :\r\n [\"Ganesh\",\"Balaji\",\"Ponnambalam\",\"Cheran\",\"Saravanan\",\"Ramesh\",\"Abhinay Vaddi\",\"Mathumitha Germany\"], 8 :\r\n [\"Namitha\",\"Vijayalakshmi\",\"Yashika\",\"Mumtaz\",\"Sherin\",\"Sakshi\",\"Kasthuri\",\"Abhirami\",\"Rekha\"], 9 :\r\n [\"Harish\",\"Bindu\",\"Raiza\",\"Janani\",\"Kavin\",\"Shivani\",\"Samyuktha\",\"Akshara\",\"Pavani\"], 10 :\r\n [\"Harathi\",\"SomShekar\",\"Suruthi\",\"Bharani\",\"Reshma\",\"Sendrayan\",\"Daniel\",\"Kaajal\",\"Nisha\"]}\r\n\r\n r = Dict[output]\r\n return random.choice(r)\r\n\r\n\r\n@app.route(\"/people/\", methods=[\"GET\",\"POST\"])\r\ndef people():\r\n if request.method == \"POST\":\r\n Q1 = int(request.form['q1']);\r\n Q2 = int(request.form['q2']);\r\n Q3 = int(request.form['q3']);\r\n Q4 = int(request.form['q4']);\r\n Q5 = int(request.form['q5']);\r\n Q6 = int(request.form['q6']); \r\n Q7 = int(request.form['q7']); \r\n Q8 = int(request.form['q8']); \r\n Q9 = int(request.form['q9']);\r\n Q10 = int(request.form['q10']); \r\n Q11 = int(request.form['q11']); \r\n \r\n arr = [Q1,Q2,Q3,Q4,Q5,Q6,Q7,Q8,Q9,Q10,Q11]; \r\n result = contestants(knn.predict([arr])[0]);\r\n \r\n return render_template('index.html',text = result);\r\n\r\n\r\nfrom werkzeug.serving import run_simple\r\n# firebase = firebase.FirebaseApplication(\"https://tamilnews-28a69-default-rtdb.firebaseio.com/\",None)\r\nrun_simple('localhost', 9600, app)\r\n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 3127, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "pandas.read_csv", "line_number": 8, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 13, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 17, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 19, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 21, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 24, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 39, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 60, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 60, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 61, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 61, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 62, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 62, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 63, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 63, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 64, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 64, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 65, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 65, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 66, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 66, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 67, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 67, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 68, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 68, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 69, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 69, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 70, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 70, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 71, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 71, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 76, "usage_type": "call"}, {"api_name": "werkzeug.serving.run_simple", "line_number": 81, "usage_type": "call"}]} +{"seq_id": "167857000", "text": "\"\"\"Test NextDNS diagnostics.\"\"\"\nimport json\n\nfrom homeassistant.components.diagnostics import REDACTED\nfrom homeassistant.core import HomeAssistant\n\nfrom . import init_integration\n\nfrom tests.common import load_fixture\nfrom tests.components.diagnostics import get_diagnostics_for_config_entry\nfrom tests.typing import ClientSessionGenerator\n\n\nasync def test_entry_diagnostics(\n hass: HomeAssistant, hass_client: ClientSessionGenerator\n) -> None:\n \"\"\"Test config entry diagnostics.\"\"\"\n settings = json.loads(load_fixture(\"settings.json\", \"nextdns\"))\n\n entry = await init_integration(hass)\n\n result = await get_diagnostics_for_config_entry(hass, hass_client, entry)\n\n assert result[\"config_entry\"] == {\n \"entry_id\": entry.entry_id,\n \"version\": 1,\n \"domain\": \"nextdns\",\n \"title\": \"Fake Profile\",\n \"data\": {\"profile_id\": REDACTED, \"api_key\": REDACTED},\n \"options\": {},\n \"pref_disable_new_entities\": False,\n \"pref_disable_polling\": False,\n \"source\": \"user\",\n \"unique_id\": REDACTED,\n \"disabled_by\": None,\n }\n assert result[\"dnssec_coordinator_data\"] == {\n \"not_validated_queries\": 25,\n \"validated_queries\": 75,\n \"validated_queries_ratio\": 75.0,\n }\n assert result[\"encryption_coordinator_data\"] == {\n \"encrypted_queries\": 60,\n \"unencrypted_queries\": 40,\n \"encrypted_queries_ratio\": 60.0,\n }\n assert result[\"ip_versions_coordinator_data\"] == {\n \"ipv6_queries\": 10,\n \"ipv4_queries\": 90,\n \"ipv6_queries_ratio\": 10.0,\n }\n assert result[\"protocols_coordinator_data\"] == {\n \"doh_queries\": 20,\n \"doh3_queries\": 15,\n \"doq_queries\": 10,\n \"dot_queries\": 30,\n \"tcp_queries\": 0,\n \"udp_queries\": 40,\n \"doh_queries_ratio\": 17.4,\n \"doh3_queries_ratio\": 13.0,\n \"doq_queries_ratio\": 8.7,\n \"dot_queries_ratio\": 26.1,\n \"tcp_queries_ratio\": 0.0,\n \"udp_queries_ratio\": 34.8,\n }\n assert result[\"settings_coordinator_data\"] == settings\n assert result[\"status_coordinator_data\"] == {\n \"all_queries\": 100,\n \"allowed_queries\": 30,\n \"blocked_queries\": 20,\n \"default_queries\": 40,\n \"relayed_queries\": 10,\n \"blocked_queries_ratio\": 20.0,\n }\n", "sub_path": "tests/components/nextdns/test_diagnostics.py", "file_name": "test_diagnostics.py", "file_ext": "py", "file_size_in_byte": 2325, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "homeassistant.core.HomeAssistant", "line_number": 15, "usage_type": "name"}, {"api_name": "tests.typing.ClientSessionGenerator", "line_number": 15, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 18, "usage_type": "call"}, {"api_name": "tests.common.load_fixture", "line_number": 18, "usage_type": "call"}, {"api_name": "tests.components.diagnostics.get_diagnostics_for_config_entry", "line_number": 22, "usage_type": "call"}, {"api_name": "homeassistant.components.diagnostics.REDACTED", "line_number": 29, "usage_type": "name"}, {"api_name": "homeassistant.components.diagnostics.REDACTED", "line_number": 34, "usage_type": "name"}]} +{"seq_id": "565875509", "text": "#\n# Copyright (C) 2018-2019 UAVCAN Development Team \n# This software is distributed under the terms of the MIT License.\n#\n\nimport re\nimport typing\nimport logging\n\n\nGrammarConstructHandler = typing.Union[\n typing.Callable[[], typing.Any],\n typing.Callable[[str], typing.Any],\n typing.Callable[[str, str], typing.Any],\n typing.Callable[[str, str, str], typing.Any],\n typing.Callable[[str, str, str, str], typing.Any],\n typing.Callable[[str, str, str, str, str], typing.Any],\n typing.Callable[[str, str, str, str, str, str], typing.Any],\n]\n\n\n_logger = logging.getLogger(__name__)\n\n\nclass InvalidGrammarError(ValueError):\n pass\n\n\nclass RegularGrammarMatcher:\n \"\"\"\n Holds a collection of regular expression that together define a simple regular grammar.\n Can process text, matching the defined grammar rules against it; invokes a specified handler on first match\n and returns its output. If no match is found, raises InvalidGrammarError. The arguments of the handler are\n the captured strings, if any are specified; nothing otherwise.\n \"\"\"\n\n def __init__(self) -> None:\n # static type not specified because mypy is malfunctioning on re.Pattern\n # noinspection Mypy\n self._rules = [] # type: ignore\n\n def add_rule(self,\n regular_expression: str,\n handler: GrammarConstructHandler) -> None:\n self._rules.append((re.compile(regular_expression), handler))\n\n def match(self, text: str) -> typing.Any:\n for regexp, handler in self._rules:\n match = re.match(regexp, text)\n if match:\n captured = match.groups()\n _logger.debug('Text %r produced %r matching this: %s', text, captured, regexp.pattern)\n return handler(*captured)\n\n raise InvalidGrammarError('Invalid grammar: %s' % text)\n\n\ndef _unittest_regular_grammar_matcher() -> None:\n from pytest import raises\n\n m = RegularGrammarMatcher()\n m.add_rule(r'float(\\d\\d?)$', lambda bits: 'F%d' % int(bits))\n m.add_rule(r'int(\\d\\d?)$', lambda bits: 'I%d' % int(bits))\n m.add_rule(r'uint(\\d\\d?)$', lambda bits: 'U%d' % int(bits))\n m.add_rule(r'void(\\d\\d?)$', lambda bits: 'V%d' % int(bits))\n m.add_rule(r'float(\\d\\d?)$', lambda _: None) # Will never be invoked - consumed by previously defined\n m.add_rule(r'([a-zA-Z0-9_\\.]+?)\\.(\\d+)(?:.(\\d+))?$', lambda n, j, m: (n, int(j), None if m is None else int(m)))\n\n assert m.match('float64') == 'F64'\n assert m.match('int13') == 'I13'\n assert m.match('void52') == 'V52'\n\n with raises(InvalidGrammarError):\n m.match('no match')\n\n assert m.match('namespace.nested.Type.123.456') == ('namespace.nested.Type', 123, 456)\n assert m.match('namespace.nested.Type.123') == ('namespace.nested.Type', 123, None)\n with raises(InvalidGrammarError):\n m.match('namespace.nested.Type.')\n\n with raises(InvalidGrammarError):\n m.match('namespace.nested.Type.123.')\n", "sub_path": "pydsdl/regular_grammar_matcher.py", "file_name": "regular_grammar_matcher.py", "file_ext": "py", "file_size_in_byte": 3005, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "typing.Union", "line_number": 11, "usage_type": "attribute"}, {"api_name": "typing.Callable", "line_number": 12, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 12, "usage_type": "attribute"}, {"api_name": "typing.Callable", "line_number": 13, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 13, "usage_type": "attribute"}, {"api_name": "typing.Callable", "line_number": 14, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 14, "usage_type": "attribute"}, {"api_name": "typing.Callable", "line_number": 15, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 15, "usage_type": "attribute"}, {"api_name": "typing.Callable", "line_number": 16, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 16, "usage_type": "attribute"}, {"api_name": "typing.Callable", "line_number": 17, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 17, "usage_type": "attribute"}, {"api_name": "typing.Callable", "line_number": 18, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 18, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 22, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 45, "usage_type": "call"}, {"api_name": "re.match", "line_number": 49, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 73, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 78, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 81, "usage_type": "call"}]} +{"seq_id": "241168290", "text": "# -*- coding: utf-8 -*-\n#\n# Copyright (c) 2010, Logica\n# Author: Nick Piper \n\nimport pkg_resources\nimport re\nimport urllib\nfrom subprocess import Popen, PIPE\n\nfrom genshi.builder import tag\n\nfrom trac.util.datefmt import from_utimestamp, to_utimestamp, utc\nfrom datetime import datetime\n\nfrom trac.admin import AdminCommandError, IAdminCommandProvider\nfrom trac.core import Component, implements, ExtensionPoint\nfrom trac.util.translation import _\nfrom trac.config import IntOption\nfrom trac.versioncontrol import RepositoryManager, IRepositoryChangeListener\nfrom tracrpc.api import IXMLRPCHandler\nfrom trac.perm import IPermissionRequestor\n\nfrom announcer.api import AnnouncementSystem, IAnnouncementProducer\n\nfrom svnverifyplugin.announcements import SVNVerifyFailEvent\n\n# this uses paths, IDs and names... Maybe it can be simplified to use\n# only a single item as the keying variable. Trac itself seems to have\n# a bit of a mix still, as until recently, they only supported a\n# single repository per project.\n\nclass SVNVerifyCommands(Component):\n \"\"\"Perform integrity checks on subversion repository\"\"\"\n \n implements(IAdminCommandProvider,\n IXMLRPCHandler,\n IPermissionRequestor,\n IRepositoryChangeListener,\n IAnnouncementProducer)\n\n number_of_commits_to_verify = IntOption(\"svn\", \"verify_n_commits\", -1, \n doc=\"Number of commits to verify when verifying 'all'. -1 for really all.\")\n\n # IAnnouncementProducer\n def realms(self):\n yield 'integrity'\n\n # IPermissionRequestor methods\n def get_permission_actions(self):\n return ['SVNVERIFY_REPORT', 'SVNVERIFY_RUN']\n\n # IRepositoryChangeListener\n def changeset_added(self, repos, changeset):\n if repos.name.split(\":\",2)[0] == \"svn\":\n self.log.debug(\"Verifying new changeset %s to %s\", changeset.rev, repos.repos.path)\n self.verify(repos.id, repos.repos.path, changeset.rev)\n \n def changeset_modified(self, repos, changeset, old_changeset):\n pass\n\n #IAdminCommandProvider methods\n def get_admin_commands(self):\n yield ('svn verify', '',\n 'Run svnadmin verify against repository',\n self._complete_admin_command, self._admin_verify)\n\n def _complete_admin_command(self, args):\n return []\n\n def _admin_verify(self):\n return self.verifyAll()\n \n # IXMLRPCHandler methods\n def xmlrpc_namespace(self):\n return 'svn'\n\n def xmlrpc_methods(self):\n yield ('SVNVERIFY_RUN', ((bool, int, str, int),), self._rpcverify, \"verify\")\n yield ('SVNVERIFY_RUN', ((bool, ),), self._rpcverifyall, \"verifyAll\")\n yield ('SVNVERIFY_REPORT', ((bool, ),), self.getStatus)\n\n def _rpcverifyall(self, req):\n \"\"\"Run svnadmin verify against a repository\n Pass revision as None or -1 to check all revisions.\"\"\"\n req.perm.require('TRAC_ADMIN') \n return bool(self.verifyAll()==0)\n\n def _rpcverify(self, req, repository_id, path, revision):\n \"\"\"Run svnadmin verify against a repository\n Pass revision as None or -1 to check all revisions.\"\"\"\n req.perm.require('TRAC_ADMIN')\n return bool(self.verify(repository_id, path, revision)==0)\n\n # own methods\n def getStatus(self, req):\n \"\"\"Get overall status from the last time verification was performed.\"\"\"\n db = self.env.get_read_db()\n cursor = db.cursor()\n rm = RepositoryManager(self.env)\n for reponame, info in rm.get_all_repositories().iteritems():\n if info.get('type',rm.repository_type) == \"svn\" or (rm.repository_type == 'svn' and info.get('type') == ''):\n self.log.debug(\"Checking database for status of %s\", info)\n cursor.execute(\"SELECT result FROM svnverify_log \"\n \"WHERE repository_id = %s \"\n \"ORDER BY time DESC LIMIT 1\",\n (info['id'],))\n row = cursor.fetchone()\n if row and row[0] != 0:\n return False\n return True\n \n def verify(self, repository_id, path, revision=None, start=None):\n \"\"\"Run svnadmin verify against a repository.\n Pass revision as None or -1 to check all revisions.\n \"\"\"\n if revision < 0:\n revision = None\n self.log.info(\"Verifying %s at %s\", repository_id, path)\n if revision is not None:\n cmdline = [\"svnadmin\",\"verify\",\"-r\",str(int(revision)),path]\n level = \"revision\"\n elif start is not None:\n cmdline = [\"svnadmin\",\"verify\", \"-r\",\"%d:HEAD\" % start,path]\n level = \"partial\"\n else:\n cmdline = [\"svnadmin\",\"verify\",path]\n level = \"full\"\n self.log.debug(cmdline)\n child = Popen(cmdline, bufsize=-1, stdin=PIPE, stdout=PIPE,\n stderr=PIPE)\n (out, err) = child.communicate()\n self.log.debug(out)\n if child.returncode == 0:\n self.log.debug(err)\n else:\n self.log.warning(\"Failed svnadmin of %s\", path)\n self.log.warning(err)\n @self.env.with_transaction()\n def do_insert(db):\n cursor = db.cursor()\n cursor.execute(\"INSERT INTO svnverify_log (repository_id, type, result, log, time) VALUES (%s,%s,%s,%s,%s)\",\n (repository_id, level, child.returncode, err, to_utimestamp(datetime.now(utc))))\n \n if child.returncode == 0:\n return True\n else:\n announcer = AnnouncementSystem(self.env)\n announcer.send(SVNVerifyFailEvent(\"integrity\", \"verifyfail\", path,\n log=err))\n return False\n\n def verifyAll(self):\n all_verified_good = True\n rm = RepositoryManager(self.env)\n for reponame, info in rm.get_all_repositories().iteritems():\n self.log.debug(\"Considering %s\", info)\n if info.get('type',rm.repository_type) == \"svn\" or (rm.repository_type == 'svn' and info.get('type') == ''):\n if self.number_of_commits_to_verify < 0:\n bound = 0\n elif self.number_of_commits_to_verify == 0:\n self.log.warning(\"Not actually verifying any commits due to [svn]verify_n_commits = 0\")\n return 0\n else:\n y = rm.get_repository(reponame).youngest_rev\n bound = max(0, y - self.number_of_commits_to_verify + 1)\n self.log.debug(\"Only want to verify %d commits, so range is %d:HEAD (HEAD is currently %d)\", \n self.number_of_commits_to_verify,\n bound,\n y)\n if not self.verify(info['id'], info['dir'], start=bound):\n all_verified_good = False\n if not all_verified_good:\n return 1\n else:\n return 0\n", "sub_path": "svnverifyplugin/commands.py", "file_name": "commands.py", "file_ext": "py", "file_size_in_byte": 7144, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "trac.core.Component", "line_number": 33, "usage_type": "name"}, {"api_name": "trac.core.implements", "line_number": 36, "usage_type": "call"}, {"api_name": "trac.admin.IAdminCommandProvider", "line_number": 36, "usage_type": "argument"}, {"api_name": "tracrpc.api.IXMLRPCHandler", "line_number": 37, "usage_type": "argument"}, {"api_name": "trac.perm.IPermissionRequestor", "line_number": 38, "usage_type": "argument"}, {"api_name": "trac.versioncontrol.IRepositoryChangeListener", "line_number": 39, "usage_type": "argument"}, {"api_name": "announcer.api.IAnnouncementProducer", "line_number": 40, "usage_type": "argument"}, {"api_name": "trac.config.IntOption", "line_number": 42, "usage_type": "call"}, {"api_name": "trac.versioncontrol.RepositoryManager", "line_number": 100, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 130, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 130, "usage_type": "name"}, {"api_name": "subprocess.PIPE", "line_number": 131, "usage_type": "name"}, {"api_name": "trac.util.datefmt.to_utimestamp", "line_number": 143, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 143, "usage_type": "call"}, {"api_name": "trac.util.datefmt.utc", "line_number": 143, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 143, "usage_type": "name"}, {"api_name": "announcer.api", "line_number": 148, "usage_type": "name"}, {"api_name": "announcer.api.AnnouncementSystem", "line_number": 148, "usage_type": "call"}, {"api_name": "announcer.api.send", "line_number": 149, "usage_type": "call"}, {"api_name": "announcer.api", "line_number": 149, "usage_type": "name"}, {"api_name": "svnverifyplugin.announcements.SVNVerifyFailEvent", "line_number": 149, "usage_type": "call"}, {"api_name": "trac.versioncontrol.RepositoryManager", "line_number": 155, "usage_type": "call"}]} +{"seq_id": "621548515", "text": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\n# Test modbus multiples registros para SB modelos 2.5 y superiores y Sunny Island\n# @autor: JanusHL para Control FV Copyright (c) 2019 JanusHL\n# @creado: 25/05/2019\n# @versión: 0.1beta\n# @créditos: Diseñado a partir de una idea de 'stoberblog' https://github.com/stoberblog/sunspec-modbus\n# Copyright (c) 2017 stoberblog para las clases ctypes incluidas en el fichero sma.py\n#\n# @licencia: Uso libre de este script si se respeta el copyright (c) de los autores. \n\n\n\"\"\"\n# registros comunes al SB (Sunny Boy)\n30201\t\"Condition:\n\t35 = Fault\n\t303 = Off\n\t307 = Ok\n\t455 = Warning\"\t\tU32\n30217\t\"Grid relay/contactor:\n 51 = Closed\n 311 = Open\n 16777213 = Information not available\"\t\tU32\n\n30513\tTotal yield\tWh\tU64\n30517\tDaily yield\tWh\tU64\n30521\tOperating time\ts\tU64\n30525\tFeed-in time\ts\tU64\n30529\tTotal yield\tWh\tU32\n30531\tTotal yield\tkWh\tU32\n30533\tTotal yield\tMWh\tU32\n30535\tDaily yield\tWh\tU32\n30537\tDaily yield\tkWh\tU32\n30539\tDaily yield\tMWh\tU32\n30541\tOperating time\ts\tU32\n30543\tFeed-in time\ts\tU32\n30559\tNumber of events for user\t\tU32\n30561\tNumber of events for installer\t\tU32\n30563\tNumber of events for service\t\tU32\n30581\tCounter reading of power drawn counter\tWh\tU32\n30583\tGrid feed-in counter reading\tWh\tU32\n30599\tNumber of grid connections\t\tU32\n30775\tPower\tW\tS32\n30777\tPower L1\tW\n30779\tPower L2\tW\n30781\tPower L3\tW\n30783\tGrid voltage phase L1\tV\n30785\tGrid voltage phase L2\tV\n30787\tGrid voltage phase L3\tV\n30789\tGrid voltage phase L1 against L2\tV\n30791\tGrid voltage phase L2 against L3\tV\n30793\tGrid voltage phase L3 against L1\tV\n30795\tGrid current\tA\n30803\tGrid frequency\tHz\n30805\tReactive power\tVAr\n30807\tReactive power L1\tVAr\n30809\tReactive power L2\tVAr\n30811\tReactive power L3\tVAr\n30813\tApparent power\tVA\n30815\tApparent power L1\tVA\n30817\tApparent power L2\tVA\n30819\tApparent power L3\tVA\n34113\tInternal temperature\t°C\tS32\n40135\tNominal frequency\tHz\tU32\n\n--------------------------------------------\n# registros comunes al SI (Sunny Island)\n30837\tActive power limitation P, active power configuration\tW\tU32\n30839\tActive power limitation P, active power configuration\t%\tU32\n30843\tBattery current\tA\tS32\n30845\tCurrent battery state of charge\t%\tU32\n30847\tCurrent battery capacity\t%\tU32\n30849\tBattery temperature\t°C\tS32\n30851\tBattery voltage\tV\tU32\n30855\tCurrent battery charging set voltage\tV\tU32\n30865\tPower drawn\tW\tS32\n30867\tPower grid feed-in\tW\tS32\n-------------------------------------------\nEste programa sirve para testear los equipos SMA, teniendo en cuenta ciertos parámetros:\nTCP_IP --> es la dirección IP del equipo que queremos testear.\nUNIT_ID --> es la unidad modbus que tiene asignada el equipo, normalmente es 3 pero debemos estar seguros\nde ello, sobre todo cuando existen varios equipos SMA en la misma red.\nPORT --> suele ser 502 por defecto, salvo que se haya modificado por alguna razón.\nRegistros a leer:\nHay definida una lista de registros para los SB en el diccionario sbRegs{} que se codifica en una matriz/tuple\nsmaDat.\nEl campo \"name\" contiene la etiqueta que define el registro a leer.\nEl campo \"addr\" contiene la dirección modbus del registro a leer. (No es necesario restar -1)\nEl campo \"leng\" contiene el número de registros que debemos leer para que nos de un valor válido.\n (comprobar la lista superior o la original de SMA para saber si son 2 (U32/S32) o 4 (U64)\nEl campo \"unit\" contiene la unidad del registro leido y se muestra la final de la línea impresa.\n\nEl programa consta del fichero test_sb.py y el módulo de 'clase' sma.py, que deben estar en la misma carpeta.\nInicialmente se leen los valores más importantes y además se graban en el fichero (datos_sma.txt) en la misma\ncarpeta donde tengamos el programa.\n\n\"\"\"\n\n\nimport os\nimport time\nfrom collections import namedtuple\n# importamos modulo desde sma.py\nimport sma\nfrom sma import convert2 as c2\n\n# Registros\n\n \n# Direccion TCP/IP para el equipo a testear\nTCP_IP = '192.168.0.253' # aquí pones la IP del SMA\nUNIT_ID = 3 # unidad modbus del equipo SMA (suele ser 3)\nPORT=502\n\n# definimos los diccionarios de conversion registros 30201 y 30217\nsbstt={35:'Fallo',303:'Off',307:'Ok',455:'Alarma', 51:'Cerrado',311:'Abierto'}\n#sbrele={51:'Cerrado',311:'Abierto'}\n\n# definimos los registros que queremos acceder en el SB\nsbRegs={}\nsmaDat=namedtuple('smaDat','name addr leng unit mult')\nsbRegs[0]=smaDat(\"Estado:\", 30201, 2, 'Stt', 0)\nsbRegs[1]=smaDat(\"Conexión:\", 30217, 2, 'on/off', 0)\nsbRegs[2]=smaDat(\"Producción Total:\", 30529, 2, 'kWh',0.001 ) # Convertido a Kwh\nsbRegs[3]=smaDat(\"Producción Diaria:\", 30535, 2,'kWh',0.001) # Convertido a Kwh\nsbRegs[4]=smaDat(\"Potencia Actual:\", 30775, 2,'W',0.01)\nsbRegs[5]=smaDat(\"DC Amps:\", 30769, 2,'A',0.001)\nsbRegs[6]=smaDat(\"DC Volt:\", 30771, 2,'V',0.001)\nsbRegs[7]=smaDat(\"DC Watts:\", 30773, 2,'W',0.01)\nsbRegs[8]=smaDat(\"Temp. Interna:\",34113,2,'°C', 1)\nsbRegs[9]=smaDat(\"Frecuencia:\",40135,2,'Hz', 1)\n\ndef save_data(reg_ini,data):\n f=open(\"datos_sma.txt\",\"a\")\n f.write(\"RegIni: \" + str(reg_ini) + \"\\n\")\n f.write(str(data) + \"\\n\")\n f.close()\n\ntry:\n\n try:\n mbus = sma.mbusTCP(UNIT_ID, TCP_IP, PORT)\n mbus.openTCP()\n except:\n print (\"error Iniciando proceso...\")\n raise\n \n try:\n #leemos tabla de registros del SB \n for i in range(0, len(sbRegs)):\n data = mbus.read_data(sbRegs[i].addr, sbRegs[i].leng)\n #data=[0x00,0xFA]\n Translate=c2()\n Translate.u16.h = data[1]\n Translate.u16.l = data[0]\n valor=Translate.uint32\n if i<2:\n unit=(sbstt.get(valor)) \n print(sbRegs[i].name, unit)\n #save_data(sbRegs[i].name,valor, unit)\n else:\n print (sbRegs[i].name,valor * sbRegs[i].mult, sbRegs[i].unit )\n #save_data(sbRegs[i].name,valor * sbRegs[i].mult, sbRegs[i].unit )\n\n except:\n print (\"error leyendo datos...\")\n raise\n \nfinally:\n # desconectamos los sockets \n print('\\nCLOSING '+ TCP_IP)\n mbus.closeTCP()\n\n \n", "sub_path": "SMA/test_sb.py", "file_name": "test_sb.py", "file_ext": "py", "file_size_in_byte": 6089, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "collections.namedtuple", "line_number": 122, "usage_type": "call"}, {"api_name": "sma.mbusTCP", "line_number": 143, "usage_type": "call"}, {"api_name": "sma.convert2", "line_number": 154, "usage_type": "call"}]} +{"seq_id": "412051641", "text": "from rest_framework import serializers\n\nfrom .tag import TagSerializer\nfrom ..models.post import Post\n\n\nclass PostSerializer(serializers.ModelSerializer):\n tags = TagSerializer(many=True)\n\n class Meta:\n model = Post\n fields = (\n 'id',\n 'title',\n 'slug',\n 'published',\n 'published_on',\n 'feature_image',\n 'feature_color',\n 'body',\n 'tags',\n 'updated_at',\n )\n", "sub_path": "apps/cms/serializers/post.py", "file_name": "post.py", "file_ext": "py", "file_size_in_byte": 494, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 7, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 7, "usage_type": "name"}, {"api_name": "tag.TagSerializer", "line_number": 8, "usage_type": "call"}, {"api_name": "models.post.Post", "line_number": 11, "usage_type": "name"}]} +{"seq_id": "609093464", "text": "from typing import List\n\ndef merge_sort(numbers: List[int]) -> List[int]:\n sorted_list = []\n # Base case\n if len(numbers) == 1:\n return numbers\n\n # Find the midpoint\n mid = len(numbers) // 2\n\n # Two recursive steps\n # Mergesort left\n left = merge_sort(numbers[:mid])\n # Mergesort right\n right = merge_sort(numbers[mid:])\n\n # Merge the two together\n\n # Loop through both lists with two markers\n left_marker = 0\n right_marker = 0\n while left_marker < len(left) and right_marker < len(right):\n if left[left_marker] < right[right_marker]:\n sorted_list.append(left[left_marker])\n left_marker += 1\n else:\n sorted_list.append(right[right_marker])\n right_marker += 1\n \n # Create a while loop to gather the rest of the values from either list\n while right_marker < len(right):\n sorted_list.append(right[right_marker])\n right_marker += 1\n \n while left_marker < len(left):\n sorted_list.append(left[left_marker])\n left_marker += 1\n\n return sorted_list\n\nprint(merge_sort([4, 1, 5, 2, 3]))\nprint(merge_sort([500, 20, 1, 2, 3, 4040]))\n\n# from typing import List\n# import random\n\n# def merge_sort(numbers: List[int]) -> List[int]:\n# # base case\n# if len(numbers) == 1:\n# return numbers\n\n# # find the midpoint\n# mid = len(numbers) // 2\n\n# # two recursive steps\n# # mergesort left\n# left = merge_sort(numbers[:mid])\n\n# # mergesort right\n# right = merge_sort(numbers[mid:])\n\n# # merge the two together\n\n# # loop through both lists with two markers\n# # if right value less than left value, add right value to sorted, increase right marker\n# # if left value less than right value, add left value to sorted, increase left marker\n\n# # create a while loop to gather the rest of the values from either list\n \n# # return the sorted list\n\n# print(merge_sort([1, 2, 3, 4, 5]))\n# print(merge_sort([5, 4, 3, 2, 1, 0]))\n# print(merge_sort([random.randrange(100) for _ in range(10)]))", "sub_path": "notes/merge_sort.py", "file_name": "merge_sort.py", "file_ext": "py", "file_size_in_byte": 2079, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "typing.List", "line_number": 3, "usage_type": "name"}]} +{"seq_id": "52717677", "text": "import torch\nimport torch.nn.functional as F\nimport numpy as np\n\ndef Loss(rpn_score, gt_rpn_score, rpn_loc, gt_rpn_loc, gt_roi_locs, gt_roi_labels, roi_cls_score, roi_cls_loc):\n # RPN classifier의 Loss\n rpn_cls_loss = F.cross_entropy(rpn_score, gt_rpn_score.long(), ignore_index=-1)\n\n # RPN에서 object score가 0보다 큰 anchor 찾음\n pos = gt_rpn_score > 0\n # rpn_loc와 차원을 맞춰서 매칭 시킴\n mask = pos.unsqueeze(1).expand_as(rpn_loc)\n\n # object score가 0보다 큰 anchor(RPN regressros layer 거친)들의 위치 정보\n mask_loc_preds = rpn_loc[mask].view(-1, 4)\n # object score가 0보다 큰 anchor(고정 크기)들의 위치 정보\n mask_loc_targets = gt_rpn_loc[mask].view(-1, 4)\n\n # 두 위치 정보의 차이를 구함(RPN의 regressors layer 학습)\n x = torch.abs(mask_loc_targets - mask_loc_preds)\n # 주어진 공식에 따라 loss값 구함(변형된 smooth L1 Loss)\n rpn_loc_loss = ((x < 1).float() * 0.5 * x**2) + ((x >= 1).float() * (x-0.5))\n\n # cls와 reg의 가중치를 비슷하게 해주는 하이퍼 파라미터\n rpn_lambda = 10.\n # mask 개수(평균 구함)\n N_reg = (gt_rpn_score > 0).float().sum()\n rpn_loc_loss = rpn_loc_loss.sum() / N_reg\n # 최종 RPN에서의 Loss\n rpn_loss = rpn_cls_loss + (rpn_lambda * rpn_loc_loss)\n\n gt_roi_loc = torch.from_numpy(gt_roi_locs)\n gt_roi_label = torch.from_numpy(np.float32(gt_roi_labels)).long()\n # Detector에서의 roi에 대한 loss\n roi_cls_loss = F.cross_entropy(roi_cls_score, gt_roi_label, ignore_index=-1)\n\n n_sample = roi_cls_loc.shape[0]\n # fc layer를 거치며 합쳐진 roi_cls_loc의 위치정보를 다시 roi당, object당 4개로 분배함\n roi_loc = roi_cls_loc.view(n_sample, -1, 4)\n # 각 roi마다의 예측된 label의 위치만 뽑음\n roi_loc = roi_loc[torch.arange(0, n_sample).long(), gt_roi_label]\n\n # RPN과 같은 방식으로 위치에 대한 Loss 구함\n x = torch.abs(roi_loc - gt_roi_loc)\n roi_loc_loss = ((x < 1).float() * 0.5 * x**2) + ((x >= 1).float() * (x-0.5))\n\n roi_lambda = 10.\n N_reg = (gt_rpn_score > 0).float().sum()\n roi_loc_loss = roi_loc_loss.sum() / n_sample\n # 최종 ROI(Detector) Loss\n roi_loss = roi_cls_loss + (roi_lambda * roi_loc_loss)\n\n # 최종 Loss = RPN + ROI\n total_loss = rpn_loss + roi_loss\n\n\n return total_loss", "sub_path": "Loss.py", "file_name": "Loss.py", "file_ext": "py", "file_size_in_byte": 2388, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "torch.nn.functional.cross_entropy", "line_number": 7, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 7, "usage_type": "name"}, {"api_name": "torch.abs", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn.functional.cross_entropy", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.arange", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "479043938", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.integrate import odeint\n\ns = 'http://byu.apmonitor.com'\na = 'mpc'\n\nfrom apm import *\n\n# define CSTR model\ndef cstr(x,t,u,Tf,Caf):\n # Inputs (3):\n # Temperature of cooling jacket (K)\n Tc = u\n # Tf = Feed Temperature (K)\n # Caf = Feed Concentration (mol/m^3)\n\n # States (2):\n # Concentration of A in CSTR (mol/m^3)\n Ca = x[0]\n # Temperature in CSTR (K)\n T = x[1]\n\n # Parameters:\n # Volumetric Flowrate (m^3/sec)\n q = 100\n # Volume of CSTR (m^3)\n V = 100\n # Density of A-B Mixture (kg/m^3)\n rho = 1000\n # Heat capacity of A-B Mixture (J/kg-K)\n Cp = 0.239\n # Heat of reaction for A->B (J/mol)\n mdelH = 5e4\n # E - Activation energy in the Arrhenius Equation (J/mol)\n # R - Universal Gas Constant = 8.31451 J/mol-K\n EoverR = 8750\n # Pre-exponential factor (1/sec)\n k0 = 7.2e10\n # U - Overall Heat Transfer Coefficient (W/m^2-K)\n # A - Area - this value is specific for the U calculation (m^2)\n UA = 5e4\n # reaction rate\n rA = k0*np.exp(-EoverR/T)*Ca\n\n # Calculate concentration derivative\n dCadt = q/V*(Caf - Ca) - rA\n # Calculate temperature derivative\n dTdt = q/V*(Tf - T) \\\n + mdelH/(rho*Cp)*rA \\\n + UA/V/rho/Cp*(Tc-T)\n \n # Return xdot:\n xdot = np.zeros(2)\n xdot[0] = dCadt\n xdot[1] = dTdt\n return xdot\n\ndef mpc_init():\n# fid = open('model.apm','w')\n# fid.write('Constants \\n')\n# fid.write(' Tc_ss = 300 \\n')\n# fid.write(' T_ss = 324.0 \\n')\n# fid.write('Parameters \\n')\n# fid.write(' Tc = Tc_ss , <350 , >250 \\n')\n# fid.write(' K = 1.3 \\n')\n# fid.write(' tau = 0.9 \\n')\n# fid.write('Variables \\n')\n# fid.write(' T = T_ss \\n')\n# fid.write('Equations \\n')\n# fid.write(' tau * $T = -(T-T_ss) + K * (Tc-Tc_ss) \\n')\n# fid.close()\n\n t = np.linspace(0,1,21)\n fid = open('data.csv','w')\n fid.write('time \\n')\n for ti in t:\n fid.write('{}\\n'.format(ti))\n fid.close()\n\n apm(s,a,'clear all')\n apm_load(s,a,'cstr.apm')\n csv_load(s,a,'data.csv')\n\n apm_info(s,a,'MV','Tc')\n apm_info(s,a,'CV','T')\n\n apm_option(s,a,'Tc.fstatus',0)\n apm_option(s,a,'Tc.status',1)\n\n apm_option(s,a,'Tc.upper',350)\n apm_option(s,a,'Tc.lower',250)\n \n apm_option(s,a,'T.fstatus',1)\n apm_option(s,a,'T.status',1)\n apm_option(s,a,'T.sphi',300.1)\n apm_option(s,a,'T.splo',299.9)\n apm_option(s,a,'T.tau',0.1)\n apm_option(s,a,'T.tr_init',1)\n\n apm_option(s,a,'nlc.imode',6)\n apm_option(s,a,'nlc.web_plot_freq',1)\n\n msg = 'Successful initialization'\n return msg\n\n# initialize application\nmpc_init()\n\ndef mpc(T):\n apm_meas(s,a,'T',T)\n apm(s,a,'solve')\n Tc = apm_tag(s,a,'Tc.newval')\n \n return Tc\n\n\n# Steady State Initial Conditions for the States\nCa_ss = 0.87725294608097\nT_ss = 324.475443431599\nx0 = np.empty(2)\nx0[0] = Ca_ss\nx0[1] = T_ss\n\n# Steady State Initial Condition\nu_ss = 300.0\n# Feed Temperature (K)\nTf = 350\n# Feed Concentration (mol/m^3)\nCaf = 1\n\n# Time Interval (min)\nt = np.arange(0,5,0.05)\n\n# Store results for plotting\nCa = np.ones(len(t)) * Ca_ss\nT = np.ones(len(t)) * T_ss\nu = np.ones(len(t)) * u_ss\n\n\n# storage for recording values\nop = np.ones(len(t))*u_ss # controller output\npv = np.zeros(len(t)) # process variable\nsp = np.zeros(len(t)) # set point\nsp[0:50] = 300.0\nsp[50:] = 320.0\n\n# Upper and Lower limits on OP\nop_hi = 350.0\nop_lo = 250.0\n\npv[0] = T_ss\n# loop through time steps \nfor i in range(len(t)-1):\n # Tc = mpc(T)\n op[i] = mpc(T[i])\n# if i==5:\n# apm_web(s,a)\n if i==49:\n # change set point\n apm_option(s,a,'T.sphi',320.1)\n apm_option(s,a,'T.splo',319.9)\n\n ts = [t[i],t[i+1]]\n u[i+1] = op[i]\n y = odeint(cstr,x0,ts,args=(u[i+1],Tf,Caf))\n Ca[i+1] = y[-1][0]\n T[i+1] = y[-1][1]\n x0[0] = Ca[i+1]\n x0[1] = T[i+1]\n pv[i+1] = T[i+1]\n# print(i)\n\nop[len(t)-1] = op[len(t)-2]\n\n# Construct results and save data file\n# Column 1 = time\n# Column 2 = cooling temperature\n# Column 3 = reactor temperature\ndata = np.vstack((t,u,T)) # vertical stack\ndata = data.T # transpose data\nnp.savetxt('data_doublet.txt',data,delimiter=',')\n \n# Plot the results\nplt.figure(1)\nplt.subplot(2,1,1)\nplt.plot(t,u,'b--',linewidth=3)\nplt.ylabel('Cooling T (K)')\n#plt.legend(['Jacket Temperature'],loc=1)\n\nplt.subplot(2,1,2)\nplt.plot(t,Ca,'g-',linewidth=3)\nplt.ylabel('Ca (mol/L)')\n#plt.legend(['Reactor Concentration'],loc=1)\nplt.tight_layout()\n\nplt.figure(2)\nplt.subplot(2,1,1)\nplt.plot(t,T,'k:',linewidth=3,label='Reactor Temperature')\nplt.plot(t,sp,'r--',linewidth=2,label='Set Point')\nplt.ylabel('T (K)')\nplt.xlabel('Time (min)')\n#plt.legend()\n\nplt.subplot(2,1,2)\nplt.plot(t,op,'r--',linewidth=3,label='Controller Output (OP)')\n#plt.legend()\nplt.ylabel('Output')\nplt.tight_layout()\n\nplt.show()\n", "sub_path": "Homework/CSTR control/MPC.py", "file_name": "MPC.py", "file_ext": "py", "file_size_in_byte": 4880, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "numpy.exp", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 144, "usage_type": "call"}, {"api_name": "scipy.integrate.odeint", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 185, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 186, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 187, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 187, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 188, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 191, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 191, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 192, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 192, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 193, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 193, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 195, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 198, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 198, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 199, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 199, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 200, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 201, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 201, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 202, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 202, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 205, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 205, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 206, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 208, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 208, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 209, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 209, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 211, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 211, "usage_type": "name"}]} +{"seq_id": "368325850", "text": "import os\nimport pickle\nimport random\nimport json\nimport numpy as np\nimport music21 as m21\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras.utils import to_categorical\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import LSTM, Dense, Dropout, Activation\nfrom tensorflow.keras.callbacks import ModelCheckpoint\n\nimport utils_multi as utils\n\n# Fix for Manjaro, CUDA 11 (pacman -S python-tensorflow-cuda)\n# Fixes: Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR\nfrom tensorflow.compat.v1 import ConfigProto\nfrom tensorflow.compat.v1 import InteractiveSession\n\nconfig = ConfigProto()\nconfig.gpu_options.allow_growth = True\nsession = InteractiveSession(config=config)\n# xif. If you don't use Manjaro, comment it or something.\n\ndef train_for_track(notes, offsets, durations):\n for off, dur in zip(offsets, durations):\n for i in range(0, len(off), 1):\n dur[i] = off[i] + '|' + str(dur[i])\n\n pitches = utils.get_unique_pitches(notes)\n note_to_int = dict((note, number) for number, note in enumerate(pitches))\n\n unique_durations = utils.get_unique_pitches(durations)\n duration_to_int = dict((duration, number) for number, duration in enumerate(unique_durations))\n\n unique_notes_count = len(note_to_int.keys())\n unique_durations_count = len(duration_to_int.keys())\n\n sequence_length = 20\n\n network_input = []\n network_output = []\n dur_network_input = []\n dur_network_output = []\n\n for song in notes:\n for i in range(0, len(song) - sequence_length, 1):\n sequence_in = song[i : i + sequence_length] # s_l notes starting from i offset\n sequence_out = song[i + sequence_length] # current note + s_l\n # map strings to numbers\n network_input.append([note_to_int[char] for char in sequence_in])\n network_output.append(note_to_int[sequence_out])\n\n for song in durations:\n for i in range(0, len(song) - sequence_length, 1):\n sequence_in = song[i : i + sequence_length] # s_l notes starting from i offset\n sequence_out = song[i + sequence_length] # current note + s_l\n # map strings to numbers\n dur_network_input.append([duration_to_int[char] for char in sequence_in])\n dur_network_output.append(duration_to_int[sequence_out])\n\n patterns_count = len(network_input)\n dur_patterns_count = len(dur_network_input)\n\n # reshape the input into a format compatible with LSTM layers\n network_input = np.reshape(network_input, (patterns_count, sequence_length, 1))\n # normalize input\n network_input = network_input / float(unique_notes_count) # normalize input\n # network_output = to_categorical(network_output) # convert the vector to a binary matrix\n\n dur_network_input = np.reshape(dur_network_input, (dur_patterns_count, sequence_length, 1))\n dur_network_input = dur_network_input / float(unique_durations_count)\n #dur_network_output = to_categorical(dur_network_output)\n\n model, callbacks = utils.create_model(\n (network_input.shape[1], network_input.shape[2]),\n unique_notes_count,\n 'output/weights.hdf5',\n loss_dest=0.5\n )\n dur_model, dur_callbacks = utils.create_model(\n (dur_network_input.shape[1], dur_network_input.shape[2]),\n unique_durations_count,\n 'output/weights_dur.hdf5',\n loss_dest=0.5\n )\n\n class DataGenerator(tf.keras.utils.Sequence):\n def __init__(self, x_col, y_col, seq, outputs, batch_size=32):\n self.batch_size = batch_size\n self.x_col = x_col\n self.y_col = y_col\n self.seq = seq\n self.outputs = outputs\n\n def __data_generation(self, index):\n 'Generates data containing batch_size samples' # X : (n_samples, *dim, n_channels)\n i = index*self.batch_size\n # Initialization\n X = np.empty((self.batch_size*self.seq)).reshape(self.batch_size,self.seq,1)\n y = np.empty((self.batch_size), dtype=int)\n # Generate data\n X[:] = self.x_col[i : i+self.batch_size]\n y[:] = self.y_col[i : i+self.batch_size]\n return X, keras.utils.to_categorical(y, num_classes=self.outputs)\n\n def __getitem__(self, index):\n 'Generate one batch of data'\n X, y = self.__data_generation(index)\n return X, y\n\n def __len__(self):\n 'Denotes the number of batches per epoch'\n return int(np.floor(len(self.y_col)/self.batch_size))\n\n\n my_generator = DataGenerator(x_col=network_input, y_col=network_output, seq=sequence_length, outputs=unique_notes_count, batch_size=64)\n dur_generator = DataGenerator(x_col=dur_network_input, y_col=dur_network_output, seq=sequence_length, outputs=unique_durations_count, batch_size=64)\n\n # start training\n model.fit(my_generator, epochs=100, callbacks=callbacks, batch_size=64)\n dur_model.fit(dur_generator, epochs=100, callbacks=dur_callbacks, batch_size=64)\n\n return model, dur_model, network_input, dur_network_input\n\ndef load_data(note_name, dur_name):\n # Loading notes data\n with open('model/'+note_name+'.p','rb') as fp:\n int_to_note = pickle.load(fp)\n with open('model/'+dur_name+'.p','rb') as fp:\n int_to_duration = pickle.load(fp)\n model = load_model('model/'+note_name+'.hdf5')\n model_dur = load_model('model/'+dur_name+'.hdf5')\n # pattern = []\n # pattern_dur = []\n # for i in range(0,20):\n # pattern.append(random.randint(0,max(int_to_note.keys())))\n # pattern_dur.append(random.randint(0,max(int_to_duration.keys())))\n return int_to_note,model,int_to_duration,model_dur\n\ndef generate_song(model, network_input, int_to_note, dur_model, dur_input, int_to_duration, output, length=500):\n # training finished, generate output song\n # convert from ints back to class names\n #pitches = utils.get_unique_pitches(track)\n #int_to_note = dict((number, note) for number, note in enumerate(pitches)) # [key => value] = [int => string]\n #unique_durations = utils.get_unique_pitches(durs)\n #int_to_duration = dict((number, duration) for number, duration in enumerate(unique_durations))\n # print(int_to_note)\n # print(int_to_duration)\n prediction_output, dur_prediction_output = utils.construct_song(model, network_input, int_to_note, dur_model, dur_input, int_to_duration, length=length) # predict notes in the new song\n print('Generated notes\\n', prediction_output)\n print('Generated durations\\n', dur_prediction_output)\n utils.generate_midi(prediction_output, dur_prediction_output, output) # convert output to a .mid file\n\ndef generate_for_server(name, dur_name, key, instrument):\n length = 500\n int_to_note, model = load_data(name)\n int_to_duration, dur_model = load_data(dur_name)\n network_input = []\n dur_network_input = []\n for j in range(0, 20):\n network_input.append(random.randint(0,max(int_to_note.keys())))\n dur_network_input.append(random.randint(0,max(int_to_duration.keys())))\n\n prediction_output, dur_prediction_output = utils.construct_song(model, network_input, int_to_note, dur_model, dur_network_input, int_to_duration, length=length) # predict notes in the new song\n return utils.generate_json(prediction_output, dur_prediction_output)\n\ndef main():\n midis_folder = './midis/VGM/'\n midi_files = map(lambda f: midis_folder + f, os.listdir(midis_folder))\n #midi_files = list(filter(lambda f: 'ashover_simple_chords' in f, midi_files)) # train only on chord files\n print('Converting midis...')\n notes = []\n offsets = []\n durations = []\n i=0\n for file in midi_files:\n if i==5:\n break\n try:\n _notes, _offsets, _durations = utils.convert_midi(file, target_key='G major')\n except:\n os.remove(file)\n \n notes.append(_notes)\n offsets.append(_offsets)\n durations.append(_durations)\n i+=1\n\n with open('output/notes.json', 'w') as fp:\n json.dump(notes, fp)\n with open('output/offsets.json', 'w') as fp:\n json.dump(offsets, fp)\n for item in durations:\n for i in range(0, len(item), 1):\n item[i] = str(item[i])\n with open('output/durations.json', 'w') as fp:\n json.dump(durations, fp)\n # with open('output/notes.json', 'r') as fp:\n # notes = json.load(fp)\n # with open('output/offsets.json', 'r') as fp:\n # offsets = json.load(fp)\n # with open('output/durations.json', 'r') as fp:\n # durations = json.load(fp)\n\n print(durations)\n model, dur_model, network_input, dur_network_input = train_for_track(notes, offsets, durations) # TO DO - add support for durations\n print('Done')\n \n pitches = utils.get_unique_pitches(notes)\n int_to_note = dict((number, note) for number, note in enumerate(pitches))\n unique_durations = utils.get_unique_pitches(durations)\n int_to_duration = dict((number, duration) for number, duration in enumerate(unique_durations))\n with open('output/int_to_note.p','wb') as fp:\n pickle.dump(int_to_note,fp,protocol=pickle.HIGHEST_PROTOCOL)\n pattern = []\n for i in range(0,20):\n pattern.append(random.randint(0,max(int_to_note.keys())))\n \n with open('output/int_to_duration.p','wb') as fp:\n pickle.dump(int_to_duration,fp,protocol=pickle.HIGHEST_PROTOCOL)\n pattern_dur = []\n for i in range(0,20):\n pattern_dur.append(random.randint(0, max(int_to_duration.keys())))\n\n #i = 0\n while True:\t\n try:\n #generate_song(model, network_input, notes, dur_model, dur_network_input, durations, 'output'+str(i)+'.mid')\n generate_song(model, pattern, int_to_note, dur_model, pattern_dur, int_to_duration, 'output'+str(i)+'.mid')\n except Exception as e:\n print(e)\n i+=1\n print(\"Continue?\")\n if input()!='y':\n break\n\nif __name__ == '__main__':\n main()", "sub_path": "main_multi.py", "file_name": "main_multi.py", "file_ext": "py", "file_size_in_byte": 9432, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "tensorflow.compat.v1.ConfigProto", "line_number": 21, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.InteractiveSession", "line_number": 23, "usage_type": "call"}, {"api_name": "utils_multi.get_unique_pitches", "line_number": 31, "usage_type": "call"}, {"api_name": "utils_multi.get_unique_pitches", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 72, "usage_type": "call"}, {"api_name": "utils_multi.create_model", "line_number": 76, "usage_type": "call"}, {"api_name": "utils_multi.create_model", "line_number": 82, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 89, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 102, "usage_type": "call"}, {"api_name": "tensorflow.keras.utils.to_categorical", "line_number": 106, "usage_type": "call"}, {"api_name": "tensorflow.keras.utils", "line_number": 106, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 106, "usage_type": "name"}, {"api_name": "numpy.floor", "line_number": 115, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 130, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 132, "usage_type": "call"}, {"api_name": "utils_multi.construct_song", "line_number": 151, "usage_type": "call"}, {"api_name": "utils_multi.generate_midi", "line_number": 154, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 163, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 164, "usage_type": "call"}, {"api_name": "utils_multi.construct_song", "line_number": 166, "usage_type": "call"}, {"api_name": "utils_multi.generate_json", "line_number": 167, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 171, "usage_type": "call"}, {"api_name": "utils_multi.convert_midi", "line_number": 182, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 184, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 192, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 194, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 199, "usage_type": "call"}, {"api_name": "utils_multi.get_unique_pitches", "line_number": 211, "usage_type": "call"}, {"api_name": "utils_multi.get_unique_pitches", "line_number": 213, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 216, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 216, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 219, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 222, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 222, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 225, "usage_type": "call"}]} +{"seq_id": "367287140", "text": "import os\nimport subprocess\nimport sys\nfrom pathlib import Path\n\nimport typer\nfrom typer.testing import CliRunner\n\nfrom first_steps import tutorial001 as mod\n\nrunner = CliRunner()\napp = typer.Typer()\napp.command()(mod.main)\n\n\ndef test_show_completion():\n result = subprocess.run(\n [\n \"bash\",\n \"-c\",\n f\"{sys.executable} -m coverage run {mod.__file__} --show-completion\",\n ],\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n encoding=\"utf-8\",\n env={**os.environ, \"SHELL\": \"/bin/bash\"},\n )\n assert \"_TUTORIAL001.PY_COMPLETE=complete-bash\" in result.stdout\n\n\ndef test_install_completion():\n bash_completion_path: Path = Path.home() / \".bash_completion\"\n text = \"\"\n if bash_completion_path.is_file():\n text = bash_completion_path.read_text()\n result = subprocess.run(\n [\n \"bash\",\n \"-c\",\n f\"{sys.executable} -m coverage run {mod.__file__} --install-completion\",\n ],\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n encoding=\"utf-8\",\n env={**os.environ, \"SHELL\": \"/bin/bash\"},\n )\n new_text = bash_completion_path.read_text()\n bash_completion_path.write_text(text)\n assert \"_TUTORIAL001.PY_COMPLETE=complete-bash\" in new_text\n assert \"completion installed in\" in result.stdout\n assert \"Completion will take effect once you restart the terminal.\" in result.stdout\n", "sub_path": "tests/test_completion.py", "file_name": "test_completion.py", "file_ext": "py", "file_size_in_byte": 1456, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "typer.testing.CliRunner", "line_number": 11, "usage_type": "call"}, {"api_name": "typer.Typer", "line_number": 12, "usage_type": "call"}, {"api_name": "first_steps.tutorial001.main", "line_number": 13, "usage_type": "attribute"}, {"api_name": "first_steps.tutorial001", "line_number": 13, "usage_type": "name"}, {"api_name": "subprocess.run", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.executable", "line_number": 21, "usage_type": "attribute"}, {"api_name": "first_steps.tutorial001.__file__", "line_number": 21, "usage_type": "attribute"}, {"api_name": "first_steps.tutorial001", "line_number": 21, "usage_type": "name"}, {"api_name": "subprocess.PIPE", "line_number": 23, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 32, "usage_type": "name"}, {"api_name": "pathlib.Path.home", "line_number": 32, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 36, "usage_type": "call"}, {"api_name": "sys.executable", "line_number": 40, "usage_type": "attribute"}, {"api_name": "first_steps.tutorial001.__file__", "line_number": 40, "usage_type": "attribute"}, {"api_name": "first_steps.tutorial001", "line_number": 40, "usage_type": "name"}, {"api_name": "subprocess.PIPE", "line_number": 42, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 45, "usage_type": "attribute"}]} +{"seq_id": "342729822", "text": "import json\r\nimport os\r\nimport shutil\r\nimport urllib2\r\n\r\n\r\n__author__ = 'Therms'\r\n__tmdb_apikey__ = '6d96a9efb4752ed0d126d94e12e52036'\r\n\r\ntry:\r\n import imdb\r\n __imdb__ = True\r\nexcept:\r\n __imdb__ = False\r\n\r\nclass XmgException(Exception):\r\n pass\r\n\r\nclass ApiError(XmgException):\r\n pass\r\n\r\nclass IdError(XmgException):\r\n pass\r\n\r\nclass NfoError(XmgException):\r\n pass\r\n\r\nclass metagen():\r\n def __init__(self, imdbid, imdbpy = None):\r\n ''' metagen is used to download metadata for a movie or tv show and then create\r\n the necessary files for the media to be imported into XBMC.\r\n\r\n Arguments\r\n ===========\r\n fanart/poster_height/width_min: Sets lowest acceptable image resolution. 0 means\r\n disregard. If no fanart available at specified resolution or greater, then\r\n we disregard this setting, and download highest resolution that is available.\r\n\r\n name*: In the case of a movie, ideally this should be the full movie name\r\n followed by the year of the movie in parentheses. e.g. \"The Matrix (1999)\".\r\n If this is specific enough to generate only one search result then we'll\r\n continue. Otherwise, we'll raise IdError.\r\n\r\n Because of the imprecise nature of this method of id, only use it if you\r\n don't have the imdb_id or tmdb_id\r\n\r\n imdb_id: Use this argument if you know the imdb id of the show/movie. If\r\n this is used, the tmdb_id argument is ignored.\r\n\r\n tmdb_id*: Use this argument if you know the tmdb id of the movie. If this\r\n is used, the imdb_id argument is ignored.\r\n\r\n imdbpy: When xmg is used as a library, imdbpy may not be installed\r\n system-wide, but included with your application. If this is the case, pass\r\n your instance of imdb.IMDb() to metagen, so we can use it.\r\n\r\n * These arguments are not yet supported.\r\n '''\r\n\r\n #first we'll evaluate our arguments for error conditions\r\n if not imdbpy and not __imdb__:\r\n raise ApiError(\"Can't import imdb and wasn't provided with an imdbpy instance\")\r\n\r\n if imdbid[:2].lower() == 'tt':\r\n self.imdbid = imdbid[2:]\r\n\r\n if imdbpy:\r\n self.imdbpy = imdbpy\r\n else:\r\n self.imdbpy = imdb.IMDb('http', useModule='beautifulsoup')\r\n\r\n\r\n self.imdbpy_movie = self._get_movie()\r\n self.nfo_string = self._nfo_gen()\r\n self.tmdb_data = self._get_tmdb_imdb()\r\n\r\n #TODO: Search by movie name\r\n #TODO: Search by tmdb_id\r\n #TODO: Search by movie hash\r\n\r\n\r\n def _get_movie(self):\r\n try:\r\n imdbpy_movie = self.imdbpy.get_movie(self.imdbid)\r\n except imdb._exceptions.IMDbParserError:\r\n raise IdError(\"%s is not a valid imdb id\" % self.imdbid)\r\n\r\n if len(imdbpy_movie.keys()) == 0:\r\n raise IdError(\"%s is not a valid imdb id\" % self.imdbid)\r\n\r\n return imdbpy_movie\r\n\r\n def _nfo_gen(self):\r\n ''' Get the imdb url for the specified movie object\r\n '''\r\n nfo_string = self.imdbpy.get_imdbURL(self.imdbpy_movie)\r\n #TODO: Generate full nfo XML\r\n return nfo_string\r\n\r\n def write_nfo(self, path):\r\n try:\r\n f = open(path, 'w')\r\n f.write(self.nfo_string)\r\n f.close()\r\n except:\r\n raise NfoError(\"Couldn't write nfo\")\r\n\r\n def _get_fanart(self, min_height, min_width):\r\n ''' Fetches the fanart for the specified imdb_id and saves it to dir.\r\n Arguments\r\n\r\n min_height/width: Sets lowest acceptable resolution fanart. 0 means\r\n disregard. If no fanart available at specified resolution or greater, then\r\n we disregard.\r\n '''\r\n images = [image['image'] for image in self.tmdb_data['backdrops'] if image['image'].get('size') == 'original']\r\n if len(images) == 0:\r\n return\r\n\r\n return self._get_image(images, min_height, min_width)\r\n\r\n def get_fanart_url(self, min_height, min_width):\r\n return self._get_fanart(min_height, min_width)['url']\r\n\r\n def write_fanart(self, filename_root, path, min_height, min_width):\r\n fanart_url = self.get_fanart_url(min_height, min_width)\r\n #fetch and write to disk\r\n dest = os.path.join(path, filename_root)\r\n try:\r\n f = open(dest, 'wb')\r\n except:\r\n raise IOError(\"Can't open for writing: %s\" % dest)\r\n\r\n response = urllib2.urlopen(fanart_url)\r\n f.write(response.read())\r\n f.close()\r\n\r\n return True\r\n\r\n def _get_poster(self, min_height, min_width):\r\n ''' Fetches the poster for the specified imdb_id and saves it to dir.\r\n Arguments\r\n\r\n min_height/width: Sets lowest acceptable resolution poster. 0 means\r\n disregard. If no poster available at specified resolution or greater, then\r\n we disregard.\r\n '''\r\n images = [image['image'] for image in self.tmdb_data['posters'] if image['image'].get('size') == 'original']\r\n if len(images) == 0:\r\n return\r\n\r\n return self._get_image(images, min_height, min_width)\r\n\r\n def get_poster_url(self, min_height, min_width):\r\n return self._get_poster(min_height, min_width)['url']\r\n\r\n def write_poster(self, filename_root, path, min_height, min_width):\r\n poster_url = self.get_poster_url(min_height, min_width)\r\n dest = os.path.join(path, filename_root)\r\n\r\n try:\r\n f = open(dest, 'wb')\r\n except:\r\n raise IOError(\"Can't open for writing: %s\" % dest)\r\n\r\n response = urllib2.urlopen(poster_url)\r\n f.write(response.read())\r\n f.close()\r\n\r\n return True\r\n\r\n def _get_tmdb_imdb(self):\r\n url = \"http://api.themoviedb.org/2.1/Movie.imdbLookup/en/json/%s/%s\" % (__tmdb_apikey__, \"tt\" + self.imdbid)\r\n response = urllib2.urlopen(url)\r\n tmdb_data = json.loads(response.read())[0]\r\n return tmdb_data\r\n\r\n def _get_image(self, image_list, min_height, min_width):\r\n #Select image\r\n images = []\r\n for image in image_list:\r\n if not min_height or min_width:\r\n images.append(image)\r\n break\r\n elif min_height and not min_width:\r\n if image['height'] >= min_height:\r\n images.append(image)\r\n break\r\n elif min_width and not min_height:\r\n if image['width'] >= min_width:\r\n images.append(image)\r\n break\r\n elif min_width and min_height:\r\n if image['width'] >= min_width and image['height'] >= min_height:\r\n images.append(image)\r\n break\r\n\r\n #No image meets our resolution requirements, so disregard those requirements\r\n if len(images) == 0 and min_height or min_width:\r\n images.append(image_list[0])\r\n\r\n return images[0]\r\n\r\n\r\nif __name__ == \"__main__\":\r\n import sys\r\n try:\r\n id = sys.argv[1]\r\n except:\r\n id = 'tt0111161'\r\n\r\n x = metagen(id)\r\n x.write_nfo(\".\\movie.nfo\")\r\n x.write_fanart(\"fanart\", \".\", 0, 0)\r\n x.write_poster(\"movie\", \".\", 0, 0)\r\n", "sub_path": "library/xmg/xmg.py", "file_name": "xmg.py", "file_ext": "py", "file_size_in_byte": 7286, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "imdb.IMDb", "line_number": 70, "usage_type": "call"}, {"api_name": "imdb._exceptions", "line_number": 85, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path", "line_number": 128, "usage_type": "attribute"}, {"api_name": "urllib2.urlopen", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 159, "usage_type": "call"}, {"api_name": "os.path", "line_number": 159, "usage_type": "attribute"}, {"api_name": "urllib2.urlopen", "line_number": 166, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 174, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 175, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 208, "usage_type": "attribute"}]} +{"seq_id": "597761271", "text": "#usage: Evals_TESTset.py \n\nimport pygpu\nimport keras\nimport matplotlib\nimport sys\nimport os\nimport numpy as np\nfrom keras.initializers import glorot_uniform\nimport theano\nfrom Bio import SeqIO\nimport keras.backend as K\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D, Activation\nfrom keras.regularizers import l2\nfrom keras.optimizers import SGD\nfrom keras.models import load_model\nfrom keras import metrics\nfrom sklearn.metrics import auc, roc_curve, precision_recall_curve, confusion_matrix\nfrom sklearn.metrics import roc_auc_score\nfrom keras.callbacks import Callback\nfrom keras.callbacks import ModelCheckpoint\nfrom sklearn.metrics import auc, roc_curve, precision_recall_curve, confusion_matrix\nimport sys\n\ndef onehot_seq(seq):\n letter_to_index = {'A':0, 'a':0,\n 'C':1, 'c':1,\n 'G':2, 'g':2,\n 'T':3, 't':3}\n to_return = np.zeros((len(seq),4), dtype='int8')\n for idx,letter in enumerate(seq):\n if letter not in ['N','n']:\n to_return[idx,letter_to_index[letter]] = 1\n return to_return\n\n\ndef encode_sequence(fasta_pos, fasta_neg, shuffleOff = True):\n x_pos = np.array([onehot_seq(seq) for seq in SeqIO.parse(fasta_pos, \"fasta\") ] +\n [onehot_seq(seq.reverse_complement()) for seq in SeqIO.parse(fasta_pos, \"fasta\") ])\n x_neg = np.array([onehot_seq(seq) for seq in SeqIO.parse(fasta_neg, \"fasta\") ] +\n [onehot_seq(seq.reverse_complement()) for seq in SeqIO.parse(fasta_neg, \"fasta\") ])\n # concatenate positives and negatives\n x = np.expand_dims(np.concatenate((x_pos, x_neg)), axis=3)\n y = np.concatenate((np.ones(len(x_pos)),np.zeros(len(x_neg))))\n # need to shuffle order of training set for validation splitting last\n if not shuffleOff:\n indices = np.arange(y.shape[0])\n np.random.shuffle(indices)\n x = x[indices,:]\n y = y[indices]\n #\n return x, y\n\nmodel = keras.models.load_model(sys.argv[1])\n\nif os.path.exists(\"Eval1_mm10_TEST.fa\") == False:\n sys.exit('ERROR: No file Eval1_mm10_TEST.fa')\nif os.path.exists(\"Eval2_hg38_TEST.fa\") == False:\n sys.exit('ERROR: No file Eval2_hg38_TEST.fa')\nif os.path.exists(\"Eval3_hg38_TEST.fa\") == False:\n sys.exit('ERROR: No file Eval3_hg38_TEST.fa')\nif os.path.exists(\"Eval4_mm10_TEST.fa\") == False:\n sys.exit('ERROR: No file Eval4_mm10_TEST.fa')\nif os.path.exists(\"Eval5_mm10_TEST.fa\") == False:\n sys.exit('ERROR: No file Eval5_mm10_TEST.fa')\nif os.path.exists(\"Eval6_mm10_TEST.fa\") == False:\n sys.exit('ERROR: No file Eval6_mm10_TEST.fa')\nif os.path.exists(\"Eval7_mm10_TEST.fa\") == False:\n sys.exit('ERROR: No file Eval7_mm10_TEST.fa')\nif os.path.exists(\"Eval8_mm10_TEST.fa\") == False:\n sys.exit('ERROR: No file Eval8_mm10_TEST.fa')\nif os.path.exists(\"Eval9_mm10_TEST.fa\") == False:\n sys.exit('ERROR: No file Eval9_mm10_TEST.fa')\nif os.path.exists(\"Eval10_mm10_TEST.fa\") == False:\n sys.exit('ERROR: No file Eval10_mm10_TEST.fa')\n\n\n\n#multispecies evaluations (all restricted to TEST set for figure generation)\n\n#Fig 1 Bar 1: + mouse&human positives, - mouse&human negatives\n(x_eval12, y_eval12) = encode_sequence(\"/projects/pfenninggroup/mouseCxStr/NeuronSubtypeATAC/Zoonomia_CNN/multispecies_PV/FinalModelData/combined_pos_TEST.fa\",\"/projects/pfenninggroup/mouseCxStr/NeuronSubtypeATAC/Zoonomia_CNN/multispecies_PV/FinalModelData/combined_neg_TEST.fa\",shuffleOff=True)\ny_pred = model.predict(x_eval12).ravel()\nfpr_keras, tpr_keras, thresholds_keras = roc_curve(y_eval12, y_pred)\nauc_12v = auc(fpr_keras, tpr_keras)\nprecision, recall, thresholds = precision_recall_curve(y_eval12, y_pred)\nauprc_12v = auc(recall, precision)\ny_predclass = np.rint(y_pred)\ntn, fp, fn, tp = confusion_matrix(y_eval12, y_predclass).ravel()\nacc_1v = tp/(tp+fn)\nacc_2v = tn/(tn+fp)\n\n#Fig 1 Bar 2: + mouse specific enhancers, - mouse non-enhancers whose human orthologs are enhancers\n(x_eval14, y_eval14) = encode_sequence(\"Eval1_mm10_TEST.fa\",\"Eval4_mm10_TEST.fa\",shuffleOff=True)\ny_pred = model.predict(x_eval14).ravel()\nfpr_keras, tpr_keras, thresholds_keras = roc_curve(y_eval14, y_pred)\nauc_14v = auc(fpr_keras, tpr_keras)\nprecision, recall, thresholds = precision_recall_curve(y_eval14, y_pred)\nauprc_14v = auc(recall, precision)\ny_predclass = np.rint(y_pred)\ntn, fp, fn, tp = confusion_matrix(y_eval14, y_predclass).ravel()\nacc_1v = tp/(tp+fn)\nacc_1v = tn/(tn+fp)\n# #pos > #neg, get aunpv\n(x_eval41, y_eval41) = encode_sequence(\"Eval4_mm10_TEST.fa\",\"Eval1_mm10_TEST.fa\",shuffleOff=True)\ny_pred41 = model.predict(x_eval41).ravel()\ny_pred41 = y_pred41 * -1\nprecision, recall, thresholds = precision_recall_curve(y_eval41, y_pred41)\naunpv41 = auc(recall, precision)\n\n\n#Fig 1 Bar 3: + human specific enchancers, - human non-enhancers whose mouse orthologs are enhancers\n(x_eval32, y_eval32) = encode_sequence(\"Eval3_hg38_TEST.fa\",\"Eval2_hg38_TEST.fa\",shuffleOff=True)\ny_pred = model.predict(x_eval32).ravel()\nfpr_keras, tpr_keras, thresholds_keras = roc_curve(y_eval32, y_pred)\nauc_32v = auc(fpr_keras, tpr_keras)\nprecision, recall, thresholds = precision_recall_curve(y_eval32, y_pred)\nauprc_32v = auc(recall, precision)\ny_predclass = np.rint(y_pred)\ntn, fp, fn, tp = confusion_matrix(y_eval32, y_predclass).ravel()\nacc_3v = tp/(tp+fn)\nacc_2v = tn/(tn+fp)\n\n\n#Fig 1 Bar 6: + shared liver/PV enhancers, - liver enhancers which are not PV enhancers\n(x_eval910, y_eval910) = encode_sequence(\"Eval9_mm10_TEST.fa\",\"Eval10_mm10_TEST.fa\",shuffleOff=True)\ny_pred = model.predict(x_eval910).ravel()\nfpr_keras, tpr_keras, thresholds_keras = roc_curve(y_eval910, y_pred)\nauc_910v = auc(fpr_keras, tpr_keras)\nprecision, recall, thresholds = precision_recall_curve(y_eval910, y_pred)\nauprc_910v = auc(recall, precision)\ny_predclass = np.rint(y_pred)\ntn, fp, fn, tp = confusion_matrix(y_eval910, y_predclass).ravel()\nacc_9v = tp/(tp+fn)\nacc_10v = tn/(tn+fp)\n\n\n#Fig 1 Bar 7: + shared PV & excitatory neuron enhancers, - excitatory neuron enhancers which are note PV enhancers\n(x_eval67, y_eval67) = encode_sequence(\"Eval6_exc_MoHu_TEST.fa\",\"Eval7exc_MoHu_TEST.fa\",shuffleOff=True)\ny_pred = model.predict(x_eval67).ravel()\nfpr_keras, tpr_keras, thresholds_keras = roc_curve(y_eval67, y_pred)\nauc_67v = auc(fpr_keras, tpr_keras)\nprecision, recall, thresholds = precision_recall_curve(y_eval67, y_pred)\nauprc_67v = auc(recall, precision)\ny_predclass = np.rint(y_pred)\ntn, fp, fn, tp = confusion_matrix(y_eval67, y_predclass).ravel()\nacc_6v = tp/(tp+fn)\nacc_7v = tn/(tn+fp)\n\n\nwith open(str(sys.argv[1]) + 'evaluations1-10_TEST.txt', 'w') as f:\n f.write(str(auc_12v) + \"\\t\" + str(auc_14v) + \"\\t\" + str(auc_32v) + \"\\t\" + str(auc_910v) + \"\\t\" + str(auc_67v))\n f.write(str(auprc_12v) + \"\\t\" + str(aunpv41) + \"\\t\" + str(auprc_32v) + \"\\t\" + str(auprc_910v) + \"\\t\" + str(auprc_67v))\n", "sub_path": "evaluationScriptsPVModels/PVmodelEvaluations.py", "file_name": "PVmodelEvaluations.py", "file_ext": "py", "file_size_in_byte": 6843, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.zeros", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 39, "usage_type": "call"}, {"api_name": "Bio.SeqIO.parse", "line_number": 39, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 39, "usage_type": "name"}, {"api_name": "Bio.SeqIO.parse", "line_number": 40, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 40, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 41, "usage_type": "call"}, {"api_name": "Bio.SeqIO.parse", "line_number": 41, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 41, "usage_type": "name"}, {"api_name": "Bio.SeqIO.parse", "line_number": 42, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 42, "usage_type": "name"}, {"api_name": "numpy.expand_dims", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 49, "usage_type": "attribute"}, {"api_name": "keras.models.load_model", "line_number": 55, "usage_type": "call"}, {"api_name": "keras.models", "line_number": 55, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path", "line_number": 73, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 76, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 85, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 86, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_recall_curve", "line_number": 87, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.rint", "line_number": 89, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 90, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 97, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 98, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_recall_curve", "line_number": 99, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.rint", "line_number": 101, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 102, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_recall_curve", "line_number": 109, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 110, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 116, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 117, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_recall_curve", "line_number": 118, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.rint", "line_number": 120, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 121, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 129, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 130, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_recall_curve", "line_number": 131, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.rint", "line_number": 133, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 134, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 142, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 143, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_recall_curve", "line_number": 144, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.rint", "line_number": 146, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 147, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 152, "usage_type": "attribute"}]} +{"seq_id": "88478946", "text": "#!/usr/bin/env python\nfrom __future__ import absolute_import, print_function\nfrom boto3.session import Session\nfrom functools import wraps\nfrom getopt import getopt, GetoptError\nimport re\nfrom string import ascii_lowercase\nfrom sys import argv, exit as sys_exit, stderr, stdin, stdout\nfrom .vset import VolumeSet\n\nentry_points = {}\n\ndef entry_point(f):\n entry_points[f.__name__] = f\n return f\n\n@entry_point\ndef attach_volume_set(args):\n \"\"\"\n The main entry point for the vset-attach utility.\n \"\"\"\n session_kw = {}\n instance_id = None\n name = None\n\n usage_errors = False\n\n try:\n opts, args = getopt(args, \"hi:n:p:r:\",\n [\"help\", \"instance-id=\", \"name=\", \"profile=\",\n \"region=\"])\n except GetoptError as e:\n print(str(e), file=stderr)\n attach_usage()\n return 1\n\n for opt, val in opts:\n if opt in (\"-h\", \"--help\",):\n attach_usage(stdout)\n return 0\n elif opt in (\"-i\", \"--instance-id\",):\n instance_id = val\n elif opt in (\"-n\", \"--name\",):\n name = val\n elif opt in (\"-p\", \"--profile\",):\n session_kw[\"profile_name\"] = val\n elif opt in (\"-r\", \"--region\",):\n session_kw[\"region_name\"] = val\n\n # We don't accept arguments, only options.\n if len(args) > 0:\n print(\"Unknown argument %r\" % args[0], file=stderr)\n usage_errors = True\n\n if instance_id is None:\n print(\"--instance-id must be specified\", file=stderr)\n usage_errors = True\n\n if name is None:\n print(\"--name must be specified\", file=stderr)\n usage_errors = True\n\n if usage_errors:\n attach_usage()\n return 1\n\n session = Session(**session_kw)\n vset = VolumeSet(session=session, name=name)\n vset.attach(instance_id=instance_id)\n return 0\n\ndef attach_usage(fd=stderr):\n fd.write(\"\"\"\\\nUsage: vset-attach [options]\nAttach a set of EBS volumes to an EC2 instance.\n\nOptions:\n -h | --help\n Display this usage information.\n\n -i | --instance-id \n Attach to the specified instance id. This is required.\n\n -n | --name \n The name of the volume set to attach. This is required.\n\n -p | --profile \n Use credentials from a profile in ~/.aws/credentials.\n\n -r | --region \n Make API calls to the specified AWS region.\n\"\"\")\n fd.flush()\n return\n\n@entry_point\ndef create_volume_set(args):\n \"\"\"\n The main entry point for the vset-create utility.\n \"\"\"\n session_kw = {}\n availability_zone = None\n iops = None\n name = None\n size = None\n tags = []\n volume_type = \"gp2\"\n\n usage_errors = False\n\n try:\n opts, args = getopt(args, \"a:hi:n:p:s:T:t:\",\n [\"availability-zone=\", \"az=\", \"help\", \"iops=\",\n \"name=\", \"profile=\", \"size=\", \"tag=\", \"type=\"])\n except GetoptError as e:\n print(str(e), file=stderr)\n create_usage()\n return 1\n\n for opt, val in opts:\n if opt in (\"-a\", \"--az\", \"--availability-zone\",):\n if len(val) == 0 or val[-1] not in ascii_lowercase:\n print(\"Invalid value for %s: %r\" % (opt, val), file=stderr)\n usage_errors = True\n else:\n availability_zone = val\n session_kw[\"region_name\"] = val[:-1]\n if opt in (\"-h\", \"--help\",):\n create_usage(stdout)\n return 0\n elif opt in (\"-i\", \"--iops\",):\n orig_val = val\n if val.endswith(\"k\"):\n multiplier = 1000\n val = val[:-1]\n else:\n multiplier = 1\n\n try:\n iops = multiplier * int(val)\n if iops <= 0:\n raise ValueError()\n except ValueError:\n print(\"Invalid value for %s: %r\" % (opt, orig_val), file=stderr)\n usage_errors = True\n elif opt in (\"-n\", \"--name\",):\n name = val\n elif opt in (\"-p\", \"--profile\",):\n session_kw[\"profile_name\"] = val\n elif opt in (\"-s\", \"--size\",):\n m = re.match(\n r\"(?P[1-9][0-9]*) *(?PGB|GiB|TB|TiB|PB|PiB)\", val)\n if m:\n size = int(m.group(\"value\"))\n unit = m.group(\"unit\")\n if unit in (\"GB\", \"GiB\"):\n # No change needed\n pass\n elif unit in (\"TB\", \"TiB\",):\n size *= 1024\n elif unit in (\"PB\", \"PiB\",):\n size *= 1024 * 1024\n else:\n print(\"Invalid value for %s: %r\" % (opt, val), file=stderr)\n usage_errors = True\n elif opt in (\"-T\", \"--tag\",):\n if \"=\" in val:\n key, value = val.split(\"=\", 1)\n else:\n key = val\n value = \"\"\n\n tags.append({\"Key\": key, \"Value\": value})\n elif opt in (\"-t\", \"--type\",):\n if val not in (\"gp2\", \"io1\", \"st1\", \"sc1\"):\n print(\"Invalid value for %s: %r\" % (opt, val), file=stderr)\n usage_errors = True\n else:\n volume_type = val\n\n # We don't accept arguments, only options.\n if len(args) > 0:\n print(\"Unknown argument %r\" % args[0], file=stderr)\n usage_errors = True\n\n if availability_zone is None:\n print(\"--availability-zone must be specified\", file=stderr)\n usage_errors = True\n\n if name is None:\n print(\"--name must be specified\", file=stderr)\n usage_errors = True\n\n if size is None:\n print(\"--size must be specified\", file=stderr)\n usage_errors = True\n\n if volume_type == \"io1\":\n if iops is None:\n print(\"--iops must be specified for io1 volume type\", file=stderr)\n usage_errors = True\n elif iops is not None:\n print(\"--iops can only be used with io1 volume type\", file=stderr)\n usage_errors = True\n\n if usage_errors:\n create_usage()\n return 1\n\n session = Session(**session_kw)\n vset = VolumeSet(\n session=session, name=name, availability_zone=availability_zone,\n size_gb=size, volume_type=volume_type, tags=tags, iops=iops)\n vset.create()\n return 0\n\ndef create_usage(fd=stderr):\n fd.write(\"\"\"\\\nUsage: vset-create [options]\nCreate a set of EBS volumes for use in a large RAID array.\n\nOptions:\n -a | --az | --availability-zone \n Create volumes in the specified availability zone. This is required.\n\n -h | --help\n Display this usage information.\n\n -i [k] | --iops [k]\n For io1 volumes, the number of IOPS to provision per volume. Adding\n a 'k' suffix multiplies the value by 1000.\n\n -n | --name \n The name of the volume set to create. This is required.\n\n -p | --profile \n Use credentials from a profile in ~/.aws/credentials.\n\n -s {GB|TB|PB|GiB|TiB|PiB} | --size {GB|TB|PB|GiB|TiB|PiB}\n Total size of the volume set. Fractions are not allowed and units must\n be specified and are always interpreted in binary: GB == GiB == 2**30,\n TB == TiB == 2**40, PB == PiB == 2**50. This is required.\n\n -T [=] | --tag [=]\n Apply the given tag to the created volumes.\n\n -t {gp2|io1|st1|sc1} | --type {gp2|io1|st1|sc1}\n Type of the volume to create. This defaults to gp2 (general purpose\n SSD). Other types are io1 (provisioned IOPS), st1 (throughput optimized\n HDD), and sc1 (cold HDD).\n\"\"\")\n fd.flush()\n return\n\n@entry_point\ndef detach_volume_set(args):\n \"\"\"\n The main entry point for the vset-detach utility.\n \"\"\"\n session_kw = {}\n force = False\n name = None\n\n usage_errors = False\n\n try:\n opts, args = getopt(args, \"hfn:p:r:\",\n [\"help\", \"force\", \"name=\", \"profile=\", \"region=\"])\n except GetoptError as e:\n print(str(e), file=stderr)\n attach_usage()\n return 1\n\n for opt, val in opts:\n if opt in (\"-h\", \"--help\",):\n attach_usage(stdout)\n return 0\n elif opt in (\"-f\", \"--force\",):\n force = True\n elif opt in (\"-n\", \"--name\",):\n name = val\n elif opt in (\"-p\", \"--profile\",):\n session_kw[\"profile_name\"] = val\n elif opt in (\"-r\", \"--region\",):\n session_kw[\"region_name\"] = val\n\n # We don't accept arguments, only options.\n if len(args) > 0:\n print(\"Unknown argument %r\" % args[0], file=stderr)\n usage_errors = True\n\n if name is None:\n print(\"--name must be specified\", file=stderr)\n usage_errors = True\n\n if usage_errors:\n detach_usage()\n return 1\n\n session = Session(**session_kw)\n vset = VolumeSet(session=session, name=name)\n vset.detach(force=force)\n return 0\n\ndef detach_usage(fd=stderr):\n fd.write(\"\"\"\\\nUsage: vset-detach [options]\nDetach a set of EBS volumes from an EC2 instance.\n\nOptions:\n -h | --help\n Display this usage information.\n\n -f | --force\n Force detachment without allowing the OS to flush disk buffers and\n cleanly unmount the volume. This may result in data corruption.\n\n -n | --name \n The name of the volume set to detach.\n\n -p | --profile \n Use credentials from a profile in ~/.aws/credentials.\n\n -r | --region \n Make API calls to the specified AWS region.\n\"\"\")\n fd.flush()\n return\n\nif __name__ == \"__main__\":\n sys_exit(entry_points[argv[1]](argv[2:]))\n", "sub_path": "vset/cli.py", "file_name": "cli.py", "file_ext": "py", "file_size_in_byte": 9839, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "getopt.getopt", "line_number": 29, "usage_type": "call"}, {"api_name": "getopt.GetoptError", "line_number": 32, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 33, "usage_type": "name"}, {"api_name": "sys.stdout", "line_number": 39, "usage_type": "argument"}, {"api_name": "sys.stderr", "line_number": 52, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 56, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 60, "usage_type": "name"}, {"api_name": "boto3.session.Session", "line_number": 67, "usage_type": "call"}, {"api_name": "vset.VolumeSet", "line_number": 68, "usage_type": "call"}, {"api_name": "vset.attach", "line_number": 69, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 72, "usage_type": "name"}, {"api_name": "getopt.getopt", "line_number": 112, "usage_type": "call"}, {"api_name": "getopt.GetoptError", "line_number": 115, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 116, "usage_type": "name"}, {"api_name": "string.ascii_lowercase", "line_number": 122, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 123, "usage_type": "name"}, {"api_name": "sys.stdout", "line_number": 129, "usage_type": "argument"}, {"api_name": "sys.stderr", "line_number": 144, "usage_type": "name"}, {"api_name": "re.match", "line_number": 151, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 164, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 176, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 183, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 187, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 191, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 195, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 200, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 203, "usage_type": "name"}, {"api_name": "boto3.session.Session", "line_number": 210, "usage_type": "call"}, {"api_name": "vset.VolumeSet", "line_number": 211, "usage_type": "call"}, {"api_name": "vset.create", "line_number": 214, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 217, "usage_type": "name"}, {"api_name": "getopt.getopt", "line_number": 267, "usage_type": "call"}, {"api_name": "getopt.GetoptError", "line_number": 269, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 270, "usage_type": "name"}, {"api_name": "sys.stdout", "line_number": 276, "usage_type": "argument"}, {"api_name": "sys.stderr", "line_number": 289, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 293, "usage_type": "name"}, {"api_name": "boto3.session.Session", "line_number": 300, "usage_type": "call"}, {"api_name": "vset.VolumeSet", "line_number": 301, "usage_type": "call"}, {"api_name": "vset.detach", "line_number": 302, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 305, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 331, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 331, "usage_type": "name"}]} +{"seq_id": "235461491", "text": "# -*- coding: utf-8 -*-\n\n'''\n\t知乎api\n'''\n\n#python自带类库\nimport os,re,shutil\n#外部倒入类\nimport click\nfrom bs4 import BeautifulSoup\n#import camelot\nfrom jinja2 import Template\n\nfrom common.utils import *\nimport requests,json\n\n\nfrom zhihu_oauth import ZhihuClient,Answer,Article\nfrom zhihu_oauth.exception import NeedCaptchaException\n\npat = re.compile(r'<[^>]*>', re.S)\ndef _unscape_html(html):\n\tcontent = html.replace(\"
\",\"\\r\\n\").replace(\"
\",\"\\r\\n\")\n\treturn pat.sub('', content)\n\n\t#pat2 = re.compile(r'(?<=\\>).*?(?=\\<)')\n\t#ret = pat2.findall(content)\n\t#return ''.join(ret)\n\n\t# from lxml.html.clean import Cleaner\n\t# cleaner = Cleaner(style = True,scripts=True,comments=True,javascript=True,page_structure=True,safe_attrs_only=True)\n\t# ret = cleaner.clean_html(content)\n\t# return ret\n\n\ndef read_zhihu_collection():\n\n\tprefix_zhihu = \"https://www.zhihu.com/collection/\"\n\ttoken_file = \"zhihu_token.pkl\"\n\tclient = ZhihuClient()\n\tobjlist = []\n\tif os.path.exists(token_file):\n\t\tclient.load_token(token_file)\n\t\tme= client.me()\n\t\tprint(me,me.collection_count)\n\t\tidlist = zhihu_read_cols()\n\t\tprint(idlist)\n\t\tfor i in idlist:\n\t\t\tcollect = client.collection(int(i))\n\t\t\tif collect:\n\t\t\t\tfor content in collect.contents:\n\t\t\t\t\tobj = {}\n\t\t\t\t\tif isinstance(content, Answer):\n\t\t\t\t\t\tanswer = content\n\t\t\t\t\t\tprefix_p = \"https://www.zhihu.com/question/\"\n\t\t\t\t\t\t#help(answer)\n\t\t\t\t\t\tobj[\"title\"] = answer.question.title\n\t\t\t\t\t\tobj[\"content\"] = _unscape_html(answer.content)\n\t\t\t\t\t\tobj[\"url\"] = os.path.join(prefix_p,str(answer.question.id))\n\t\t\t\t\t\tobj[\"tag_name\"] = collect.title\n\t\t\t\t\t\tobj[\"tag_desc\"] = collect.description\n\t\t\t\t\t\tobj[\"tag_id\"] = i\n\t\t\t\t\t\tobj[\"tag_url\"] = os.path.join(prefix_zhihu,i)\n\t\t\t\t\t\t#print(obj[\"content\"])\n\t\t\t\t\t\tobjlist.append(obj)\n\t\t\t\t\t\tbreak\n\t\t\t\t\telif isinstance(content, Article):\n\t\t\t\t\t\tarticle = content\n\t\t\t\t\t\tprefix_p = \"https://zhuanlan.zhihu.com/p/\"\n\n\t\t\t\t\t\tobj[\"title\"] = article.title\n\t\t\t\t\t\tobj[\"content\"] = unscape_html(article.content) #article.content\n\t\t\t\t\t\tobj[\"url\"] = os.path.join(prefix_p,str(article.id))\n\t\t\t\t\t\tobj[\"tag_name\"] = collect.title\n\t\t\t\t\t\tobj[\"tag_desc\"] = collect.description\n\t\t\t\t\t\tobj[\"tag_id\"] = i\n\t\t\t\t\t\tobj[\"tag_url\"] = os.path.join(prefix_zhihu,i)\n\t\t\t\t\t\t#print(obj[\"content\"])\n\t\t\t\t\t\tobjlist.append(obj)\n\t\t\tbreak\n\telse:\n\t\tprint(\"first_login\")\n\t\tzhihu_first_login(client,token_file)\n\n\tfor j in objlist:\n\t\tcontent = j['content']\n\t\ttitle = j['title']+\".txt\"\n\t\tdel j['content'];\n\t\twritedata = json.dumps(j,ensure_ascii=False)+\"\\r\\n\"+content\n\t\twfile = os.path.join(\"/Users/arafat/Downloads/book/zhihu\",title)\n\t\tif os.path.exists(wfile):\n\t\t\tprint(\"文件已存在\",wfile,j)\n\t\telse:\n\t\t\tg_fileutil.write_files(wfile,writedata)\n\t\t\tprint(\"写入文件\",wfile)\n\n\treturn \tobjlist\n\n\n\ndef zhihu_first_login(client,token_file):\n\ttry:\n\t\tclient.login('arafat5549@gmail.com', 'wyy19851218')\n\t\t#me= client.me()\n\t\t#print(me)\n\texcept NeedCaptchaException:\n\t\t# 保存验证码并提示输入，重新登录\n\t\twith open('a.gif', 'wb') as f:\n\t\t\tf.write(client.get_captcha())\n\t\tcaptcha = input('please input captcha:')\n\t\tclient.login('arafat5549@gmail.com', 'wyy19851218', captcha)\n\t\tme= client.me()\n\t\tprint(me)\n\t\tclient.save_token(token_file)\n\n\ndef zhihu_read_cols():\n\tidlist = []\n\tprefix_rsshub = \"https://rsshub.app/zhihu/collection/\"\n\tfile_url = \"/Users/arafat/Downloads/a.html\"\n\tjscontent = g_fileutil.read_file_lines(file_url)\n\tbs4 = BeautifulSoup(\"\".join([k for k in jscontent]), \"lxml\")\n\t#print(bs4)\n\tss = bs4.findAll(\"div\",class_=\"List-item\")\n\tfor s in ss:\n\t\tnode = s.find(\"div\",class_=\"ContentItem\")\n\t\tflag = node.has_attr(\"data-za-extra-module\")\n\t\tif flag:\n\t\t\tjsonstr = node[\"data-za-extra-module\"]\n\t\t\tjsonobj =json.loads(jsonstr)\n\t\t\tcid = jsonobj['card']['content'][\"token\"]\n\t\t\trss = os.path.join(prefix_rsshub,cid)\n\t\t\t#print(rss+\"?limit=1000\")\n\t\t\tidlist.append(cid)\n\treturn idlist\n\ndef net_test():\n\turl = \"https://www.zhihu.com/people/wang-yao-79-36/collections\"\n\tr = requests.get(url)\n\tprint(r.content)\n\n\nif __name__ == '__main__':\n\n\tread_zhihu_collection()\n\t#read_cols()\n\tpass\n", "sub_path": "script/py3init/zhihuutil.py", "file_name": "zhihuutil.py", "file_ext": "py", "file_size_in_byte": 4013, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "re.compile", "line_number": 22, "usage_type": "call"}, {"api_name": "re.S", "line_number": 22, "usage_type": "attribute"}, {"api_name": "zhihu_oauth.ZhihuClient", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "zhihu_oauth.Answer", "line_number": 54, "usage_type": "argument"}, {"api_name": "os.path.join", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "zhihu_oauth.Article", "line_number": 68, "usage_type": "argument"}, {"api_name": "os.path.join", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path", "line_number": 91, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "zhihu_oauth.exception.NeedCaptchaException", "line_number": 107, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 123, "usage_type": "call"}, {"api_name": "bs4.findAll", "line_number": 125, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path", "line_number": 133, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 140, "usage_type": "call"}]} +{"seq_id": "104904370", "text": "#!/usr/bin/env python\r\n\r\n# =============================================================================\r\n# Import libraries\r\n# =============================================================================\r\nfrom ibapi.client import EClient\r\nfrom ibapi.wrapper import EWrapper\r\nfrom ibapi.contract import Contract\r\nfrom ibapi.ticktype import TickTypeEnum\r\nimport pandas as pd\r\nimport datetime\r\nimport math\r\nimport time\r\nimport datetime\r\nimport queue\r\nimport pymysql\r\nimport sqlalchemy\r\nfrom sqlalchemy import create_engine\r\nimport threading\r\nimport queue\r\nfrom queue import Queue\r\n\r\n\r\nflag = threading.Lock()\r\n\r\n# Export queue takes retrieves ticks from the API and immediately hands\r\n# them off to be stored and eventually written to DB\r\nexport_q = Queue()\r\n\r\n# tick_list gets ticks from the export_q and appends them until it reaches\r\n# a len threshold to export to DB\r\ntick_list = []\r\n\r\n# df_queue takes the tick_list to be exported so the tick_list can be \r\n#$ immediately reset to accept new ticks\r\ndf_queue = Queue()\r\n\r\n# reqId codes specified in the GetTicks function, converted to strings\r\nreqId_codes = {1:'price', 2:'trade'}\r\n\r\n\r\n\r\n# =============================================================================\r\n# MySQL Functions\r\n# =============================================================================\r\n\r\ndef sql_connect(schema='ES'):\r\n db = pymysql.connect('localhost', 'root', 'rootroot', schema)\r\n return(db)\r\n\r\ndb = sql_connect('ES')\r\n\r\n# SQL close connection\r\ndef sql_close():\r\n db.close()\r\n\r\n# SQL query\r\ndef qry(query, schema = 'ES'):\r\n df = pd.read_sql(query, db)\r\n return df\r\n\r\n# SQL update\r\ndef update(query, db=db):\r\n db = db\r\n db_cursor = db.cursor()\r\n db_cursor.execute(query)\r\n db.commit()\r\n\r\n# Allows writing in the DB\r\nupdate(\"set sql_safe_updates=0\")\r\n\r\n# engine allows batch writing into DB\r\nengine = create_engine(\"mysql+pymysql://{user}:{pw}@localhost/{db}\".format(user=\"root\", pw=\"rootroot\", db='ES'))\r\n\r\n# Create DB table name for new session\r\ntable_name = str('ES_')+str(datetime.datetime.now().year)+'-'+ \\\r\nstr(datetime.datetime.now().month)+'-' + \\\r\nstr(datetime.datetime.now().day+1)\r\n\r\nupdate(\"use `ES`;\")\r\n\r\nupdate(f\"create table if not exists `ES`.`{table_name}` (\\\r\n`Index_key` BIGINT UNSIGNED NOT NULL AUTO_INCREMENT, \\\r\n`Timestamp` DATETIME, \\\r\n`ReqId` CHARACTER(16), \\\r\n`BidQty` SMALLINT UNSIGNED, \\\r\n`BidPrc` FLOAT8 UNSIGNED, \\\r\n`AskPrc` FLOAT8 UNSIGNED, \\\r\n`AskQty` SMALLINT UNSIGNED, \\\r\n`Price` DECIMAL(6,2) UNSIGNED, \\\r\n`Qty` SMALLINT UNSIGNED, \\\r\nPRIMARY KEY (`Index_key`)) \\\r\nENGINE = InnoDB \\\r\nAUTO_INCREMENT = 0 \\\r\nDEFAULT CHARACTER SET = utf8mb4;\")\r\n\r\n\r\n# =============================================================================\r\n# Data Handling\r\n# =============================================================================\r\n\r\n# Pulls data from export_q into tick_list\r\ndef q_to_list():\r\n global tick_list\r\n tick_list.append(export_q.get())\r\n flag.acquire()\r\n if len(tick_list)>=1000:\r\n df_queue.put(tick_list)\r\n tick_list = []\r\n sql_export()\r\n flag.release()\r\n\r\n# Pulls data from the df_queue, converts to a DF and pushes to DB; called by q_to_list function\r\ndef sql_export():\r\n tick_df = pd.DataFrame(df_queue.get())\r\n tick_df['timestamp'] = tick_df['timestamp'].apply(lambda x: datetime.datetime.fromtimestamp(x).strftime('%Y-%m-%d %H:%M:%S.000'))\r\n tick_df.to_sql(table_name, con=engine, if_exists='append', chunksize=1000, index=False)\r\n print(datetime.datetime.now(), \": \", 'Pushed to DB', sep = '')\r\n\r\n\r\n# =============================================================================\r\n# IB Data Feeds\r\n# =============================================================================\r\n\r\n\r\nclass TickFeed(EWrapper, EClient):\r\n\r\n def __init__(self, export_func):\r\n EClient.__init__(self,self)\r\n self.export_func = export_func\r\n global export_q\r\n\r\n\r\n def error(self, reqId, errorCode, errorString):\r\n print(datetime.datetime.now(),\"Msg Code: \", reqId, \" \",errorCode, \" \", errorString)\r\n\r\n\r\n def tickByTickBidAsk(self, reqId: int, time: int, bidPrice: float, askPrice: float,\r\n bidSize: int, askSize: int, tickAttribBidAsk: int):\r\n\r\n super().tickByTickBidAsk(reqId, time, bidPrice, askPrice, bidSize,\r\n askSize, tickAttribBidAsk)\r\n new_price = {'timestamp':time, 'reqId':reqId_codes[reqId], 'bidQty':bidSize, 'bidPrc':bidPrice, 'askPrc':askPrice, 'askQty':askSize}\r\n #print(new_price)\r\n #flag.acquire()\r\n export_q.put(new_price)\r\n #flag.release()\r\n #get_queue_thread.run()\r\n self.export_func()\r\n\r\n def tickByTickAllLast(self, reqId: int, tickType: int, time: int, price: float,\r\n size: int, tickAtrribLast: int, exchange: str,\r\n specialConditions: str):\r\n\r\n super().tickByTickAllLast(reqId, tickType, time, price, size, tickAtrribLast,\r\n exchange, specialConditions)\r\n new_trade = {'timestamp':time, 'reqId':reqId_codes[reqId], 'price':price, 'qty':size}\r\n #print(new_trade)\r\n #flag.acquire()\r\n export_q.put(new_trade)\r\n #flag.release()\r\n #get_queue_thread.run()\r\n self.export_func()\r\n\r\n\r\ndef GetTicks():\r\n app = TickFeed(export_func = q_to_list)\r\n\r\n app.connect(\"127.0.0.1\", 4002, 0)\r\n\r\n contract = Contract()\r\n contract.secType = \"FUT\"\r\n contract.exchange = \"GLOBEX\"\r\n contract.currency = \"USD\"\r\n contract.localSymbol = \"ESH0\"\r\n\r\n app.reqMarketDataType(1)\r\n app.reqTickByTickData(1, contract, \"BidAsk\", 0, False)\r\n app.reqTickByTickData(2, contract, \"AllLast\", 0, False)\r\n\r\n app.run()\r\n\r\nif __name__ == '__main__':\r\n GetTicks()", "sub_path": "IB_API_prod.py", "file_name": "IB_API_prod.py", "file_ext": "py", "file_size_in_byte": 5763, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "threading.Lock", "line_number": 24, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 28, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 36, "usage_type": "call"}, {"api_name": "pymysql.connect", "line_number": 48, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 59, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 73, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 76, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 76, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 77, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 77, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 78, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 78, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 115, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 116, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 116, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 118, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 118, "usage_type": "attribute"}, {"api_name": "ibapi.wrapper.EWrapper", "line_number": 126, "usage_type": "name"}, {"api_name": "ibapi.client.EClient", "line_number": 126, "usage_type": "name"}, {"api_name": "ibapi.client.EClient.__init__", "line_number": 129, "usage_type": "call"}, {"api_name": "ibapi.client.EClient", "line_number": 129, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 135, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 135, "usage_type": "attribute"}, {"api_name": "ibapi.contract.Contract", "line_number": 171, "usage_type": "call"}]} +{"seq_id": "321013987", "text": "# -*- encoding: utf-8 -*-\n\nimport odoorpc\nfrom datetime import datetime\nfrom datetime import date\nfrom datetime import timedelta\nimport atscon as con\nimport re\nimport sys\n\n_table = { \n \"á\" : \"a\", \"à\" : \"a\", \"â\" : \"a\", \"ä\" : \"a\", \"ã\" : \"a\", \"å\" : \"a\",\n \"é\" : \"e\", \"è\" : \"e\", \"ê\" : \"e\", \"ë\" : \"e\",\n \"í\" : \"i\", \"ì\" : \"i\", \"î\" : \"i\", \"ï\" : \"i\",\n \"ó\" : \"o\", \"ò\" : \"o\", \"ô\" : \"o\", \"ö\" : \"o\", \"õ\" : \"o\", \"ø\" : \"o\", \n \"ú\" : \"u\", \"ù\" : \"u\", \"û\" : \"u\", \"ü\" : \"u\",\n \"ñ\" : \"n\", \"ç\" : \"c\",\n \"Á\" : \"A\", \"À\" : \"A\", \"Â\" : \"A\", \"Ä\" : \"A\", \"Ã\" : \"A\", \"Å\" : \"A\",\n \"É\" : \"E\", \"È\" : \"E\", \"Ê\" : \"E\", \"Ë\" : \"E\", \n \"Í\" : \"I\", \"Ì\" : \"I\", \"Î\" : \"I\", \"Ï\" : \"I\", \n \"Ó\" : \"O\", \"Ò\" : \"O\", \"Ô\" : \"O\", \"Ö\" : \"O\", \"Õ\" : \"O\", \"Ø\" : \"O\",\n \"Ú\" : \"U\", \"Ù\" : \"U\", \"Û\" : \"U\", \"Ü\" : \"U\", \n \"Ñ\" : \"N\", \"Ç\" : \"C\",\n \"ß\" : \"ss\", \"Þ\" : \"d\" , \"æ\" : \"ae\", \"º\": \".\", \"ª\": \".\", \"'\": \"\"\n}\n\nclass AtsCliente:\n\n def asciize(self,s):\n \"\"\" \n Converts a entire string to a ASCII only string.\n\n string\n The string to be converted.\n \"\"\"\n for original, plain in _table.items():\n s = s.replace(original, plain)\n return s\n\n \n ######## IMPORTAR CLIENTES\n def clientes(self):\n db = con.Conexao()\n sist = db.sistema()\n coding = sys.stdout.encoding\n #import pudb;pu.db\n #order = odoo.env['pos.order']\n hj = datetime.now()\n hj = hj - timedelta(days=1220)\n hj = datetime.strftime(hj,'%Y-%m-%d %H:%M:%S')\n\n cliente = sist.env['res.partner']\n #if cliente_id == 0:\n cli_ids = cliente.search([('create_date', '>=', hj), ('customer','=', True)])\n #else:\n # cli_ids = cliente_id\n for partner_id in cliente.browse(cli_ids):\n sqlc = 'select codcliente from clientes where codcliente = %s' %(partner_id.id)\n cli = db.query(sqlc)\n nome = self.asciize(partner_id.name.encode(coding))\n if partner_id.legal_name:\n razao = self.asciize(partner_id.legal_name.encode(coding))\n else:\n razao = nome\n print(partner_id.name.encode('ascii', 'ignore'))\n try:\n print(nome.decode())\n except:\n nome = partner_id.name.encode('ascii', 'ignore')\n if not len(cli):\n tipo = '0'\n if partner_id.is_company:\n tipo = '1'\n vendedor = '1'\n if partner_id.user_id.id:\n vendedor = str(partner_id.user_id.id)\n ie = ''\n if partner_id.inscr_est:\n ie = partner_id.inscr_est\n fiscal = 'J'\n #if partner_id.property_account_position:\n # fiscal = partner_id.property_account_position.note\n #nome = partner_id.name.encode('ascii', 'ignore')\n regiao = '0'\n if partner_id.curso:\n regiao = '1'\n insere = 'insert into clientes (\\\n CODCLIENTE, NOMECLIENTE, RAZAOSOCIAL,\\\n TIPOFIRMA,CNPJ, INSCESTADUAL,\\\n SEGMENTO, REGIAO, LIMITECREDITO,\\\n DATACADASTRO, CODUSUARIO, STATUS, CODBANCO, CODFISCAL)\\\n values (%s, \\'%s\\', \\'%s\\',\\\n %s, \\'%s\\',\\'%s\\',\\\n %s, %s, %s,\\\n %s, %s, %s, %s, \\'%s\\')'\\\n %(str(partner_id.id), nome.decode(), razao.decode(), \\\n tipo, partner_id.cnpj_cpf, ie,\\\n '1', regiao, '0.0',\\\n 'current_date', vendedor, '1', '1', fiscal)\n \n db.insert(insere)\n fone = 'Null'\n ddd = 'Null'\n if partner_id.phone:\n fone = '''%s''' %(partner_id.phone[4:])\n ddd = '''%s''' %(partner_id.phone[1:3])\n fone1 = 'Null'\n ddd1 = 'Null'\n if partner_id.mobile:\n fone1 = '''%s''' %(partner_id.mobile[4:])\n ddd1 = partner_id.mobile[1:3]\n fone2 = 'Null'\n ddd2 = 'Null'\n if partner_id.fax:\n fone2 = partner_id.fax[4:]\n ddd2 = partner_id.fax[1:3]\n #buscar Cidade/UF/Pais\n cidade = 'Null'\n ibge = 'Null'\n uf = 'Null'\n pais = 'Null'\n if partner_id.city_id:\n cidade = partner_id.city_id.name[:39]\n if partner_id.city_id.ibge_code:\n ibge = '%s%s-%s' %(partner_id.city_id.state_id.ibge_code, \\\n partner_id.city_id.ibge_code[:4], \\\n partner_id.city_id.ibge_code[4:])\n uf = partner_id.city_id.state_id.code\n pais = partner_id.city_id.state_id.country_id.name\n endereco = 'Null'\n if partner_id.street:\n endereco = partner_id.street[:49]\n bairro = 'Null'\n if partner_id.district:\n bairro = partner_id.district[:29]\n complemento = 'Null'\n if partner_id.street2:\n complemento = partner_id.street2[:29]\n cep = 'Null'\n if partner_id.zip:\n cep = '%s-%s' %(partner_id.zip[:5], \\\n partner_id.zip[5:])\n cep = cep[:10]\n email = 'Null'\n if partner_id.email:\n email = partner_id.email[:255]\n obs = 'Null'\n if partner_id.comment:\n obs = partner_id.comment[:199]\n numero = 'Null'\n if partner_id.number:\n numero = partner_id.number[:5]\n inserir = 'INSERT INTO ENDERECOCLIENTE (CODENDERECO, \\\n CODCLIENTE, LOGRADOURO, BAIRRO, COMPLEMENTO,\\\n CIDADE, UF, CEP, TELEFONE, TELEFONE1, TELEFONE2,\\\n E_MAIL, TIPOEND,\\\n DADOSADICIONAIS, DDD, DDD1, DDD2,\\\n NUMERO, CD_IBGE, PAIS) VALUES ('\n inserir += str(partner_id.id)\n inserir += ',' + str(partner_id.id)\n if endereco != 'Null':\n inserir += ', \\'%s\\'' %(str(endereco.encode('ascii', 'ignore')))\n else:\n inserir += ', Null'\n if bairro != 'Null':\n inserir += ', \\'%s\\'' % (str(bairro.encode('ascii', 'ignore')))\n else:\n inserir += ', Null'\n if complemento != 'Null':\n inserir += ', \\'%s\\'' % (str(complemento.encode('ascii', 'ignore')))\n else:\n inserir += ', Null'\n if cidade != 'Null':\n inserir += ', \\'%s\\'' % (str(cidade.encode('ascii', 'ignore')))\n else:\n inserir += ', Null'\n if uf != 'Null':\n inserir += ', \\'%s\\'' % (str(uf))\n else:\n inserir += ', Null'\n if cep != 'Null':\n inserir += ', \\'%s\\'' % (cep)\n else:\n inserir += ', Null'\n if fone != 'Null':\n inserir += ', \\'%s\\'' % (fone)\n else:\n inserir += ', Null'\n if fone1 != 'Null':\n inserir += ', \\'%s\\'' % (fone1)\n else:\n inserir += ', Null'\n if fone2 != 'Null':\n inserir += ', \\'%s\\'' % (fone2)\n else:\n inserir += ', Null'\n if email != 'Null':\n inserir += ', \\'%s\\'' % (email)\n else:\n inserir += ', Null'\n inserir += ', 0' # tipoEnd\n if obs != 'Null':\n inserir += ', \\'%s\\'' % (str(obs.encode('ascii', 'ignore')))\n else:\n inserir += ', Null'\n if ddd != 'Null':\n inserir += ', \\'%s\\'' % (ddd)\n else:\n inserir += ', Null'\n if ddd1 != 'Null':\n inserir += ', \\'%s\\'' % (ddd1)\n else:\n inserir += ', Null'\n if ddd2 != 'Null':\n inserir += ', \\'%s\\'' % (ddd2)\n else:\n inserir += ', Null'\n if numero != 'Null':\n inserir += ', \\'%s\\'' % (numero)\n else:\n inserir += ', Null'\n if ibge != 'Null':\n inserir += ', \\'%s\\'' % (ibge)\n else:\n inserir += ', Null'\n if pais != 'Null':\n inserir += ', \\'%s\\');' % (pais)\n else:\n inserir += ', Null);'\n print(partner_id.street)\n db.insert(inserir)\n else:\n regiao = '0'\n if partner_id.curso:\n regiao = '1'\n altera = 'UPDATE CLIENTES SET REGIAO = %s \\\n ,NOMECLIENTE = \\'%s\\' \\\n WHERE CODCLIENTE = %s' %(regiao, nome.decode(), str(partner_id.id))\n db.insert(altera )\n \np = AtsCliente()\np.clientes()\n", "sub_path": "atsCliente.py", "file_name": "atsCliente.py", "file_ext": "py", "file_size_in_byte": 9851, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "atscon.Conexao", "line_number": 43, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 45, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 48, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.datetime.strftime", "line_number": 50, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 50, "usage_type": "name"}]} +{"seq_id": "62736068", "text": "from django.shortcuts import render_to_response\nfrom django.template import RequestContext\nfrom django.http import HttpResponse, HttpResponseBadRequest, HttpResponseServerError\nfrom django.views.decorators.http import require_GET\nfrom django.views.decorators.cache import cache_page\nfrom django.utils import simplejson\n\nfrom thankyou.models.video import Video\n\nimport logging\n\n\nlogger = logging.getLogger(__name__)\n\n\n@require_GET\n@cache_page(60 * 60)\ndef index(request):\n return render_to_response(\"videos.html\",\n {\n 'videos': Video.objects.all()[:9]\n },\n context_instance=RequestContext(request))\n\n\n@require_GET\ndef more(request):\n if not request.is_ajax():\n return HttpResponseBadRequest()\n\n try:\n skip = request['skip'] if 'skip' in request else 0\n videos = map(lambda v: v.to_dict(), Video.objects.all()[skip:9])\n videos_json = simplejson.dumps({'videos': videos})\n return HttpResponse(videos_json, mimetype='application/json')\n except Exception as e:\n logger.error(\"Failed to retrieve more videos\")\n logger.error(e)\n return HttpResponseServerError()\n", "sub_path": "thankyou/views/video.py", "file_name": "video.py", "file_ext": "py", "file_size_in_byte": 1155, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 19, "usage_type": "call"}, {"api_name": "thankyou.models.video.Video.objects.all", "line_number": 21, "usage_type": "call"}, {"api_name": "thankyou.models.video.Video.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "thankyou.models.video.Video", "line_number": 21, "usage_type": "name"}, {"api_name": "django.template.RequestContext", "line_number": 23, "usage_type": "call"}, {"api_name": "django.views.decorators.http.require_GET", "line_number": 16, "usage_type": "name"}, {"api_name": "django.views.decorators.cache.cache_page", "line_number": 17, "usage_type": "call"}, {"api_name": "django.http.HttpResponseBadRequest", "line_number": 29, "usage_type": "call"}, {"api_name": "thankyou.models.video.Video.objects.all", "line_number": 33, "usage_type": "call"}, {"api_name": "thankyou.models.video.Video.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "thankyou.models.video.Video", "line_number": 33, "usage_type": "name"}, {"api_name": "django.utils.simplejson.dumps", "line_number": 34, "usage_type": "call"}, {"api_name": "django.utils.simplejson", "line_number": 34, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 35, "usage_type": "call"}, {"api_name": "django.http.HttpResponseServerError", "line_number": 39, "usage_type": "call"}, {"api_name": "django.views.decorators.http.require_GET", "line_number": 26, "usage_type": "name"}]} +{"seq_id": "226350460", "text": "from transformers import *\nfrom custom_data import *\nfrom tqdm import tqdm\nfrom torch.utils.data import DataLoader\nfrom torch.nn import functional as F\nfrom itertools import chain\n\nimport torch\nimport os, sys\nimport numpy as np\nimport argparse\nimport copy\nimport random\n\n\nclass Manager():\n def __init__(self, args):\n self.args = args\n \n if torch.cuda.is_available():\n self.args.device = torch.device(f\"cuda:{self.args.gpu}\")\n else:\n self.args.device = torch.device(\"cpu\")\n \n # Tokenizer & Vocab\n print(\"Loading the tokenizer...\")\n self.tokenizer = GPT2Tokenizer.from_pretrained(self.args.model_type)\n special_tokens = {\n 'bos_token': self.args.bos_token,\n 'eos_token': self.args.eos_token,\n 'pad_token': self.args.pad_token,\n 'additional_special_tokens': [self.args.sp1_token, self.args.sp2_token]\n }\n num_new_tokens = self.tokenizer.add_special_tokens(special_tokens)\n vocab = self.tokenizer.get_vocab()\n self.args.vocab_size = len(vocab)\n self.args.pad_id = vocab[self.args.pad_token]\n self.args.bos_id = vocab[self.args.bos_token]\n self.args.eos_id = vocab[self.args.eos_token]\n self.args.sp1_id = vocab[self.args.sp1_token]\n self.args.sp2_id = vocab[self.args.sp2_token]\n \n self.args.utter_len = (self.args.max_len-self.args.max_turns-2) // self.args.max_turns\n \n # Load model \n print(\"Loading the model...\")\n self.fix_seed(self.args.seed)\n self.model = GPT2LMHeadModel.from_pretrained(self.args.model_type).to(self.args.device)\n self.model.resize_token_embeddings(self.args.vocab_size)\n \n self.args.max_len = min(self.args.max_len, self.model.config.n_ctx)\n \n if self.args.mode == 'train': \n # Load optimizer\n print(\"Loading the optimizer...\")\n self.optim = torch.optim.AdamW(self.model.parameters(), lr=self.args.lr)\n self.best_loss = sys.float_info.max\n self.last_epoch = 0\n \n # Load train & valid dataset\n print(\"Loading train & valid data...\")\n train_set = CustomDataset(self.args.train_prefix, self.args)\n valid_set = CustomDataset(self.args.valid_prefix, self.args)\n ppd = PadCollate(pad_id=self.args.pad_id)\n \n self.fix_seed(self.args.seed)\n self.train_loader = DataLoader(train_set, \n collate_fn=ppd.pad_collate, \n shuffle=True, \n batch_size=self.args.batch_size, \n num_workers=self.args.num_workers, \n pin_memory=True)\n self.valid_loader = DataLoader(valid_set, \n collate_fn=ppd.pad_collate,\n batch_size=self.args.batch_size, \n num_workers=self.args.num_workers, \n pin_memory=True)\n \n if not os.path.exists(self.args.ckpt_dir):\n os.makedirs(self.args.ckpt_dir)\n \n if self.args.ckpt_name is not None:\n if os.path.exists(f\"{self.args.ckpt_dir}/{self.args.ckpt_name}.ckpt\"):\n print(\"Loading the trained checkpoint...\")\n ckpt = torch.load(f\"{self.args.ckpt_dir}/{self.args.ckpt_name}.ckpt\")\n self.model.load_state_dict(ckpt['model_state_dict'])\n \n if self.args.mode == 'train':\n print(f\"The training restarts with the specified checkpoint: {self.args.ckpt_name}.ckpt.\")\n self.optim.load_state_dict(ckpt['optim_state_dict'])\n self.best_loss = ckpt['loss']\n self.last_epoch = ckpt['epoch']\n else:\n print(\"The inference will start with the specified checkpoint.\")\n else:\n print(\"Cannot fine the specified checkpoint.\")\n if self.args.mode == 'train':\n print(\"Training will start with the initialized model.\")\n else:\n print(\"Cannot inference.\")\n exit()\n \n print(\"Setting finished.\")\n \n def train(self):\n print(\"Training starts.\")\n \n start_epoch = self.last_epoch+1\n for epoch in range(start_epoch, start_epoch+self.args.num_epochs):\n self.model.train()\n \n print(f\"#\"*50 + f\"Epoch: {epoch}\" + \"#\"*50)\n train_losses = []\n train_ppls = []\n for i, batch in enumerate(tqdm(self.train_loader)):\n input_ids, token_type_ids, lm_labels = batch\n input_ids, token_type_ids, lm_labels = \\\n input_ids.to(self.args.device), token_type_ids.to(self.args.device), lm_labels.to(self.args.device)\n \n outputs = self.model(\n input_ids=input_ids,\n token_type_ids = token_type_ids,\n labels = lm_labels\n )\n \n loss, logits = outputs[0], outputs[1]\n \n self.optim.zero_grad()\n loss.backward()\n self.optim.step()\n \n train_losses.append(loss)\n train_ppls.append(torch.exp(loss))\n \n train_losses = [loss.item() for loss in train_losses]\n train_ppls = [ppl.item() for ppl in train_ppls]\n train_loss = np.mean(train_losses)\n train_ppl = np.mean(train_ppls)\n print(f\"Train loss: {train_loss} || Train perplexity: {train_ppl}\")\n \n self.last_epoch += 1\n \n valid_loss, valid_ppl = self.validation()\n \n if valid_loss < self.best_loss:\n self.best_loss = valid_loss\n state_dict = {\n 'model_state_dict': self.model.state_dict(),\n 'optim_state_dict': self.optim.state_dict(),\n 'loss': self.best_loss,\n 'epoch': self.last_epoch\n }\n \n torch.save(state_dict, f\"{self.args.ckpt_dir}/best_ckpt_epoch={epoch}_valid_loss={round(self.best_loss, 4)}.ckpt\")\n print(\"*\"*10 + \"Current best checkpoint is saved.\" + \"*\"*10)\n print(f\"{self.args.ckpt_dir}/best_ckpt_epoch={epoch}_valid_loss={round(self.best_loss, 4)}.ckpt\")\n \n print(f\"Best valid loss: {self.best_loss}\")\n print(f\"Valid loss: {valid_loss} || Valid perplexity: {valid_ppl}\")\n \n print(\"Training finished!\")\n \n def validation(self):\n print(\"Validation processing...\")\n self.model.eval()\n \n valid_losses = []\n valid_ppls = []\n with torch.no_grad():\n for i, batch in enumerate(tqdm(self.valid_loader)):\n input_ids, token_type_ids, lm_labels = batch\n input_ids, token_type_ids, lm_labels = \\\n input_ids.to(self.args.device), token_type_ids.to(self.args.device), lm_labels.to(self.args.device)\n \n outputs = self.model(\n input_ids=input_ids,\n token_type_ids = token_type_ids,\n labels = lm_labels\n )\n \n loss, logits = outputs[0], outputs[1]\n \n valid_losses.append(loss)\n valid_ppls.append(torch.exp(loss))\n \n valid_losses = [loss.item() for loss in valid_losses]\n valid_ppls = [ppl.item() for ppl in valid_ppls]\n valid_loss = np.mean(valid_losses)\n valid_ppl = np.mean(valid_ppls)\n \n return valid_loss, valid_ppl\n \n \n def inference(self):\n print(\"Let's start!\")\n print(f\"If you want to quit the conversation, please type \\\"{self.args.end_command}\\\".\")\n self.model.eval()\n \n with torch.no_grad():\n cur_sp = 1\n input_ids_list = []\n token_type_ids_list = []\n t = 0\n output_id = None\n \n while True:\n if cur_sp == 1:\n cur_sp_id = self.args.sp1_id\n utter = input(\"You: \")\n \n if utter == self.args.end_command:\n print(\"Bot: Good bye.\")\n break\n \n input_id = [cur_sp_id] + self.tokenizer.encode(utter)\n \n if t == 0:\n input_id = [self.args.bos_id] + input_id\n else:\n cur_sp_id = self.args.sp2_id\n input_id = copy.deepcopy(output_id)\n \n token_type_id = [cur_sp_id] * len(input_id)\n \n if input_id[-1] == self.args.eos_id:\n input_id = input_id[:-1]\n token_type_id = token_type_id[:-1] \n \n input_ids_list.append(input_id)\n token_type_ids_list.append(token_type_id)\n \n if t >= self.args.max_turns:\n input_ids_list = input_ids_list[1:]\n token_type_ids_list = token_type_ids_list[1:]\n \n next_sp = (cur_sp % 2) + 1\n if next_sp == 1:\n next_sp_id = self.args.sp1_id\n else:\n next_sp_id = self.args.sp2_id\n \n if cur_sp == 1:\n output_id = self.nucleus_sampling(input_ids_list, token_type_ids_list, next_sp_id)\n res = self.tokenizer.decode(output_id)\n\n print(f\"Bot: {res}\")\n \n cur_sp = next_sp\n t += 1\n \n def nucleus_sampling(self, input_ids_list, token_type_ids_list, next_sp_id):\n output_id = []\n res_id = [next_sp_id]\n res_type_id = [next_sp_id]\n for pos in range(self.args.utter_len):\n input_ids = list(chain.from_iterable(input_ids_list)) + res_id\n token_type_ids = list(chain.from_iterable(token_type_ids_list)) + res_type_id\n input_len = len(input_ids)\n \n left = self.args.max_len - len(input_ids)\n input_ids += [self.args.pad_id] * left\n token_type_ids += [self.args.pad_id] * left\n\n assert len(input_ids) == len(token_type_ids), \"There is something wrong in dialogue process.\"\n \n input_ids = torch.LongTensor(input_ids).unsqueeze(0).to(self.args.device) # (1, L)\n token_type_ids = torch.LongTensor(token_type_ids).unsqueeze(0).to(self.args.device) # (1, L)\n \n output = self.model(input_ids=input_ids, token_type_ids=token_type_ids)[0][:, input_len-1] # (1, vocab_size)\n output = F.softmax(output, dim=-1) # (1, vocab_size)\n \n sorted_probs, sorted_idxs = torch.sort(output, descending=True)\n cumsum_probs = torch.cumsum(sorted_probs, dim=-1) # (1, vocab_size)\n idx_remove = cumsum_probs > self.args.top_p\n sorted_probs[idx_remove] = 1e-8\n sorted_probs /= torch.sum(sorted_probs, dim=-1, keepdim=True) # (1, vocab_size)\n \n # Random sampling\n probs = torch.zeros(output.shape).to(self.args.device).scatter_(-1, sorted_idxs, sorted_probs) # (1, vocab_size)\n idx = torch.multinomial(probs, 1).squeeze(-1).squeeze(0).item()\n \n if len(output_id) == self.args.utter_len or idx == self.args.eos_id:\n break\n else:\n output_id.append(idx)\n res_id.append(idx)\n res_type_id.append(next_sp_id)\n \n return output_id\n \n def fix_seed(self, seed):\n np.random.seed(seed)\n torch.manual_seed(seed)\n torch.cuda.manual_seed_all(seed)\n random.seed(seed)\n \n\nif __name__=='__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('--seed', type=int, default=0, help=\"The random seed.\")\n parser.add_argument('--mode', type=str, required=True, help=\"The running mode: train or inference?\")\n parser.add_argument('--data_dir', type=str, default=\"data\", help=\"The name of the parent directory where data files are stored.\")\n parser.add_argument('--train_prefix', type=str, default=\"train\", help=\"The prefix of the train data files' name.\")\n parser.add_argument('--valid_prefix', type=str, default=\"valid\", help=\"The prefix of the validation data files' name.\")\n parser.add_argument('--model_type', type=str, default=\"gpt2\", help=\"The model type of GPT-2.\")\n parser.add_argument('--pad_token', type=str, default=\"\", help=\"The pad token.\")\n parser.add_argument('--bos_token', type=str, default=\"\", help=\"The BOS token.\")\n parser.add_argument('--eos_token', type=str, default=\"\", help=\"The EOS token.\")\n parser.add_argument('--sp1_token', type=str, default=\"\", help=\"The speaker1 token.\")\n parser.add_argument('--sp2_token', type=str, default=\"\", help=\"The speaker2 token.\")\n parser.add_argument('--gpu', type=str, default=\"0\", help=\"The index of GPU to use.\")\n parser.add_argument('--lr', type=float, default=5e-4, help=\"The learning rate.\")\n parser.add_argument('--batch_size', type=int, default=8, help=\"The batch size.\")\n parser.add_argument('--num_workers', type=int, default=0, help=\"The number of workers for data loading.\")\n parser.add_argument('--num_epochs', type=int, default=10, help=\"The number of total epochs.\")\n parser.add_argument('--max_len', type=int, default=1024, help=\"The maximum length of input sequence.\")\n parser.add_argument('--max_turns', type=int, default=5, help=\"The maximum number of dialogue histories to include.\")\n parser.add_argument('--top_p', type=float, default=0.9, help=\"The top-p value for nucleus sampling decoding.\")\n parser.add_argument('--ckpt_dir', type=str, default=\"saved_models\", help=\"The directory name for saved checkpoints.\")\n parser.add_argument('--ckpt_name', type=str, required=False, help=\"The name of the trained checkpoint. (without extension)\")\n parser.add_argument('--end_command', type=str, default=\"Abort!\", help=\"The command to stop the conversation when inferencing.\")\n \n args = parser.parse_args()\n \n assert args.mode in [\"train\", \"inference\"]\n assert args.model_type in [\"gpt2\", \"gpt2-medium\", \"gpt2-large\", \"gpt2-xl\"]\n \n args.data_dir = f\"{args.data_dir}/{args.model_type}\"\n args.ckpt_dir = f\"{args.ckpt_dir}/{args.model_type}\"\n \n if args.mode == 'train':\n manager = Manager(args)\n manager.train()\n \n elif args.mode == 'inference':\n assert args.ckpt_name is not None, \"Please specify the trained model checkpoint.\"\n \n manager = Manager(args)\n manager.inference()\n", "sub_path": "src/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 15498, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "torch.cuda.is_available", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 20, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.optim.AdamW", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 56, "usage_type": "attribute"}, {"api_name": "sys.float_info", "line_number": 57, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 85, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 138, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 154, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 169, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 170, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 189, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 199, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 221, "usage_type": "call"}, {"api_name": "itertools.chain.from_iterable", "line_number": 256, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 256, "usage_type": "name"}, {"api_name": "itertools.chain.from_iterable", "line_number": 257, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 257, "usage_type": "name"}, {"api_name": "torch.LongTensor", "line_number": 266, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 267, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 270, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 270, "usage_type": "name"}, {"api_name": "torch.sort", "line_number": 272, "usage_type": "call"}, {"api_name": "torch.cumsum", "line_number": 273, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 276, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 279, "usage_type": "call"}, {"api_name": "torch.multinomial", "line_number": 280, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 292, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 292, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 293, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed_all", "line_number": 294, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 294, "usage_type": "attribute"}, {"api_name": "random.seed", "line_number": 295, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 299, "usage_type": "call"}]} +{"seq_id": "621911424", "text": "import requests\nimport uuid\nimport os\nfrom bs4 import BeautifulSoup\nfrom login.videorank.feteadm_api import FateadmApi\nfrom cookiespool.config import *\n\nclass VideoRankCookies():\n def __init__(self, mobile, password):\n self.url = 'https://videorank.cn/login'\n self.mobile = mobile\n self.password = password\n self.is_login = False\n\n def login(self):\n \"\"\"\n 登录\n :return:\n \"\"\"\n\n #获取cookie\n response = requests.get(self.url)\n cookies = response.cookies\n #获取token\n token = ''\n soup = BeautifulSoup(response.text, 'lxml')\n token = soup.find(attrs={'name' : '_token'}).get('value')\n headers = {\n 'Host': 'videorank.cn',\n 'Origin': 'https://videorank.cn',\n 'Referer': 'https://videorank.cn/login',\n 'Accept' : 'image/webp,image/apng,image/*,*/*;q=0.8',\n 'Accept-Encoding': 'gzip, deflate, br',\n 'Connection': 'keep-alive',\n 'Content-Encoding': 'gzip',\n 'Content-Type': 'application/x-www-form-urlencoded',\n 'User-Agent' : 'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.109 Safari/537.36'\n }\n\n #通过cookie获取验证码\n response = requests.get('https://videorank.cn/captcha/default', headers= headers, cookies = cookies)\n # print(response.content)\n file_name = '/app/image/' + str(uuid.uuid1()) + '.png'\n with open(file_name, 'wb') as f:\n f.write(response.content)\n #解析验证码\n captcha = self.get_captcha_code(file_name)\n os.remove(file_name)\n #登录\n response = requests.post(\n 'https://videorank.cn/login?_token=' + token + '&mobile=' + self.mobile + '&password=' + self.password + '&captcha=' + captcha.value, headers= headers, cookies=response.cookies, verify=False)\n # print(response.cookies)\n # print(response.text)\n if (response.text.find(self.mobile) > -1):\n self.is_login = True\n self.cookies = response.cookies._cookies.get('videorank.cn').get('/')\n\n def get_captcha_code(self, file_name):\n pd_id = FATEADM_API_MAP.get('pd_id') # 用户中心页可以查询到pd信息\n pd_key = FATEADM_API_MAP.get('pd_key')\n app_id = FATEADM_API_MAP.get('app_id') # 开发者分成用的账号，在开发者中心可以查询到\n app_key = FATEADM_API_MAP.get('app_key')\n # 识别类型，\n # 具体类型可以查看官方网站的价格页选择具体的类型，不清楚类型的，可以咨询客服\n pred_type = '30400'\n api = FateadmApi(app_id, app_key, pd_id, pd_key)\n rsp = api.PredictFromFile(pred_type, file_name)\n if (rsp != None and rsp.ret_code == 0):\n # print(rsp.pred_rsp)\n return rsp.pred_rsp\n\n def main(self):\n self.login()\n if (self.is_login):\n cookies = self.get_cookies()\n return {\n 'status': 1,\n 'content': cookies\n }\n else:\n return {\n 'status': 3,\n 'content': '登录失败'\n }\n\n def get_cookies(self):\n array = []\n for x, y in self.cookies.items():\n dict = {}\n dict['name'] = x\n dict['value'] = y.value\n array.append(dict)\n return array\n\n\nif __name__ == '__main__':\n VideoRankCookies('', '').main();", "sub_path": "login/videorank/cookies.py", "file_name": "cookies.py", "file_ext": "py", "file_size_in_byte": 3549, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "requests.get", "line_number": 22, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 26, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 41, "usage_type": "call"}, {"api_name": "uuid.uuid1", "line_number": 43, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 48, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 50, "usage_type": "call"}, {"api_name": "login.videorank.feteadm_api.FateadmApi", "line_number": 66, "usage_type": "call"}]} +{"seq_id": "505629374", "text": "#coding:utf-8\n\"\"\"olnote 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.urls import url,include\nfrom django.contrib import admin\nfrom blog import views\nfrom blog import models\n\nurlpatterns = [\n url(r'^admin/', admin.site.urls),\n url(r\"^discuss/$\",views.discuss),#,{'template':'user/comment.html'}\n url(r\"^discuss/(?P\\w+)/(?P\\d+)/$\",views.discuss_mailto),#,{'template':'user/comment.html'}\n url(r\"^discuss/$\",views.discuss),#,{'template':'user/comment.html'}\n url(r'^$',views.MyView.as_view()),\n url(r\"^tags/(?Pt_\\d+)/$\",views.MyView.as_view()),\n url(r\"^(?P\\w+?)/(?P.+?)/(?P.+?)/\",views.MyView.as_view()),\n url(r\"^register/$\",views.register,{'template':'user/register.html'}),\n url(r\"^login/$\",views.login,{'template':'user/login.html'}),\n url(r\"^forget/(?P.+?)/$\",views.forget,{'template':\"user/forget.html\"}),\n url(r\"^comment/$\",views.comment),\n\n]\n", "sub_path": "olnote/olnote/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1525, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.conf.urls.url", "line_number": 23, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 23, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 23, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 24, "usage_type": "call"}, {"api_name": "blog.views.discuss", "line_number": 24, "usage_type": "attribute"}, {"api_name": "blog.views", "line_number": 24, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 25, "usage_type": "call"}, {"api_name": "blog.views.discuss_mailto", "line_number": 25, "usage_type": "attribute"}, {"api_name": "blog.views", "line_number": 25, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 26, "usage_type": "call"}, {"api_name": "blog.views.discuss", "line_number": 26, "usage_type": "attribute"}, {"api_name": "blog.views", "line_number": 26, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 27, "usage_type": "call"}, {"api_name": "blog.views.MyView.as_view", "line_number": 27, "usage_type": "call"}, {"api_name": "blog.views.MyView", "line_number": 27, "usage_type": "attribute"}, {"api_name": "blog.views", "line_number": 27, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 28, "usage_type": "call"}, {"api_name": "blog.views.MyView.as_view", "line_number": 28, "usage_type": "call"}, {"api_name": "blog.views.MyView", "line_number": 28, "usage_type": "attribute"}, {"api_name": "blog.views", "line_number": 28, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 29, "usage_type": "call"}, {"api_name": "blog.views.MyView.as_view", "line_number": 29, "usage_type": "call"}, {"api_name": "blog.views.MyView", "line_number": 29, "usage_type": "attribute"}, {"api_name": "blog.views", "line_number": 29, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 30, "usage_type": "call"}, {"api_name": "blog.views.register", "line_number": 30, "usage_type": "attribute"}, {"api_name": "blog.views", "line_number": 30, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 31, "usage_type": "call"}, {"api_name": "blog.views.login", "line_number": 31, "usage_type": "attribute"}, {"api_name": "blog.views", "line_number": 31, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 32, "usage_type": "call"}, {"api_name": "blog.views.forget", "line_number": 32, "usage_type": "attribute"}, {"api_name": "blog.views", "line_number": 32, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 33, "usage_type": "call"}, {"api_name": "blog.views.comment", "line_number": 33, "usage_type": "attribute"}, {"api_name": "blog.views", "line_number": 33, "usage_type": "name"}]} +{"seq_id": "372133424", "text": "import os\nimport argparse\nimport pickle as pkl\nimport numpy as np\nfrom collections import Counter, defaultdict\nfrom tqdm import tqdm\nfrom utils.clean import get_chinese\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument('--input', type=str, default='data/graph/ownthink.pkl')\n parser.add_argument('--item_list_path', type=str,\n default='data/graph/item.txt')\n parser.add_argument('--instance_path', type=str, default='data/emotion/')\n args = parser.parse_args()\n\n vocab = {'': 0}\n item_to_node_list = {}\n with open(args.item_list_path) as f:\n for line in f.readlines():\n item_id_hash, title = line.split('\\t')\n keywords = title.split()\n item_to_node_list[item_id_hash] = keywords\n for keyword in keywords:\n if keyword not in vocab:\n vocab[keyword] = len(vocab)\n print(len(vocab))\n\n ownthink = pkl.load(open(args.input, 'rb'))\n ownthink = dict([(k, ownthink[k])\n for k in ownthink\n if ownthink[k]['message'] != 'error' and '请求异常' not in ownthink[k]['data']])\n abstract = dict([(k, get_chinese(ownthink[k]['data']['desc'])[:50])\n for k in ownthink\n if len(get_chinese(ownthink[k]['data']['desc'])) > 10])\n print(len(abstract))\n\n # map supporting fact chars to target vocabulary\n fact_dict = {}\n with open(os.path.join(args.instance_path, 'preprocessed/tgt.dict')) as f:\n for i, word in enumerate(f.read().strip().split('\\n')):\n fact_dict[word.split()[0]] = i\n abstract_id = defaultdict(list)\n for k in abstract:\n for x in list(abstract[k]):\n abstract_id[k].append(\n fact_dict[x] if x in fact_dict else fact_dict[''])\n\n # Calculating document frequency for sampling\n df = defaultdict(int)\n N = len(item_to_node_list)\n for itemid in item_to_node_list:\n node_list = [node for node in item_to_node_list[itemid]\n if node in abstract]\n for node in set(node_list):\n df[node] += 1\n\n def sample_supporting_fact(node_list):\n node_list = [node for node in node_list if node in abstract]\n tfidf = []\n nodes = []\n for node in set(node_list):\n # Discard rare words\n if df[node] > 5 and len(node) > 1:\n tfidf.append(node_list.count(node) * np.log(N / df[node]))\n nodes.append(node)\n # print(np.random.choice(nodes, size=2, replace=False, p=tfidf / np.sum(tfidf)), nodes)\n if nodes:\n if len(nodes) > 4:\n sampled = sorted(np.random.choice(len(nodes), 4, replace=False, p=tfidf / np.sum(tfidf)))\n sampled = [nodes[idx] for idx in sampled]\n else:\n sampled = nodes\n else:\n # Sample one word from the item title and use its supporting fact\n sampled = [np.random.choice(list(abstract.keys()))]\n return sampled\n\n # for itemid in item_to_node_list:\n # node_list = item_to_node_list[itemid]\n\n splits = ['valid', 'test', 'train']\n for split in splits:\n with open(os.path.join(args.instance_path, split + '.hash_id')) as f:\n instances_hashids = f.read().strip().split('\\n')\n with open(os.path.join(args.instance_path, split + '.src')) as f:\n instance_srcs = f.read().strip().split('\\n')\n with open(os.path.join(args.instance_path, split + '.tgt')) as f:\n instance_tgts = f.read().strip().split('\\n')\n with open(os.path.join(args.instance_path, split + '.supporting_facts'), 'w') as f, open(os.path.join(args.instance_path, split + '.supporting_facts_str'), 'w') as f_str:\n for src, tgt, hashid in tqdm(zip(instance_srcs, instance_tgts, instances_hashids)):\n if src != \"\" and tgt != \"\":\n sampled_list = sample_supporting_fact(item_to_node_list[hashid]) \n for sampled in sampled_list:\n f.write(' '.join(map(str, abstract_id[sampled])) + ' ')\n f_str.write(' '.join(abstract[sampled]) + ' ')\n f.write('\\n')\n f_str.write('\\n')\n", "sub_path": "utils/process_ownthink.py", "file_name": "process_ownthink.py", "file_ext": "py", "file_size_in_byte": 4320, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 10, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 29, "usage_type": "call"}, {"api_name": "utils.clean.get_chinese", "line_number": 33, "usage_type": "call"}, {"api_name": "utils.clean.get_chinese", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 43, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 70, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 76, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path", "line_number": 90, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 91, "usage_type": "call"}]} +{"seq_id": "55458618", "text": "from os import path, listdir\nimport pathlib\npasta = str(pathlib.Path().absolute())\n\ncaminhos = [path.join(pasta, nome) for nome in listdir(pasta)]\narquivos = [arq for arq in caminhos if path.isfile(\n arq) and arq.lower().endswith(\".png\")]\npngs = [arq for arq in arquivos if arq.lower().endswith(\".png\")]\narquivos_nomes = [arq[73:] for arq in arquivos]\nprint(len(arquivos_nomes))\nprint(arquivos_nomes)", "sub_path": "App/Desenvolvimento/target/Alfaedu/imagens_name.py", "file_name": "imagens_name.py", "file_ext": "py", "file_size_in_byte": 403, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pathlib.Path", "line_number": 3, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "name"}]} +{"seq_id": "357581756", "text": "from collections import defaultdict\nimport io\nimport os\n\nimport ffmpeg\n\nfrom django.core.exceptions import ObjectDoesNotExist, ValidationError\nfrom django.core.files.uploadedfile import InMemoryUploadedFile\nfrom django.db import models\nfrom django.db.models.fields.files import FileField\nfrom django.dispatch import receiver\n\n\nclass WAVAudioField(FileField):\n description = \"WAV File\"\n _registry = defaultdict(list)\n supported_upload_formats = ['wav', 'mp3', 'ogg']\n\n def __init__(self, max_vid_length=None, max_upload_size=None, **kwargs):\n \"\"\"\n max_upload_size: controls how large the upload file\n can be (before it's trimmed/converted)\n max_vid_length: controls the length that the audio file\n will be trimmed to before saving\n \"\"\"\n self.max_upload_size = max_upload_size # in MB\n self.max_vid_length = max_vid_length # in seconds\n super().__init__(**kwargs)\n\n def deconstruct(self):\n name, path, args, kwargs = super().deconstruct()\n if self.max_vid_length:\n kwargs['max_vid_length'] = self.max_vid_length\n if self.max_upload_size:\n kwargs['max_upload_size'] = self.max_upload_size\n return name, path, args, kwargs\n\n def contribute_to_class(self, cls, name, **kwargs):\n super().contribute_to_class(cls, name, **kwargs)\n self._registry[cls].append(self.attname)\n\n def clean(self, fieldfile, model_instance):\n if self.max_upload_size:\n if fieldfile.size > (self.max_upload_size << 20):\n raise ValidationError(\n f'audio file is too large. Please upload '\n f'files below {self.max_upload_size}MB'\n )\n try:\n cls = model_instance.__class__\n prev_instance = cls.objects.get(id=model_instance.id)\n prev_fieldfile = getattr(prev_instance, self.attname)\n if prev_fieldfile == fieldfile:\n return fieldfile # nothing to update\n except ObjectDoesNotExist:\n prev_fieldfile = None\n\n path = fieldfile.name\n\n ext = os.path.splitext(path)[1]\n if not ext:\n raise ValidationError('audio file must have extension')\n\n ext = ext[1:] # remove the dot\n if ext not in self.supported_upload_formats:\n raise ValidationError(\n f'{ext} is not a supported upload format '\n f'({\", \".join(self.supported_upload_formats)})'\n )\n\n filename = f'{model_instance.__class__.__name__}.wav'\n self._reformat_audio(fieldfile, ext, filename)\n\n if prev_fieldfile:\n # only delete previous file after reformat_audio\n # has returned safely\n prev_fieldfile.delete(save=False)\n\n return fieldfile\n\n def _reformat_audio(self, fieldfile, from_format, filename):\n \"\"\"\n resize an audio file and convert to .wav before saving\n \"\"\"\n if not hasattr(fieldfile, 'file') or fieldfile.file is None:\n print('returning None')\n return None\n with fieldfile.file.open(mode='rb') as f:\n try:\n kwargs = {}\n if self.max_vid_length:\n kwargs['t'] = self.max_vid_length\n out, err = (\n ffmpeg.input('pipe:', format=from_format)\n .output('pipe:',\n ac=1,\n format='wav',\n **kwargs)\n .run(input=f.read(), quiet=True)\n )\n except ffmpeg.Error:\n raise ValidationError('error reading audio file')\n\n stream = io.BytesIO(out)\n fieldfile.name = filename\n fieldfile.file = InMemoryUploadedFile(\n file=stream,\n name=fieldfile.name,\n content_type='audio/wave',\n field_name=self.attname,\n charset=None,\n size=stream.tell()\n )\n return fieldfile\n\n\n@receiver(models.signals.post_delete)\ndef clean_up_wavs(sender, instance, **kwargs):\n \"\"\"\n delete the jpeg files managed by a JPEGImageField when an\n instance is deleted. Note that this won't run when just the\n audio file is deleted, but not the model instance.\n \"\"\"\n if sender in WAVAudioField._registry:\n for attname in WAVAudioField._registry[sender]:\n audio = getattr(instance, attname)\n if audio:\n audio.delete(save=False)\n\n", "sub_path": "web/apps/custom/fields/audio.py", "file_name": "audio.py", "file_ext": "py", "file_size_in_byte": 4660, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.db.models.fields.files.FileField", "line_number": 14, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 16, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 45, "usage_type": "call"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 55, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 62, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 66, "usage_type": "call"}, {"api_name": "ffmpeg.input", "line_number": 94, "usage_type": "call"}, {"api_name": "ffmpeg.Error", "line_number": 101, "usage_type": "attribute"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 102, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 104, "usage_type": "call"}, {"api_name": "django.core.files.uploadedfile.InMemoryUploadedFile", "line_number": 106, "usage_type": "call"}, {"api_name": "django.dispatch.receiver", "line_number": 117, "usage_type": "call"}, {"api_name": "django.db.models.signals", "line_number": 117, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 117, "usage_type": "name"}]} +{"seq_id": "276593729", "text": "from rest_framework import serializers\n\nfrom etu.models import Patient\n\n\nclass PatientSerializer(serializers.HyperlinkedModelSerializer):\n class Meta:\n model = Patient\n fields = ('uid', 'first_name', 'last_name', 'enter_number', \n 'caregiver_number', 'age', 'geolocation', 'etu', 'alive',\n 'json')\n", "sub_path": "core/api/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 349, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "rest_framework.serializers.HyperlinkedModelSerializer", "line_number": 6, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 6, "usage_type": "name"}, {"api_name": "etu.models.Patient", "line_number": 8, "usage_type": "name"}]} +{"seq_id": "78373151", "text": "#coding=utf-8\nfrom Crypto.Cipher import AES\nfrom Crypto.Hash import SHA256\nimport os\nimport pickle\nimport utils.logger as logger\nimport base64\n\n\ndef get_IV_key():\n try:\n IV='$j8Kww4PCncKZwpx'\n key = get_key(\"g3nCsMKgw&\")\n except:\n logger.logger().exception(\"set_config:get_IV_key\")\n finally:\n return IV, key\n\ndef get_key(password): \n hasher = None\n try:\n hasher = SHA256.new(password.encode('utf-8'))\n except:\n logger.logger().exception(\"set_config:get_key\")\n finally:\n return hasher.digest()\n\n\ndef encode(key, clear):\n enc = []\n for i in range(len(clear)):\n key_c = key[i % len(key)]\n enc_c = chr((ord(clear[i]) + ord(key_c)) % 256)\n enc.append(enc_c)\n return base64.urlsafe_b64encode(\"\".join(enc).encode()).decode()\n\n\ndef decode(key, enc):\n dec = []\n enc = base64.urlsafe_b64decode(enc).decode()\n for i in range(len(enc)):\n key_c = key[i % len(key)]\n dec_c = chr((256 + ord(enc[i]) - ord(key_c)) % 256)\n dec.append(dec_c)\n return \"\".join(dec)\n\n\ndef encrypt_text(plaintext, key, IV):\n ciphertext=None\n try:\n print(\"encrypt_text(plaintext, key, IV)\")\n \n encryptor = AES.new(key, AES.MODE_CBC, IV)\n text_size = len(plaintext)\n if text_size%16!=0:\n plaintext += ' '*(16-text_size%16)\n ciphertext = encryptor.encrypt(plaintext) \n except:\n logger.logger().exception(\"set_config:encrypt_text\")\n finally:\n return ciphertext\n\n\ndef decrypt_text(ciphertext, key, IV):\n try:\n print(\"decrypt_text(ciphertext, key, IV)\")\n decryptor = AES.new(key, AES.MODE_CBC, IV)\n plaintext = decryptor.decrypt(ciphertext)\n plaintext = plaintext.split()[0].decode('utf-8')\n except:\n logger.logger().exception(\"set_config:decrypt_text\")\n finally:\n return plaintext\n \n\n \ndef get_item(src_file, item_key, key, IV):\n ciphertext=None\n try:\n with open(src_file, 'rb') as handle:\n dest_dict = pickle.load(handle)\n ciphertext = dest_dict.get(item_key)\n \n except:\n logger.logger().exception(\"set_config:get_item\")\n finally:\n if ciphertext:\n item_value = decrypt_text(ciphertext, key, IV)\n return item_value\n else:\n return ''\n \n \ndef set_item(src_file, item_key, item_value, key, IV):\n try:\n if os.path.isfile(src_file):\n with open(src_file, 'rb') as handle:\n config_dict = pickle.load(handle)\n else:\n config_dict = {}\n item_value = encrypt_text(item_value, key, IV)\n config_dict[item_key] = item_value\n with open(src_file, 'wb') as handle:\n pickle.dump(config_dict, handle)\n except:\n logger.logger().exception(\"set_config:set_item\")\n\n \ndef init_config(src_file): \n IV, key = get_IV_key()\n set_item(src_file, \"TIMES\", '0', key, IV)\n item_value = get_item(src_file, \"TIMES\", key, IV)\n print(item_value)\n\n\n \nif __name__=='__main__':\n\n src_file = os.path.expanduser('~')+'/.QtsConfigs'\n IV, key = get_IV_key()\n\n set_item(src_file, \"TIMES\", '0', key, IV)\n item_value = get_item(src_file, \"TIMES\", key, IV)\n print(item_value)\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n\n\n", "sub_path": "src/utils/set_config.py", "file_name": "set_config.py", "file_ext": "py", "file_size_in_byte": 3372, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "utils.logger.logger", "line_number": 15, "usage_type": "call"}, {"api_name": "utils.logger", "line_number": 15, "usage_type": "name"}, {"api_name": "Crypto.Hash.SHA256.new", "line_number": 22, "usage_type": "call"}, {"api_name": "Crypto.Hash.SHA256", "line_number": 22, "usage_type": "name"}, {"api_name": "utils.logger.logger", "line_number": 24, "usage_type": "call"}, {"api_name": "utils.logger", "line_number": 24, "usage_type": "name"}, {"api_name": "base64.urlsafe_b64encode", "line_number": 35, "usage_type": "call"}, {"api_name": "base64.urlsafe_b64decode", "line_number": 40, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES.new", "line_number": 53, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES", "line_number": 53, "usage_type": "name"}, {"api_name": "Crypto.Cipher.AES.MODE_CBC", "line_number": 53, "usage_type": "attribute"}, {"api_name": "utils.logger.logger", "line_number": 59, "usage_type": "call"}, {"api_name": "utils.logger", "line_number": 59, "usage_type": "name"}, {"api_name": "Crypto.Cipher.AES.new", "line_number": 67, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES", "line_number": 67, "usage_type": "name"}, {"api_name": "Crypto.Cipher.AES.MODE_CBC", "line_number": 67, "usage_type": "attribute"}, {"api_name": "utils.logger.logger", "line_number": 71, "usage_type": "call"}, {"api_name": "utils.logger", "line_number": 71, "usage_type": "name"}, {"api_name": "pickle.load", "line_number": 81, "usage_type": "call"}, {"api_name": "utils.logger.logger", "line_number": 85, "usage_type": "call"}, {"api_name": "utils.logger", "line_number": 85, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path", "line_number": 96, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 98, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 104, "usage_type": "call"}, {"api_name": "utils.logger.logger", "line_number": 106, "usage_type": "call"}, {"api_name": "utils.logger", "line_number": 106, "usage_type": "name"}, {"api_name": "os.path.expanduser", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path", "line_number": 119, "usage_type": "attribute"}]} +{"seq_id": "167770541", "text": "\nimport numpy as np\nimport pickle\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\n\n# script input\nspecies = 'Bpa'\n\n# load MCMC output and thin\nts = pickle.load(open('../Data/UMB_trace/Bpa.pickle', 'rb'))\nts['alpha'] = 10**ts['alpha_log10']\nts['kxmax'] = 10**ts['kxmax_log10']\n\nlatex = [r'$b$', r'$c$', '$\\\\psi_{x50}$',\\\n r'$k_{xmax} / g_1$', r'$k_{xmax} / \\alpha$']\ndf_thinned = {}\nfor key in ts:\n #print(np.median(ts[key]))\n df_thinned[key] = [item for index, item in enumerate(ts[key])\\\n if index % 10 == 0]\ndf_thinned = pd.DataFrame.from_dict(data=df_thinned, orient='columns')\ndf_thinned['par1'] = df_thinned.kxmax/df_thinned.g1\ndf_thinned['par2'] = df_thinned.kxmax/df_thinned.alpha\ndf_thinned = df_thinned.drop(['alpha_log10', 'kxmax_log10', 'kxmax', 'g1',\\\n 'alpha'], axis=1)\ndf_thinned.rename(columns=dict(zip(df_thinned.columns, latex)), inplace=True)\n\n# figure\nsns.set(font_scale=1)\ng = sns.PairGrid(data=df_thinned, vars=latex, diag_sharey=False)\ng.fig.set_size_inches(12, 6)\ng = g.map_lower(plt.scatter, s=1)\ng = g.map_diag(plt.hist)\n#g = g.map_diag(sns.kdeplot, lw=3, legend=False)\n#g = g.map_upper(plt.scatter, s=1)\ndef hide_current_axis(*args, **kwds):\n plt.gca().set_visible(False)\ng.map_upper(hide_current_axis)\ng.savefig('../Figures/Figure correlation Bpa.png', bbox_inches=\"tight\")\n", "sub_path": "Anderegg/Figure_correlation.py", "file_name": "Figure_correlation.py", "file_ext": "py", "file_size_in_byte": 1395, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pickle.load", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 23, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 23, "usage_type": "attribute"}, {"api_name": "seaborn.set", "line_number": 31, "usage_type": "call"}, {"api_name": "seaborn.PairGrid", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 34, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 35, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}]} +{"seq_id": "379177983", "text": "#\n# ColorHandPose3DNetwork - Network for estimating 3D Hand Pose from a single RGB Image\n# Copyright (C) 2017 Christian Zimmermann\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# 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 .\n#\nfrom __future__ import print_function, unicode_literals\n\nimport tensorflow as tf\nimport numpy as np\nimport scipy.misc\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom easydict import EasyDict as edict\nfrom data.STB_dataset import STBdataset\nfrom data.RHD_dataset import RHDdataset\nfrom nets.ColorHandPose3DNetwork import ColorHandPose3DNetwork\nfrom utils.general import EvalUtil, detect_keypoints, trafo_coords, plot_hand, plot_hand_3d\n\nif __name__ == '__main__':\n # images to be shown\n opt = edict()\n opt.dataroot = \"./datasets/stb-dataset/test\"\n opt.isTrain = False\n data = STBdataset(opt)\n # opt.dataroot = \"./datasets/rhd-dataset/test\"\n # opt.isTrain = False\n # data = RHDdataset(opt)\n\n # network input\n image_tf = tf.placeholder(tf.float32, shape=(1, 256, 256, 3))\n hand_side_tf = tf.constant([[1.0, 0.0]]) # left hand (true for all samples provided)\n evaluation = tf.placeholder_with_default(True, shape=())\n\n # build network\n net = ColorHandPose3DNetwork()\n hand_scoremap_tf, image_crop_tf, scale_tf, center_tf, \\\n keypoints_scoremap_tf, keypoint_coord3d_tf = net.inference_crop(image_tf, hand_side_tf, evaluation)\n # Start TF\n gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)\n sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))\n\n # initialize network\n net.init(sess)\n\n # Feed image list through network\n eval2d = EvalUtil()\n eval3d = EvalUtil()\n import tqdm\n import cv2\n\n for i in tqdm.tqdm(range(len(data))):\n sample = data[i]\n image_v = sample['image']\n\n hand_scoremap_v, image_crop_v, scale_v, center_v, \\\n keypoints_scoremap_v, keypoint_coord3d_v = sess.run([hand_scoremap_tf, image_crop_tf, scale_tf, center_tf,\n keypoints_scoremap_tf, keypoint_coord3d_tf],\n feed_dict={image_tf: image_v})\n\n hand_scoremap_v = np.squeeze(hand_scoremap_v)\n image_crop_v = np.squeeze(image_crop_v)\n keypoints_scoremap_v = np.squeeze(keypoints_scoremap_v)\n keypoint_coord3d_v = np.squeeze(keypoint_coord3d_v) * sample['keypoint_scale']\n keypoint_xyz21_gt = np.squeeze(sample['keypoint_xyz21'])\n keypoint_xyz21_gt -= keypoint_xyz21_gt[0]\n keypoint_uv_gt = np.squeeze(sample['keypoint_uv21'])\n # post processing\n image_crop_v = ((image_crop_v + 0.5) * 255).astype('uint8')\n coord_hw_pred_crop = detect_keypoints(np.squeeze(keypoints_scoremap_v))\n coord_uv_pred_crop = np.stack([coord_hw_pred_crop[:, 1], coord_hw_pred_crop[:, 0]], 1)\n\n kp_vis2d = np.ones_like(keypoint_uv_gt[:, 0])\n kp_vis = np.ones_like(keypoint_xyz21_gt[:, 0])\n eval2d.feed(keypoint_uv_gt, kp_vis2d, coord_uv_pred_crop)\n eval3d.feed(keypoint_xyz21_gt, kp_vis, keypoint_coord3d_v)\n\n # for i, kp in enumerate(keypoint_uv_gt):\n # kp = tuple(kp.astype(np.uint8))\n # image_crop_v = cv2.circle(image_crop_v, kp, 2, [255,255,255], 2)\n # image_crop_v = cv2.putText(image_crop_v, str(i),\n # kp,cv2.FONT_HERSHEY_SIMPLEX, 0.5,\n # [0,255,0])\n\n # import pdb; pdb.set_trace()\n # cv2.imwrite(\"test.png\", image_crop_v)\nmean, median, auc, _, _ = eval3d.get_measures(0.0, 0.050, 20)\nprint('Evaluation results for 3d ')\nprint('Average mean EPE: %.3f mm' % (mean * 1000))\nprint('Average median EPE: %.3f mm' % (median * 1000))\nprint('Area under curve: %.3f' % auc)\n\nmean, median, auc, _, _ = eval2d.get_measures(0.0, 30.0, 20)\nprint('Evaluation results 2d :')\nprint('Average mean EPE: %.3f pixels' % mean)\nprint('Average median EPE: %.3f pixels' % median)\nprint('Area under curve: %.3f' % auc)\n", "sub_path": "hand_pose_estimators/CVPR2020_hand3d/CVPR2020_eval_full.py", "file_name": "CVPR2020_eval_full.py", "file_ext": "py", "file_size_in_byte": 4641, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "easydict.EasyDict", "line_number": 33, "usage_type": "call"}, {"api_name": "data.STB_dataset", "line_number": 36, "usage_type": "name"}, {"api_name": "data.STB_dataset.STBdataset", "line_number": 36, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 42, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 42, "usage_type": "attribute"}, {"api_name": "tensorflow.constant", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.placeholder_with_default", "line_number": 44, "usage_type": "call"}, {"api_name": "nets.ColorHandPose3DNetwork.ColorHandPose3DNetwork", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.GPUOptions", "line_number": 51, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.ConfigProto", "line_number": 52, "usage_type": "call"}, {"api_name": "utils.general.EvalUtil", "line_number": 58, "usage_type": "call"}, {"api_name": "utils.general.EvalUtil", "line_number": 59, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 63, "usage_type": "call"}, {"api_name": "data.STB_dataset", "line_number": 63, "usage_type": "argument"}, {"api_name": "data.STB_dataset", "line_number": 64, "usage_type": "name"}, {"api_name": "numpy.squeeze", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 78, "usage_type": "call"}, {"api_name": "utils.general.detect_keypoints", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.ones_like", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.ones_like", "line_number": 85, "usage_type": "call"}]} +{"seq_id": "309202648", "text": "# Forwarder high-level functions\nimport datetime\nimport json\nimport logging\nimport config\nfrom forwarder import client\nfrom forwarder import utility\n\nlogger = logging.getLogger(__name__)\n\n# get user autorization\ndef user_authorization():\n have_authorization = False\n logger.debug(\"Starting user authorization\")\n\n while not have_authorization:\n event = client.td_receive()\n if event:\n # process authorization states\n if event[\"@type\"] == \"updateAuthorizationState\":\n auth_state = event[\"authorization_state\"]\n\n # if client is closed, we need to destroy it and create new client\n if auth_state[\"@type\"] == \"authorizationStateClosed\":\n utility.log_api_error(\"authorizationStateClosed\", logging.CRITICAL)\n break\n\n # set TDLib parameters\n # you MUST obtain your own api_id and api_hash at https://my.telegram.org\n # and use them in the setTdlibParameters call\n if auth_state[\"@type\"] == \"authorizationStateWaitTdlibParameters\":\n client.td_send(\n {\n \"@type\": \"setTdlibParameters\",\n \"parameters\": {\n \"database_directory\": config.CLIENT[\n \"database_directory\"\n ],\n \"use_message_database\": config.CLIENT[\n \"use_message_database\"\n ],\n \"use_secret_chats\": config.CLIENT[\"use_secret_chats\"],\n \"api_id\": config.CLIENT[\"api_id\"],\n \"api_hash\": config.CLIENT[\"api_hash\"],\n \"system_language_code\": config.CLIENT[\n \"system_language\"\n ],\n \"device_model\": config.CLIENT[\"device_model\"],\n \"application_version\": config.CLIENT[\"app_version\"],\n \"enable_storage_optimizer\": config.CLIENT[\n \"enable_storage_optimizer\"\n ],\n },\n }\n )\n\n # set an encryption key for database to let know TDLib how to open the database\n if auth_state[\"@type\"] == \"authorizationStateWaitEncryptionKey\":\n client.td_send(\n {\"@type\": \"checkDatabaseEncryptionKey\", \"encryption_key\": \"\"}\n )\n\n # enter phone number to log in\n if auth_state[\"@type\"] == \"authorizationStateWaitPhoneNumber\":\n phone_number = input(\"Please enter your phone number: \")\n client.td_send(\n {\n \"@type\": \"setAuthenticationPhoneNumber\",\n \"phone_number\": phone_number,\n }\n )\n\n # wait for authorization code\n if auth_state[\"@type\"] == \"authorizationStateWaitCode\":\n code = input(\"Please enter the authentication code you received: \")\n client.td_send({\"@type\": \"checkAuthenticationCode\", \"code\": code})\n\n # wait for first and last name for new users\n if auth_state[\"@type\"] == \"authorizationStateWaitRegistration\":\n first_name = input(\"Please enter your first name: \")\n last_name = input(\"Please enter your last name: \")\n client.td_send(\n {\n \"@type\": \"registerUser\",\n \"first_name\": first_name,\n \"last_name\": last_name,\n }\n )\n\n # wait for password if present\n if auth_state[\"@type\"] == \"authorizationStateWaitPassword\":\n password = input(\"Please enter your password: \")\n client.td_send(\n {\"@type\": \"checkAuthenticationPassword\", \"password\": password}\n )\n\n if auth_state[\"@type\"] == \"authorizationStateReady\":\n have_authorization = True\n logger.info(\"User authorized\")\n\n if event[\"@type\"] == \"error\":\n utility.log_api_error(event, logging.ERROR)\n\n return have_authorization\n\n\n# start listening new messages to forward\ndef update_new_messages():\n try:\n rules_file = open(config.FORWARDER[\"rules_path\"])\n rules = json.load(rules_file)\n # messages queue\n messages = []\n # chrono\n start_update_time = datetime.datetime.now()\n forwarded = 0\n\n print(\"Listening to new messages...\")\n logger.info(\"Listening to new messages...\")\n while True:\n recently_added = False\n event = client.td_receive()\n\n # event is not empty\n if event:\n # wait for a new message\n if event[\"@type\"] == \"updateNewMessage\":\n message = event[\"message\"]\n for rule in rules[\"forward\"]:\n # if the message from chat_id is in file\n if message[\"chat_id\"] == rule[\"from_chat\"]:\n message_forward = {\n \"rule_id\": rule[\"id\"],\n \"message_id\": [message[\"id\"]],\n }\n # append the message to the queue\n messages.append(message_forward)\n logger.debug(\n \"Message published at: {}, appended to the queue\".format(\n utility.convert_unix_to_datetime(message[\"date\"])\n )\n )\n recently_added = True\n\n if event[\"@type\"] == \"error\":\n # log the error\n utility.log_api_error(event, logging.ERROR)\n\n # proccess queue messages every 2 seconds\n now = datetime.datetime.now()\n difference_seconds = int((now - start_update_time).total_seconds())\n\n if difference_seconds % config.FORWARDER[\"periodicity_fwd\"] == 0:\n # only execute this once\n if forwarded < difference_seconds:\n # if a message was added in this iteration\n if recently_added:\n logger.warning(\n \"A recent message was added to the queue, skipping to next iteration\"\n )\n # continue to the next iteration\n continue\n\n # there are messages to proccess\n if len(messages) > 0:\n logger.debug(\"Processing message queue\")\n # proccess stored messages\n proccess_messages(messages, rules)\n # clear queue of messages\n messages.clear()\n logger.debug(\"Message queue processed and cleared\")\n # updates forwarded state\n forwarded = difference_seconds\n\n except KeyboardInterrupt:\n rules_file.close()\n logger.info(\"Listening to messages stopped: interrupted by user\")\n\n\n# forward stored messages in queue\ndef proccess_messages(messages, rules):\n grouped_messages = utility.group_message_id(messages)\n logger.debug(\"Message/s grouped by message_id\")\n for message in grouped_messages:\n for rule in rules[\"forward\"]:\n if message[\"rule_id\"] == rule[\"id\"]:\n # variables\n chat_id = rule[\"to_chat\"]\n from_chat_id = rule[\"from_chat\"]\n message_id = message[\"message_id\"]\n options = rule[\"options\"]\n send_copy = rule[\"send_copy\"]\n remove_caption = rule[\"remove_caption\"]\n # forward messages\n client.forward_message(\n chat_id,\n from_chat_id,\n message_id,\n options,\n send_copy,\n remove_caption,\n )\n # log action\n utility.log_api_action(chat_id, from_chat_id, message_id)\n\n\n# get chat from an id\ndef get_chat(chat_id):\n print(\"Getting chat...\")\n logging.info(\"Getting chat by id\")\n\n if chat_id:\n chat_list = {\"chat_list\": []}\n # send request to the Telegram API\n client.td_send({\"@type\": \"getChat\", \"chat_id\": chat_id})\n while True:\n event = client.td_receive()\n if event:\n if event[\"@type\"] == \"updateNewChat\":\n chat_list[\"chat_list\"].append(event)\n break\n # write in the events file\n utility.write_events(chat_list)\n logger.info(\"Got requested chat, check log/events.json\")\n else:\n print(\"No id entered\")\n\n\n# get chats from main chat list\ndef get_chats(number_chats):\n print(\"Getting main chat list...\")\n logging.info(\"Getting main chat list\")\n\n limit_chats = number_chats or config.FORWARDER[\"limit_chats\"]\n chat_list = {\"chat_list\": []}\n # send request to the Telegram API\n client.td_send({\"@type\": \"getChats\", \"limit\": limit_chats})\n while len(chat_list[\"chat_list\"]) < limit_chats:\n event = client.td_receive()\n if event:\n if event[\"@type\"] == \"updateNewChat\":\n chat_list[\"chat_list\"].append(event)\n\n # write in the events file\n utility.write_events(chat_list)\n logger.info(\"Got main chat list, check log/events.json\")\n\n\n# listen all requests/updates and writes in a file (for debugging)\ndef listen():\n try:\n print(\"Listening all requests/updates...\")\n logger.debug(\"Listening all requests/updates\")\n event_list = []\n\n while True:\n event = client.td_receive()\n if event:\n event_list.append(event)\n print(event)\n\n except KeyboardInterrupt:\n utility.write_events(event_list)\n logger.info(\"Listening all requests/updates stopped: interrupted by user\")\n\n\n# main method\ndef start(argument=None):\n # start the client by sending request to it\n client.td_send({\"@type\": \"getAuthorizationState\", \"@extra\": 1.01234})\n # get authorization\n authorized = user_authorization()\n if authorized:\n try:\n if argument is None:\n is_closed = False\n while not is_closed:\n command = input(\n \"-> Enter command:\\nfwd - Start listening to new messages for forwarding\\ngcs - Get all chats from main chat list\\ngc - Get chat by its id\\nl - Listen all updates/requests (for debugging)\\nq - Quit program\\n\\n\"\n )\n if command == \"fwd\":\n update_new_messages()\n elif command == \"gcs\":\n number_chats = input(\n \"Enter the chats you want to retrieve ({} chats by default): \".format(\n config.FORWARDER[\"limit_chats\"]\n )\n )\n if number_chats:\n number_chats = int(number_chats)\n else:\n number_chats = 0\n\n get_chats(number_chats)\n elif command == \"gc\":\n chat_id = input(\"Enter the chat id: \")\n get_chat(chat_id)\n elif command == \"l\":\n listen()\n elif command == \"q\":\n print(\"Stopping the execution...\")\n is_closed = True\n else:\n pass\n elif argument == \"fwd\":\n update_new_messages()\n elif argument == \"l\":\n listen()\n else:\n print(\n \"\\nList of available arguments: \\nfwd - Start listening to new messages for forwarding\\nl - Listen all updates/requests (for testing)\\n\"\n )\n except KeyboardInterrupt:\n logger.info(\"ForwarderApp stopped: interrupted by user\")\n else:\n logger.error(\"User not authorized\")", "sub_path": "forwarder/forwarder.py", "file_name": "forwarder.py", "file_ext": "py", "file_size_in_byte": 12822, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "forwarder.client.td_receive", "line_number": 17, "usage_type": "call"}, {"api_name": "forwarder.client", "line_number": 17, "usage_type": "name"}, {"api_name": "forwarder.utility.log_api_error", "line_number": 25, "usage_type": "call"}, {"api_name": "forwarder.utility", "line_number": 25, "usage_type": "name"}, {"api_name": "logging.CRITICAL", "line_number": 25, "usage_type": "attribute"}, {"api_name": "forwarder.client.td_send", "line_number": 32, "usage_type": "call"}, {"api_name": "forwarder.client", "line_number": 32, "usage_type": "name"}, {"api_name": "config.CLIENT", "line_number": 36, "usage_type": "attribute"}, {"api_name": "config.CLIENT", "line_number": 39, "usage_type": "attribute"}, {"api_name": "config.CLIENT", "line_number": 42, "usage_type": "attribute"}, {"api_name": "config.CLIENT", "line_number": 43, "usage_type": "attribute"}, {"api_name": "config.CLIENT", "line_number": 44, "usage_type": "attribute"}, {"api_name": "config.CLIENT", "line_number": 45, "usage_type": "attribute"}, {"api_name": "config.CLIENT", "line_number": 48, "usage_type": "attribute"}, {"api_name": "config.CLIENT", "line_number": 49, "usage_type": "attribute"}, {"api_name": "config.CLIENT", "line_number": 50, "usage_type": "attribute"}, {"api_name": "forwarder.client.td_send", "line_number": 59, "usage_type": "call"}, {"api_name": "forwarder.client", "line_number": 59, "usage_type": "name"}, {"api_name": "forwarder.client.td_send", "line_number": 66, "usage_type": "call"}, {"api_name": "forwarder.client", "line_number": 66, "usage_type": "name"}, {"api_name": "forwarder.client.td_send", "line_number": 76, "usage_type": "call"}, {"api_name": "forwarder.client", "line_number": 76, "usage_type": "name"}, {"api_name": "forwarder.client.td_send", "line_number": 82, "usage_type": "call"}, {"api_name": "forwarder.client", "line_number": 82, "usage_type": "name"}, {"api_name": "forwarder.client.td_send", "line_number": 93, "usage_type": "call"}, {"api_name": "forwarder.client", "line_number": 93, "usage_type": "name"}, {"api_name": "forwarder.utility.log_api_error", "line_number": 102, "usage_type": "call"}, {"api_name": "forwarder.utility", "line_number": 102, "usage_type": "name"}, {"api_name": "logging.ERROR", "line_number": 102, "usage_type": "attribute"}, {"api_name": "config.FORWARDER", "line_number": 110, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 111, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 115, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 115, "usage_type": "attribute"}, {"api_name": "forwarder.client.td_receive", "line_number": 122, "usage_type": "call"}, {"api_name": "forwarder.client", "line_number": 122, "usage_type": "name"}, {"api_name": "forwarder.utility.convert_unix_to_datetime", "line_number": 140, "usage_type": "call"}, {"api_name": "forwarder.utility", "line_number": 140, "usage_type": "name"}, {"api_name": "forwarder.utility.log_api_error", "line_number": 147, "usage_type": "call"}, {"api_name": "forwarder.utility", "line_number": 147, "usage_type": "name"}, {"api_name": "logging.ERROR", "line_number": 147, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 150, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 150, "usage_type": "attribute"}, {"api_name": "config.FORWARDER", "line_number": 153, "usage_type": "attribute"}, {"api_name": "forwarder.utility.group_message_id", "line_number": 182, "usage_type": "call"}, {"api_name": "forwarder.utility", "line_number": 182, "usage_type": "name"}, {"api_name": "forwarder.client.forward_message", "line_number": 195, "usage_type": "call"}, {"api_name": "forwarder.client", "line_number": 195, "usage_type": "name"}, {"api_name": "forwarder.utility.log_api_action", "line_number": 204, "usage_type": "call"}, {"api_name": "forwarder.utility", "line_number": 204, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 210, "usage_type": "call"}, {"api_name": "forwarder.client.td_send", "line_number": 215, "usage_type": "call"}, {"api_name": "forwarder.client", "line_number": 215, "usage_type": "name"}, {"api_name": "forwarder.client.td_receive", "line_number": 217, "usage_type": "call"}, {"api_name": "forwarder.client", "line_number": 217, "usage_type": "name"}, {"api_name": "forwarder.utility.write_events", "line_number": 223, "usage_type": "call"}, {"api_name": "forwarder.utility", "line_number": 223, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 232, "usage_type": "call"}, {"api_name": "config.FORWARDER", "line_number": 234, "usage_type": "attribute"}, {"api_name": "forwarder.client.td_send", "line_number": 237, "usage_type": "call"}, {"api_name": "forwarder.client", "line_number": 237, "usage_type": "name"}, {"api_name": "forwarder.client.td_receive", "line_number": 239, "usage_type": "call"}, {"api_name": "forwarder.client", "line_number": 239, "usage_type": "name"}, {"api_name": "forwarder.utility.write_events", "line_number": 245, "usage_type": "call"}, {"api_name": "forwarder.utility", "line_number": 245, "usage_type": "name"}, {"api_name": "forwarder.client.td_receive", "line_number": 257, "usage_type": "call"}, {"api_name": "forwarder.client", "line_number": 257, "usage_type": "name"}, {"api_name": "forwarder.utility.write_events", "line_number": 263, "usage_type": "call"}, {"api_name": "forwarder.utility", "line_number": 263, "usage_type": "name"}, {"api_name": "forwarder.client.td_send", "line_number": 270, "usage_type": "call"}, {"api_name": "forwarder.client", "line_number": 270, "usage_type": "name"}, {"api_name": "config.FORWARDER", "line_number": 286, "usage_type": "attribute"}]} +{"seq_id": "356163307", "text": "import numpy as np\nimport fast_interp\nimport time\nimport numexpr as ne\nfrom scipy.special import struve, y0\n\nn = 1000000\n\n# function to test evaluation of\n# in this case, nasty and singular at the origin\ndef true_func(x):\n\treturn struve(0, x) - y0(x)\napprox_range = [1e-14, 10]\n\n# generate approximation\napprox_func5 = fast_interp.FunctionGenerator(true_func, approx_range[0], approx_range[1], k=5)\napprox_func7 = fast_interp.FunctionGenerator(true_func, approx_range[0], approx_range[1], k=7)\n\ndef random_in(n, a, b):\n\tx = np.random.rand(n)\n\tx *= (b-a)\n\tx += a\n\treturn x\n\nxtest = random_in(n, approx_range[0], approx_range[1])\n\nst = time.time()\nft = true_func(xtest)\ntrue_func_time = time.time() - st\n\nfa = approx_func5(xtest)\nst = time.time()\nfa5 = approx_func5(xtest)\napprox_func5_time = time.time()-st\n\nfa = approx_func7(xtest)\nst = time.time()\nfa7 = approx_func7(xtest)\napprox_func7_time = time.time()-st\n\nreg = np.abs(ft)\n\nreg[reg < 1] = 1\nerr5 = np.abs(fa5-ft)/reg\nerr7 = np.abs(fa7-ft)/reg\n\nprint('')\nprint('Error (5): {:0.1e}'.format(err5.max()))\nprint('Error (7): {:0.1e}'.format(err7.max()))\nprint('True time: {:0.1f}'.format(true_func_time*1000))\nprint('Approx time (5): {:0.1f}'.format(approx_func5_time*1000))\nprint('Approx time (7): {:0.1f}'.format(approx_func7_time*1000))\nprint('Points/Sec/Core, Thousands (5): {:0.1f}'.format(n/approx_func5_time/1000))\nprint('Points/Sec/Core, Thousands (7): {:0.1f}'.format(n/approx_func7_time/1000))\n\n", "sub_path": "examples/test_function_generation.py", "file_name": "test_function_generation.py", "file_ext": "py", "file_size_in_byte": 1554, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "scipy.special.struve", "line_number": 12, "usage_type": "call"}, {"api_name": "scipy.special.y0", "line_number": 12, "usage_type": "call"}, {"api_name": "fast_interp.FunctionGenerator", "line_number": 16, "usage_type": "call"}, {"api_name": "fast_interp.FunctionGenerator", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 20, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 27, "usage_type": "call"}, {"api_name": "time.time", "line_number": 29, "usage_type": "call"}, {"api_name": "time.time", "line_number": 32, "usage_type": "call"}, {"api_name": "time.time", "line_number": 34, "usage_type": "call"}, {"api_name": "time.time", "line_number": 37, "usage_type": "call"}, {"api_name": "time.time", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 45, "usage_type": "call"}]} +{"seq_id": "279989217", "text": "\"\"\"新建js项目的执行命令.\"\"\"\nimport json\nfrom typing import Dict, Any\nfrom pmfp.const import (\n JS_ENV_PATH\n)\n\n\ndef new_es_script(config: Dict[str, Any]):\n \"\"\"新建js项目的执行命令.\n\n Args:\n config (Dict[str, Any]): 项目配置.\n \"\"\"\n entry = config[\"entry\"]\n with open(str(JS_ENV_PATH), encoding=\"utf-8\") as f:\n content = json.load(f)\n old_scripts = content.get(\"scripts\")\n with open(str(JS_ENV_PATH), \"w\", encoding=\"utf-8\") as f:\n if config.get(\"env\") == \"node\":\n default_script = {\n \"start\": f\"./node_modules/.bin/babel-node {entry}\",\n \"build\": f\"./node_modules/.bin/babel es -d lib\",\n \"test\": \"./node_modules/.bin/nyc --reporter=text ./node_modules/.bin/mocha --require babel-polyfill --require babel-register\"\n }\n elif config.get(\"env\") == \"frontend\":\n default_script = {\n \"start\": \"./node_modules/.bin/live-server --port=3000 public\",\n \"build\": \"./node_modules/.bin/babel es -d public\",\n \"test\": \"./node_modules/.bin/nyc --reporter=text ./node_modules/.bin/mocha --require babel-polyfill --require babel-register\"\n }\n elif config.get(\"env\") == \"webpack\":\n default_script = {\n \"start\": \"./node_modules/.bin/webpack-dev-server --open --config env/webpack.config.dev.js\",\n \"serv:dev\": \"./node_modules/.bin/webpack-dev-server --open --config env/webpack.config.dev.js\",\n \"serv:test\": \"./node_modules/.bin/webpack-dev-server --open --config env/webpack.config.test.js\",\n \"serv:prod\": \"./node_modules/.bin/webpack-dev-server --open --config env/webpack.config.prod.js\",\n \"build\": \"./node_modules/.bin/webpack --config env/webpack.config.prod.js\",\n \"build:dev\": \"./node_modules/.bin/webpack --config env/webpack.config.dev.js\",\n \"build:test\": \"./node_modules/.bin/webpack --config env/webpack.config.test.js\",\n \"build:prod\": \"./node_modules/.bin/webpack --config env/webpack.config.prod.js\",\n \"test\": \"./node_modules/.bin/nyc --reporter=text ./node_modules/.bin/mocha --require babel-polyfill --require babel-register\"\n }\n else:\n default_script = {\n }\n if old_scripts:\n scripts = dict(old_scripts)\n scripts.update(default_script)\n else:\n scripts = default_script\n if content.get(\"esdoc\"):\n scripts.update(\n {\n \"doc\": \"./node_modules/.bin/esdoc\",\n }\n )\n\n content.update({\n \"scripts\": scripts\n })\n json.dump(content, f)\n", "sub_path": "pmfp/new/_new_es_script.py", "file_name": "_new_es_script.py", "file_ext": "py", "file_size_in_byte": 2774, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "typing.Dict", "line_number": 9, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 9, "usage_type": "name"}, {"api_name": "pmfp.const.JS_ENV_PATH", "line_number": 16, "usage_type": "argument"}, {"api_name": "json.load", "line_number": 17, "usage_type": "call"}, {"api_name": "pmfp.const.JS_ENV_PATH", "line_number": 19, "usage_type": "argument"}, {"api_name": "json.dump", "line_number": 62, "usage_type": "call"}]} +{"seq_id": "335647830", "text": "import boto3\nimport json\nimport os\nimport datetime\nimport time\nfrom dateutil.relativedelta import relativedelta\n\n\ndef lambda_handler(event, context):\n \n s3outputbucketname = os.environ['s3outputbucketname']\n outputfolder = os.environ['outputfolder'] + '/'\n print(\"outputfolder: \" + outputfolder)\n \n map_migrated_db = os.environ['map_migrated_db']\n map_migrated_table = os.environ['map_migrated_table']\n extraction_query_name = os.environ['extraction_query_name']\n athena_output_location = os.environ['athena_output_location']\n \n #Check if output folder exists. If it doesn't exist then create an output folder. If it exists, then proceed to empty\n #create_s3outputbucketfolder(s3outputbucketname, outputfolder)\n \n # Empty the existing MAP query output bucket for linked accounts\n empty_s3outputbucket(s3outputbucketname, outputfolder)\n \n #Retrieve and run Athena Extraction query- extracts relevant MAP reports for the linked member\n run_athenaextractionquery(map_migrated_db,map_migrated_table,extraction_query_name,athena_output_location)\n return \n\ndef create_s3outputbucketfolder(s3outputbucketname, outputfolder):\n\n client = boto3.client('s3')\n print(\"inside create_s3outputbucketfolder\")\n response = client.list_objects_v2(\n Bucket=s3outputbucketname\n )\n \n print(\"keycount: \" + str(response['KeyCount']) )\n \n if (response['KeyCount'] == 0):\n print(\"inside create folder\")\n response = client.put_object(\n Bucket=s3outputbucketname, \n Key=outputfolder\n )\n \n\ndef empty_s3outputbucket(s3outputbucketname, outputfolder):\n\n client = boto3.client('s3')\n response = client.list_objects_v2(\n Bucket=s3outputbucketname,\n Prefix=outputfolder\n )\n print(\"keycount in bucket to be deleted: \" + str(response['KeyCount']) )\n \n if (response['KeyCount'] > 0):\n s3objects = response['Contents']\n for s3object in s3objects:\n objectkey = s3object['Key']\n print(\"delete object objectkey is: \" + objectkey)\n response = client.delete_object(\n Bucket=s3outputbucketname,\n Key=objectkey\n )\n response_folder = client.put_object(\n Bucket=s3outputbucketname, \n Key=outputfolder\n ) \n \n# Run MAP Extraction Query for linked accounts\ndef run_athenaextractionquery(map_migrated_db, map_migrated_table, extraction_query_name, athena_output_location):\n\n client = boto3.client('athena')\n response = client.list_named_queries()\n named_query_IDs = response['NamedQueryIds']\n \n \n for query_ID in named_query_IDs: \n print(\"named_query_id: \" + str(query_ID) )\n named_query = client.get_named_query(\n NamedQueryId=query_ID\n )\n query_string = named_query['NamedQuery']['QueryString']\n query_name = named_query['NamedQuery']['Name']\n print(\"query_name: \" + query_name )\n \n if extraction_query_name in query_name:\n drop_query_string = 'DROP TABLE ' + map_migrated_db + '.temp_table'\n print(\"inside drop_query_string: \" + drop_query_string )\n executionID_drop = client.start_query_execution(\n QueryString=drop_query_string,\n ResultConfiguration={\n 'OutputLocation': athena_output_location,\n 'EncryptionConfiguration': {\n 'EncryptionOption': 'SSE_S3',\n }\n }\n )\n \n response_exec = client.get_query_execution(\n QueryExecutionId=executionID_drop['QueryExecutionId']\n )['QueryExecution']['Status']['State']\n while response_exec in ['QUEUED','RUNNING']:\n time.sleep(30)\n response_exec = client.get_query_execution(\n QueryExecutionId=executionID_drop['QueryExecutionId']\n )['QueryExecution']['Status']['State']\n \n \n print(\"completed drop_query_string: \" + drop_query_string )\n \n executionID_create = client.start_query_execution(\n QueryString=query_string,\n ResultConfiguration={\n 'OutputLocation': athena_output_location,\n 'EncryptionConfiguration': {\n 'EncryptionOption': 'SSE_S3',\n }\n }\n )\n print(\"completed create_query_string: \" + query_string)\n \n response_exec_2 = client.get_query_execution(\n QueryExecutionId=executionID_create['QueryExecutionId']\n )['QueryExecution']['Status']['State']\n while response_exec_2 in ['QUEUED','RUNNING']:\n time.sleep(30)\n response_exec_2 = client.get_query_execution(\n QueryExecutionId=executionID_create['QueryExecutionId']\n )['QueryExecution']['Status']['State']\n \n break\n", "sub_path": "lambda/MAP_athenaextractionquerylambda.py", "file_name": "MAP_athenaextractionquerylambda.py", "file_ext": "py", "file_size_in_byte": 5089, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.environ", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 18, "usage_type": "attribute"}, {"api_name": "boto3.client", "line_number": 32, "usage_type": "call"}, {"api_name": "boto3.client", "line_number": 50, "usage_type": "call"}, {"api_name": "boto3.client", "line_number": 74, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 105, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 128, "usage_type": "call"}]} +{"seq_id": "631261041", "text": "\n\nimport csv\nimport datetime\n\ndef validate(date_text):\n try:\n datetime.datetime.strptime(date_text, '%Y-%m-%d')\n return True\n except ValueError:\n return False\n\n\ndef readMyFile(filename):\n with open(filename) as csvDataFile:\n csvReader = csv.reader(csvDataFile)\n print(\"joined,left,difference\")\n for row in csvReader:\n if validate(row[12]) and validate(row[15]):\n join = datetime.datetime.strptime(row[12], '%Y-%m-%d')\n left = datetime.datetime.strptime(row[15], '%Y-%m-%d')\n days = abs(left-join).days\n if days < 400:\n print(\"%s,%s,%s\" % (row[12], row[15], days))\n\nreadMyFile('data/people.csv')\n\n\n", "sub_path": "dates.py", "file_name": "dates.py", "file_ext": "py", "file_size_in_byte": 758, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "datetime.datetime.strptime", "line_number": 8, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 8, "usage_type": "attribute"}, {"api_name": "csv.reader", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 20, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 21, "usage_type": "attribute"}]} +{"seq_id": "50579988", "text": "\"\"\"This modules contains classes for computational grid generation.\"\"\"\nfrom functools import partial\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom legendre_functions import legendre_prime, legendre_double_prime_recursive\nfrom root_finding import newton_method\nimport pickle\nimport time\n\n\nclass Grid(object):\n \"\"\"Computational grid.\n\n Attributes:\n a (float) = start domain\n b (float) = end.domain\n n (int) = number of points - 1\n nodal_pts (np.array) = array containing the nodal points.\n \"\"\"\n\n def __init__(self, start, end, num):\n \"\"\"Initialize grid.\n\n Args:\n start (float) = start domain.\n end (float) = end domain.\n num (int) = number of points - 1.\n \"\"\"\n self.a = start\n self.b = end\n self.n = num\n self.nodal_pts = np.empty(num + 1)\n\n def linear_scaling(self):\n \"\"\"Linear map : [-1,1] --> [a,b].\"\"\"\n self.nodal_pts = (-self.nodal_pts + 1) * (self.b - self.a) / 2 + self.a\n\n def uniform(self):\n \"\"\"Generate a uniform grid.\"\"\"\n self.nodal_pts = np.linspace(self.a, self.b, self.n + 1)\n return self.nodal_pts\n\n def chebychev(self):\n \"\"\"Calculate roots of the n+1 Chebychev polynomial of the first kind.\n\n Returns:\n nodal_pts (np.array)\n \"\"\"\n i = np.arange(0, self.n + 1)\n # nodal point in [-1,1]\n self.nodal_pts = np.cos((2 * i + 1) / (2 * (self.n + 1)) * np.pi)\n self.linear_scaling()\n return self.nodal_pts\n\n def gauss_lobatto(self):\n \"\"\"Calculate the roots of (1+x**2)*L'_n(x) and populate nodal_pts.\n\n Returns:\n nodal_pts (np.array)\n \"\"\"\n try:\n self.nodal_pts = pickle.load(open(\"data/grid/gauss_n\" + str(self.n) + \".p\", \"rb\"))\n except (OSError, IOError) as e:\n # Chebychev nodes as initial value for newton method\n x_0 = np.cos(np.arange(1, self.n) / self.n * np.pi)\n self.nodal_pts = np.empty(self.n + 1)\n # Last and first pts are fixed for every n\n self.nodal_pts[-1] = -1\n self.nodal_pts[0] = 1\n # Newton's method to find the root\n for i, ch_pt in enumerate(x_0):\n leg_p = partial(legendre_prime, n=self.n)\n leg_pp = partial(legendre_double_prime_recursive, n=self.n)\n self.nodal_pts[i + 1] = newton_method(leg_p, leg_pp, ch_pt, 40)[0]\n pickle.dump(self.nodal_pts, open(\"data/grid/gauss_n\" + str(self.n) + \".p\", \"wb\"))\n # scale to arbitrary domain\n self.linear_scaling()\n return self.nodal_pts\n\n def dual_central(self):\n self.gauss_lobatto()\n tmp_nodal_pts = np.ones((np.size(self.nodal_pts) + 1))\n for i in range(0, self.n):\n tmp_nodal_pts[i + 1] = (self.nodal_pts[i] + self.nodal_pts[i + 1]) / 2\n # self.nodal_pts.append(self.b)\n tmp_nodal_pts[0] = self.a\n tmp_nodal_pts[-1] = self.b\n self.nodal_pts = tmp_nodal_pts\n return self.nodal_pts\n\n def plot(self, show=True):\n \"\"\"Plot the grid.\"\"\"\n plt.plot(self.nodal_pts, np.zeros(np.size(self.nodal_pts)), '-o')\n if show:\n plt.show()\n\n def show(self):\n # TODO(modify it to __str__ method)\n print('Nodal point of the grid: \\n', self.nodal_pts)\n\n\ndef plot_grids(grids, *args, show=True, save=False):\n \"\"\"Plot multiple grids.\n\n Args:\n grids (list) = list of Grids\n args = label for the plot, grid ith receive label ith\n \"\"\"\n # add the grids to the plot with labels\n if np.size(grids) > 1:\n for i, grid in enumerate(grids):\n if args:\n plt.plot(grid, np.ones(np.size(grid)) * i, '-o', label=args[i])\n else:\n plt.plot(grid, np.ones(np.size(grid)) * i, '-o')\n else:\n plt.plot(grids, np.zeros(len(grids)), '-o')\n plt.title(args[-1] + \"for N = {0}\" .format(np.size(grids[0]) - 1))\n plt.xlabel(r'$\\xi$')\n plt.ylim(-1, np.ndim(grids) + 1)\n plt.legend()\n if save:\n plt.savefig('imamges/Grid_N_' +\n str(np.size(grids[0]) - 1) + '.png', bbox_inches='tight')\n if show:\n plt.show()\n\n\nclass Grid2d(object):\n \"\"\"2D computational grid.\n\n Attributes:\n xx (2d np.array) = meshgrid of x interval.\n yy (2d np.array) = meshgrid of y interval.\n n (tuple) = (n_x,n_y) where n_x is the number of points in the x direction and\n n_y is the number of points in the y direction.\n grid1d (np.array) = array of two elements, in order 1d grid in x and 1d grid in y.\n nodal_pts (3d np.array) = array containing the nodal points.\n nodal_pts[i,j] returns the coordinates [x_i,y_j] of the nodal point.\n \"\"\"\n\n def __init__(self, start, end, n):\n \"\"\"Initialize 2d grid.\n\n Args:\n start (tuple) = (a_x,a_y), tuple of floats determining start of the domain in x and y.\n end (tuple) = (b_x,b_y), tuple of floats determining end of the domain in x and y.\n n (tuple) = (n_x,n_y) where n_x is the number of points in the x direction and\n n_y is the number of points in the y direction.\n \"\"\"\n self.n = n\n self.nodal_pts = None\n self.grid1d = np.array((Grid(start[0], end[0], n[0]), Grid(start[1], end[1], n[1])))\n self.xx = None\n self.yy = None\n\n def uniform(self):\n \"\"\"Generate a uniform 2d grid.\"\"\"\n # generate 1d uniform grid\n for grid in self.grid1d:\n grid.uniform()\n self.xx, self.yy = np.meshgrid(self.grid1d[0].nodal_pts, self.grid1d[1].nodal_pts)\n self.nodal_pts = np.dstack((self.xx, self.yy))\n\n def chebychev(self):\n \"\"\"Generate a chebychev 2d grid.\"\"\"\n # generate 1d chebychev grids\n for grid in self.grid1d:\n grid.chebychev()\n self.xx, self.yy = np.meshgrid(self.grid1d[0].nodal_pts, self.grid1d[1].nodal_pts)\n # store 3d array the nodal pts, nodal_pts[i,j] returns [x_i,y_j]\n self.nodal_pts = np.dstack((self.xx, self.yy))\n\n def gauss_lobatto(self):\n \"\"\"Generate a gauss_lobatto 2d grid.\"\"\"\n # generate 1d gauss_lobatto grids.\n for grid in self.grid1d:\n grid.gauss_lobatto()\n self.xx, self.yy = np.meshgrid(self.grid1d[0].nodal_pts, self.grid1d[1].nodal_pts)\n self.nodal_pts = np.dstack((self.yy, self.xx))\n\n def plot(self):\n \"\"\"Plot the grid.\"\"\"\n # plotting through scattering\n plt.scatter(self.xx, self.yy, s=10)\n plt.title('Computational grid')\n plt.ylabel('y')\n plt.xlabel('x')\n plt.show()\n\n\ndef check_dual_grid(nodes, dual_nodes):\n naked_dual_nodes = dual_nodes[1:-1]\n compatible = True\n for i in range(len(nodes) - 1):\n if not nodes[i] < naked_dual_nodes[i] < nodes[i + 1]:\n compatible = False\n return compatible\n\n # def __str__(self):\n # \"\"\"New reprensentation for the grid.\"\"\"\n #\n # nodalpts = \"The nodal points are \\n\" + str(self.nodal_pts)\n # description = \"\\nThe grid is organized as follows:\\ngrid[0,0] = \" + str(self.nodal_pts[0, 0]) + \"\\ngrid[0,-1] = \" + str(self.nodal_pts[0, -1]) + \"\\ngrid[-1,0] = \" +\n # str(self.nodal_pts[-1, 0]) + \"\\ngrid[-1,-1] = \" + str(self.nodal_pts[-1, -1])\n # return nodalpts + description\n\n\n# examples\nif __name__ == '__main__':\n dual = Grid(-1, 1, 4)\n primal = Grid(-1, 1, 4)\n #\n # uni_grid_0 = grid_0.uniform()\n nodes = primal.gauss_lobatto()\n dual_nodes = dual.dual_central()\n print(\"The grid are compatible : \", check_dual_grid(nodes, dual_nodes))\n\n # ch_grid_0 = grid_0.chebychev()\n # dual_lob = grid_0.dual_central()\n #\n plot_grids([nodes, dual_nodes], 'primal', 'dual', \"lobatto grids\")\n # plot_grids([uni_grid_0, ch_grid_0, lob_grid_0], 'Uniform',\n # 'Chebychev', 'Gauss-Lobatto', 'Grid comparison', save=False)\n # plot_grids([lob_grid_0, dual_lob], 'Lobatto', 'Dual', 'Dual and Primal complex', save=False)\n # grid2d = Grid2d((-1, -1), (1, 1), (4, 4))\n # grid2d.gauss_lobatto()\n # grid2d.plot()\n # print(grid2d)\n\n # grid2d.plot()\n # print(np.shape(grid2d.nodal_pts))\n # print(grid2d.nodal_pts[0, 0], grid2d.nodal_pts[1, 0], grid2d.nodal_pts[-1, -1])\n # print(grid2d.nodal_pts)\n", "sub_path": "grid.py", "file_name": "grid.py", "file_ext": "py", "file_size_in_byte": 8424, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "numpy.empty", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 65, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 66, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 72, "usage_type": "call"}, {"api_name": "legendre_functions.legendre_prime", "line_number": 72, "usage_type": "argument"}, {"api_name": "functools.partial", "line_number": 73, "usage_type": "call"}, {"api_name": "legendre_functions.legendre_double_prime_recursive", "line_number": 73, "usage_type": "argument"}, {"api_name": "root_finding.newton_method", "line_number": 74, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "numpy.size", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "numpy.ones", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 115, "usage_type": "name"}, {"api_name": "numpy.ones", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "numpy.size", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "numpy.ndim", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "numpy.size", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.dstack", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.dstack", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.dstack", "line_number": 180, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 185, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 186, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 187, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 187, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 188, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 189, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 189, "usage_type": "name"}]} +{"seq_id": "335883789", "text": "from flask import *\nfrom flask_sqlalchemy import SQLAlchemy\nimport dash\nimport dash_html_components as html\nimport dash_core_components as dcc\nimport plotly.express as px\nimport pandas as pd\nimport base64\n\n\n\nserver = Flask(__name__)\n\n\nserver=Flask(__name__)\nserver.config['SQLALCHEMY_DATABASE_URI'] = 'mysql+pymysql://username:password@server/db'\nserver.config['SQLALCHEMY_ECHO'] = True\ndb= SQLAlchemy(server)\n\nclass User(db.Model):\n Name= db.Column(db.String(45),nullable = False)\n Contact = db.Column(db.String(10),primary_key=True)\n Photo=db.Column(db.LargeBinary, nullable = False)\n\n def __init__(self,b,c,d):\n self.Name = b\n self.Contact=c\n self.Photo=d\n\ndef convert_row_to_dict(x):\n d=dict()\n d['name']=x.Name\n d['contact']=x.Contact\n d['photo']=str(base64.encodebytes(x.Photo))\n return d\n\n@server.route('/api',methods=['GET'])\ndef api():\n count=User.query.all()\n #converting rows brought from database to list of dicts\n arr=list(map(convert_row_to_dict,count))\n return {'data':arr}\n\n@server.route('/datahandler', methods=['DELETE'])\ndef deldata():\n print(request)\n print(request.query_string)\n data=request.get_json()\n contact=data['contactnum']\n try:\n person = User.query.filter_by(Contact=contact).first()\n db.session.delete(person)\n db.session.commit()\n return {'Status':'Success'}\n except:\n return {'Status':'Fail'}\n\n@server.route('/datahandler', methods=['PUT',])\ndef updatedata():\n print('---------')\n print('---------')\n data=request.get_json()\n to_update=dict()\n to_update['Name']=data['user']\n to_update['Contact']=data['contactnum']\n contact=data['old']\n print(contact)\n try: \n User.query.filter_by(Contact=contact).update(to_update)\n db.session.commit()\n return {'Status':'Success'}\n except:\n return {'Status':'Fail'}\n\n@server.route('/datahandler', methods=['POST'])\ndef insertdata():\n print(request)\n print(request.query_string)\n if request.method == 'POST':\n print(request)\n print(request.query_string)\n data=request.get_json()\n user=data['user']\n print(user)\n num=data['contactnum']\n img=data['photo']\n img=img[22:]\n photo=base64.b64decode(img) \n new_user=User(user,num,photo)\n try:\n db.session.add(new_user)\n db.session.commit()\n return jsonify({'Status': 'User added'})\n except:\n return jsonify({'Status': 'Image cant be proccessed,change image'})\n\n@server.route(\"/dash\")\n@server.route(\"/alert\")\n@server.route(\"/note\")\n@server.route(\"/home\")\n@server.route('/')\ndef index():\n return render_template(\"index.html\", flask_token=\"Hello world\")\n\napp =dash.Dash(\n __name__,server=server,\n routes_pathname_prefix='/dash/'\n)\ndata = pd.read_csv(\"https://people.sc.fsu.edu/~jburkardt/data/csv/hooke.csv\",sep=',')\nt1 = data[' \"Spring 1 (m)\"']\nt2= data[' \"Spring 2 (m)\"']\nx = data['Index']\n\ndf = pd.DataFrame({\"Index\": x, \"Spring 1\": t1, \"Spring 2\": t2})\n\nfig = px.bar(df, x=\"Index\", y=[\"Spring 1\",\"Spring 2\"], barmode=\"group\")\n\napp.layout = html.Div(children=[\n html.H1(children='Hello Dash'),\n\n html.Div(children='''\n Dash: A web application framework for Python.\n '''),\n\n dcc.Graph(\n id='example-graph',\n figure=fig\n )\n])\n\n\napp2 =dash.Dash(\n __name__,server=server,\n routes_pathname_prefix='/dash2/'\n)\ndf2 = pd.DataFrame({\"x\": [1, 2, 3], \"SF\": [4, 1, 2], \"Montreal\": [2, 4, 5]})\n\nfig2 = px.bar(df2, x=\"x\", y=[\"SF\", \"Montreal\"], barmode=\"group\")\n\napp2.layout = html.Div(children=[\n html.H1(children='Hello Dash'),\n\n html.Div(children='''\n Dash: A web application framework for Python.\n '''),\n\n dcc.Graph(\n id='example-graph',\n figure=fig2\n )\n])\nif __name__ == '__main__':\n server.run(debug=True)\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3909, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 18, "usage_type": "call"}, {"api_name": "base64.encodebytes", "line_number": 34, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 88, "usage_type": "call"}, {"api_name": "dash.Dash", "line_number": 105, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 109, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 114, "usage_type": "call"}, {"api_name": "plotly.express.bar", "line_number": 116, "usage_type": "call"}, {"api_name": "plotly.express", "line_number": 116, "usage_type": "name"}, {"api_name": "dash_html_components.Div", "line_number": 118, "usage_type": "call"}, {"api_name": "dash_html_components.H1", "line_number": 119, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 121, "usage_type": "call"}, {"api_name": "dash_core_components.Graph", "line_number": 125, "usage_type": "call"}, {"api_name": "dash.Dash", "line_number": 132, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 136, "usage_type": "call"}, {"api_name": "plotly.express.bar", "line_number": 138, "usage_type": "call"}, {"api_name": "plotly.express", "line_number": 138, "usage_type": "name"}, {"api_name": "dash_html_components.Div", "line_number": 140, "usage_type": "call"}, {"api_name": "dash_html_components.H1", "line_number": 141, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 143, "usage_type": "call"}, {"api_name": "dash_core_components.Graph", "line_number": 147, "usage_type": "call"}]} +{"seq_id": "90528830", "text": "#Albert functions.py\nimport os, numpy as np, glob, re, cv2\n\n#Homemade functions\ndef img_loader(input_img_folder,image_format,sort):\n try:\n cwd = os.getcwd()\n os.chdir(input_img_folder)\n files =glob.glob1(input_img_folder,'*'+image_format)\n if sort==True:\n files = sorted(files, key=lambda x:float(re.findall(r\"(\\d+)\",x)[0]))\n os.chdir(cwd)\n return files\n except:\n os.chdir(cwd)\n return None\n\ndef img_marked_saver(img_folder,image_format,img_No, img):\n try:\n cwd = os.getcwd() \n os.chdir(img_folder)\n cv2.imwrite(str(img_No)+image_format, img)\n os.chdir(cwd)\n return True\n except:\n os.chdir(cwd)\n print('Could not save image')\n return False\n\ndef ball_drift(center, previouscenter, threshold):\n try: \n ball_drift = np.sqrt(np.sum((center-previouscenter)**2))\n if ball_drift < threshold:\n print('Ball_drift = ',ball_drift)\n return True\n else:\n return False\n except:\n return None\n\ndef ball_size(ball_size, pixel_size):\n try:\n #Z = f*H/h, Z = distance to object, H = ball size, h = pixel size , f = focal length 3,04 mm \n return pixel_size/ball_size\n except:\n return False\n\ndef video_export_v1(output_img_folder,image_format,sort):\n img_array = []\n try:\n os.chdir(output_img_folder)\n files =glob.glob1(output_img_folder,'*'+image_format)\n if sort==True:\n files = sorted(files, key=lambda x:float(re.findall(r\"(\\d+)\",x)[0]))\n \n for file in files:\n img = cv2.imread(file)\n height, width, = img.shape\n size = (width,height)\n img_array.append(img)\n print(len(img_array))\n out = cv2.VideoWriter('project.avi',cv2.VideoWriter_fourcc(*'DIVX'), 15, size)\n \n for i in range(len(img_array)):\n out.write(img_array[i])\n out.release()\n return 1\n except:\n return None\n\n\n#Parameters:\t\n#image – 8-bit, single-channel, grayscale input image.\n#circles – Output vector of found circles. Each vector is encoded as a 3-element floating-p oint vector (x, y, radius) .\n#circle_storage – In C function this is a memory storage that will contain the output sequence of found circles.\n#method – Detection method to use. Currently, the only implemented method is CV_HOUGH_GRADIENT , which is basically 21HT , described in [Yuen90].\n#dp – Inverse ratio of the accumulator resolution to the image resolution. For example, if dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has half as big width and height.\n\n#for filename in glob.glob(\"*.png\"): # This line take all the files of the filename .png from the current folder. Source http://stackoverflow.com/questions/6997419/how-to-create-a-loop-to-read-several-images-in-a-python-script\n# if you want sort files according to the digits included in the filename, you can do as following:\n#files = sorted(files, key=lambda x:float(re.findall(\"(\\d+)\",x)[0]))\n\n#HoughCircles params:\n#minDist – Minimum distance between the centers of the detected circles. If the parameter is too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is too large, some circles may be missed.\n#param1 – First method-specific parameter. In case of CV_HOUGH_GRADIENT , it is the higher threshold of the two passed to the Canny() edge detector (the lower one is twice smaller).\n#param2 – Second method-specific parameter. In case of CV_HOUGH_GRADIENT , it is the accumulator threshold for the circle centers at the detection stage. The smaller it is, the more false circles may be detected. Circles, corresponding to the larger accumulator values, will be returned first.\n#minRadius – Minimum circle radius.\n#maxRadius – Maximum circle radius\n\n#For basement pictures in TestDrone640 use minRadius=5,maxRadius=30, param1 = 500, param2 = 9,minDist=500)\n#For pica 30 use: param1=500,param2=9,minRadius=5,maxRadius=15) \ndef h_circles(img, blur,params):\n try:\n img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n if blur:\n img = cv2.GaussianBlur(img,(3,3),0)\n if params == []:\n params=[500, 200, 5, 15, 80] \n# print(\"No params provided\")\n circles = cv2.HoughCircles(img,cv2.HOUGH_GRADIENT,1,minDist=params[0],param1=params[1],param2=params[2],minRadius=params[3],maxRadius=params[4])\n circles = circles.astype(int)\n return circles\n except:\n return None\n\ndef draw_circles(img, circ):\n try:\n circ = np.uint16(np.around(circ))\n for i in circ[0,:]:\n # draw the outer circle\n cv2.circle(img,(i[0],i[1]),i[2],(0,255,0),2)\n # draw the center of the circle\n cv2.circle(img,(i[0],i[1]),2,(0,0,255),3)\n return img\n except:\n# print('No circles found in image: ')\n return img\n\ndef draw_circles2(img, circ):\n try:\n circ = np.uint16(np.around(circ))\n # draw the outer circle\n cv2.circle(img,(circ[0],circ[1]),circ[2],(0,255,0),2)\n # draw the center of the circle\n cv2.circle(img,(circ[0],circ[1]),2,(0,0,255),3)\n return img\n except:\n# print('No circles found in image: ')\n return img\n\n\ndef video_export_v2(output_img_folder,images,filename):\n \n cwd = os.getcwd()\n os.chdir(output_img_folder)\n img_array = []\n# try:\n for img in images:\n height, width, layer = img.shape \n size = (width,height)\n img_array.append(img)\n out = cv2.VideoWriter(filename,cv2.VideoWriter_fourcc(*'DIVX'), 15, size)\n \n for i in range(len(img_array)):\n out.write(img_array[i])\n out.release()\n print(cwd)\n os.chdir(cwd)\n return True\n# except:\n# os.chdir(cwd) \n# return None\n\ndef colourmask(img,colour):\n#Different applications use different scales for HSV. \n#For example gimp uses H = 0-360, S = 0-100 and V = 0-100. \n#But OpenCV uses H: 0-179, S: 0-255, V: 0-255. Here i got a hue value of 22 in gimp.\n#So I took half of it, 11, and defined range for that. ie (5,50,50) - (15,255,255).\n# masked_img=[]\n try:\n img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n if colour == 'red':\n low_red1 = np.array([0, 200, 50])\n# high_red1 = np.array([10, 255, 255])\n high_red1 = np.array([20, 255, 255])\n low_red2 = np.array([175, 200, 50])\n high_red2 = np.array([179, 255, 255])\n red_mask1 = cv2.inRange(img_hsv, low_red1, high_red1)\n# cv2.imshow(\"red_lower\",red_mask1)\n red_mask2 = cv2.inRange(img_hsv, low_red2, high_red2)\n# cv2.imshow(\"red_higher\",red_mask2)\n# masked_img1 = cv2.bitwise_and(img, img, mask=red_mask1)\n# masked_img2 = cv2.bitwise_and(img, img, mask=red_mask2)\n# masked_img = cv2.bitwise_or(masked_img1, masked_img2) \n# return masked_img\n return cv2.bitwise_or(red_mask1,red_mask2)\n elif colour == 'green':\n low_green = np.array([38, 50, 50])\n high_green = np.array([75, 255, 255])\n green_mask = cv2.inRange(img_hsv, low_green, high_green)\n# masked_img = cv2.bitwise_and(img, img, mask=green_mask)\n# return masked_img\n return green_mask\n elif colour == 'blue':\n low_blue = np.array([94, 80, 2])\n high_blue = np.array([126, 255, 255])\n blue_mask = cv2.inRange(img_hsv, low_blue, high_blue)\n# masked_img = cv2.bitwise_and(img, img, mask=blue_mask)\n# return masked_img\n return blue_mask\n \n except:\n return None\n\ndef getBoxLim(img,yxr,scale):\n# print(\"img: \",img, \"yxr: \",yxr)\n# size=np.uint16(np.around(scale*yxr[2]))\n size=int((np.around(scale*yxr[2])))\n left = yxr[1]-size\n right = yxr[1]+size\n up = yxr[0]-size\n down = yxr[0]+size\n if(right>img[0]):\n right=img[0]\n if(left<0):\n left=0\n if(down>img[1]):\n down=img[1]\n if(up<0):\n up=0\n# print(\"xL: \",xL,\"xH: \",xH,\"yL: \",yL,\"yH: \",yH)\n return [left,right,up,down]\n\n\n\ndef search_box1(img,yxr,scale):\n# print(\"img: \",img, \"yxr: \",yxr)\n size=np.uint16(np.around(scale*yxr[2]))\n xL = yxr[1]-size\n xH = yxr[1]+size\n yL = yxr[0]-size\n yH = yxr[0]+size\n if(xH>img[0]):\n xH=img[0]\n if(xL<0):\n xL=0\n if(yH>img[1]):\n yH=img[1]\n if(yL<0):\n yL=0\n# print(\"xL: \",xL,\"xH: \",xH,\"yL: \",yL,\"yH: \",yH)\n return [xL,xH,yL,yH]\n\ndef search_box2(img,yxr,scale):\n# print(\"img: \",img.shape, \"yxr: \",yxr)\n size=np.uint16(np.around(scale*yxr[2]))\n xL = yxr[1]-size\n xH = yxr[1]+size\n yL = yxr[0]-size\n yH = yxr[0]+size\n # if(xH>img.shape[0]):\n # xH=img.shape[0]\n # if(xL<0):\n # xL=0\n # if(yH>img.shape[1]):\n # yH=img.shape[1]\n # if(yL<0):\n # yL=0\n if(xH>img[0]):\n xH=img[0]\n if(xL<0):\n xL=0\n if(yH>img[1]):\n yH=img[1]\n if(yL<0):\n yL=0\n #b_img=cv2.rectangle(img, (yL, xL), (yH, xH), (255,0,0), 2)\n# print(\"xL: \",xL,\"xH: \",xH,\"yL: \",yL,\"yH: \",yH)\n #return b_img\n print(\"xL: \",xL,\"xH: \",xH,\"yL: \",yL,\"yH: \",yH)\n return xL,xH,yL,yH\n\n#def search_colour(mask_img,box):\n# value = sum(sum(mask_img[box[0]:box[1],box[2]:box[3]]))\n# return value\n\n\n# ========================================================================#\n#TBD\ndef ball_dist(radius,ballsize):\n return(radius)\n\n\n#https://stackoverflow.com/questions/4623446/how-do-you-sort-files-numerically\ndef tryint(s):\n try:\n return int(s)\n except:\n return s\n\ndef alphanum_key(s):\n \"\"\" Turn a string into a list of string and number chunks.\n \"z23a\" -> [\"z\", 23, \"a\"]\n \"\"\"\n return [ tryint(c) for c in re.split('([0-9]+)', s) ]\n\ndef sort_nicely(l):\n \"\"\" Sort the given list in the way that humans expect.\n \"\"\"\n l.sort(key=alphanum_key)\n \n#https://stackoverflow.com/questions/9041681/opencv-python-rotate-image-by-x-degrees-around-specific-point\ndef rotateImage(image, angle):\n image_center = tuple(np.array(image.shape[1::-1]) / 2)\n rot_mat = cv2.getRotationMatrix2D(image_center, angle, 1.0)\n result = cv2.warpAffine(image, rot_mat, image.shape[1::-1], flags=cv2.INTER_LINEAR)\n return result", "sub_path": "fælles/AlbertFunctions.py", "file_name": "AlbertFunctions.py", "file_ext": "py", "file_size_in_byte": 10559, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "os.getcwd", "line_number": 7, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 8, "usage_type": "call"}, {"api_name": "glob.glob1", "line_number": 9, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 11, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 12, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 15, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 20, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 22, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 23, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 32, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 51, "usage_type": "call"}, {"api_name": "glob.glob1", "line_number": 52, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 54, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.VideoWriter", "line_number": 62, "usage_type": "call"}, {"api_name": "cv2.VideoWriter_fourcc", "line_number": 62, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 94, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 94, "usage_type": "attribute"}, {"api_name": "cv2.GaussianBlur", "line_number": 96, "usage_type": "call"}, {"api_name": "cv2.HoughCircles", "line_number": 100, "usage_type": "call"}, {"api_name": "cv2.HOUGH_GRADIENT", "line_number": 100, "usage_type": "attribute"}, {"api_name": "numpy.uint16", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 108, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 111, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.uint16", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 121, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 123, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 125, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 134, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 135, "usage_type": "call"}, {"api_name": "cv2.VideoWriter", "line_number": 142, "usage_type": "call"}, {"api_name": "cv2.VideoWriter_fourcc", "line_number": 142, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 148, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 161, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 161, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 167, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 168, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 170, "usage_type": "call"}, {"api_name": "cv2.bitwise_or", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 179, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 186, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.uint16", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.uint16", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 236, "usage_type": "call"}, {"api_name": "re.split", "line_number": 285, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 294, "usage_type": "call"}, {"api_name": "cv2.getRotationMatrix2D", "line_number": 295, "usage_type": "call"}, {"api_name": "cv2.warpAffine", "line_number": 296, "usage_type": "call"}, {"api_name": "cv2.INTER_LINEAR", "line_number": 296, "usage_type": "attribute"}]} +{"seq_id": "423546445", "text": "from django.conf.urls import url\nfrom . import views\n\nurlpatterns = [\n url(r'^$', views.index),\n\n url(r'^login$', views.login),\n\n url(r'^cart$', views.cart),\n\n url(r'^dashboard$', views.dashboard),\n\n url(r'^borrower$', views.borrower),\n]\n", "sub_path": "ecommerce/apps/ecommerce_app/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 253, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "django.conf.urls.url", "line_number": 5, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "114165744", "text": "# -*- coding: utf-8 -*-\n__author__ = 'xtwxfxk'\n\n\nfrom setuptools import setup, Command, find_packages\nimport io\nimport re\nimport os\n\ncurdir = os.path.abspath(os.path.dirname(__file__))\n\nREADME = open(os.path.join(curdir, \"README.md\")).read()\n\ndef version():\n ret = re.findall(r'VERSION: (.*)', README)[0]\n return ret.strip()\n\n\ndef read_requirements(filename):\n with open(filename) as f:\n return f.read().splitlines()\n\npackages = find_packages()\n\ndef get_data_files():\n sep = os.path.sep\n # install the datasets\n data_files = {}\n\n for r, ds, fs in os.walk(os.path.join(curdir, \"lutils/ext\")):\n r_ = os.path.relpath(r, start=curdir)\n data_files.update({r_.replace(sep, \".\") : [\"*.xpi\", ]})\n\n return data_files\n\npackage_data = get_data_files()\npackage_data.update({\"lutils\" : [\"header\", \"logging.conf\", \"ser\", \"user_agent\", \"user_agent_all\"]})\n\n\n\nsetup(\n name=\"lutils\",\n version=version(),\n author=\"xtwxfxk\",\n author_email=\"xtwxfxk@163.com\",\n description=\"\",\n long_description=README,\n license=\"BSD\",\n keywords=\"lutils\",\n url=\"https://github.com/xtwxfxk/lutils3\",\n packages=packages,\n package_data = package_data,\n platforms=['any'],\n classifiers=[\n \"Programming Language :: Python\",\n \"Programming Language :: Python :: 2.7\",\n\n \"Programming Language :: Python :: Implementation :: CPython\",\n \"Natural Language :: English\",\n\n \"Operating System :: OS Independent\",\n \"License :: OSI Approved :: Apache Software License\",\n ],\n include_package_data=False,\n install_requires=read_requirements('requirements.txt')\n)", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1640, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.path.abspath", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 15, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.path.relpath", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "setuptools.setup", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "600323068", "text": "import os\nimport sys\nimport transaction\n\nfrom pyramid.paster import (\n get_appsettings,\n setup_logging,\n )\n\nfrom pyramid.scripts.common import parse_vars\n\nfrom ..models.meta import Base\nfrom ..models import (\n get_engine,\n get_session_factory,\n get_tm_session,\n )\nfrom ..models.mymodel import MyModel\n\nfrom ..models.user import User\n\n#from ..models.tracking.record import Record\n#from ..models.tracking.section import Section, SectionItem, ViewSection\nfrom ..data.entry import EntryCreator\nfrom ..data.perspective import PerspectiveCreator\n\nfrom ..components.storage.database import set_user_preference_default\n\nfrom ..resources import get_root\nfrom pyramid.traversal import find_resource, resource_path\n\nfrom datetime import datetime\n\ndef usage(argv):\n cmd = os.path.basename(argv[0])\n print('usage: %s [var=value]\\n'\n '(example: \"%s development.ini\")' % (cmd, cmd))\n sys.exit(1)\n\n\ndef main(argv=sys.argv):\n if len(argv) < 2:\n usage(argv)\n config_uri = argv[1]\n options = parse_vars(argv[2:])\n setup_logging(config_uri)\n settings = get_appsettings(config_uri, options=options)\n\n engine = get_engine(settings)\n Base.metadata.drop_all(engine)\n Base.metadata.create_all(engine)\n session_factory = get_session_factory(engine)\n\n with transaction.manager:\n dbsession = get_tm_session(session_factory, transaction.manager)\n\n model = MyModel(name='one', value=1)\n dbsession.add(model)\n\n ## user preference\n resource_root = get_root(None)\n\n ## user\n ### app\n #user_app = set_user_preference_default(dbsession)\n ### test user\n user_test = User(full_name='Daniel Klesc', user_id='czdankle', role='admin')\n user_test.set_password('heslo')\n dbsession.add(user_test)\n\n data_creator = EntryCreator()\n data_creator.create_data()\n data_creator.push_to_db(dbsession)\n\n perspective_creator = PerspectiveCreator(data_creator.entry_groups,\\\n data_creator.entry_options)\n perspective_creator.create_data()\n perspective_creator.push_to_db(dbsession)\n\n \"\"\"\n # tracking tool\n ## who\n section_who = Section()\n section_who.section_id = \"who\"\n section_who.name = \"Kdo\"\n dbsession.add(section_who)\n ### items\n #### building B\n\n #### building C\n section_who_coordinator_c = SectionItem()\n section_who_coordinator_c.section_item_id = 'coordinator-c'\n section_who_coordinator_c.name = 'Koordinátor - C'\n section_who.items.append(section_who_coordinator_c)\n section_who_who_c_1 = SectionItem()\n section_who_who_c_1.section_item_id = 'who_c_1'\n section_who_who_c_1.name = 'WHO C3 - L03'\n section_who.items.append(section_who_who_c_1)\n dbsession.add(section_who_who_c_1)\n section_who_who_c_2 = SectionItem()\n section_who_who_c_2.section_item_id = 'who_c_2'\n section_who_who_c_2.name = 'WHO C3 - L04'\n section_who.items.append(section_who_who_c_2)\n dbsession.add(section_who_who_c_2)\n section_who_who_c_3 = SectionItem()\n section_who_who_c_3.section_item_id = 'who_c_3'\n section_who_who_c_3.name = 'WHO C3 - L07'\n section_who.items.append(section_who_who_c_3)\n dbsession.add(section_who_who_c_3)\n section_who_who_c_4 = SectionItem()\n section_who_who_c_4.section_item_id = 'who_c_4'\n section_who_who_c_4.name = 'WHO C3 - L06'\n section_who.items.append(section_who_who_c_4)\n dbsession.add(section_who_who_c_4)\n section_who_who_c_5 = SectionItem()\n section_who_who_c_5.section_item_id = 'who_c_5'\n section_who_who_c_5.name = 'WHO C3 - L02'\n section_who.items.append(section_who_who_c_5)\n dbsession.add(section_who_who_c_5)\n\n ## what\n ### items\n #### in\n #### out\n\n ## where - building B\n ### start\n ### end\n #### items\n ##### gate - b\n\n ##### production line\n\n ##### interface\n\n ##### washing machine\n section_where_washing_machine = SectionItem()\n section_where_washing_machine.section_item_id = 'washing_machine'\n section_where_washing_machine.name = 'Myčka'\n section_where_start.items.append(section_where_washing_machine)\n section_where_end.items.append(section_where_washing_machine)\n dbsession.add(section_where_washing_machine)\n ##### scale station\n ##### vna\n ##### ta\n ##### ra\n ##### pa\n\n\n ## where - building C\n\n ### start\n #### items\n ##### gate\n ##### gate - c\n\n ##### production line\n ##### interface\n ##### scale station\n\n ##### vna\n ##### ta\n ##### ra\n ##### pa\n \"\"\"\n", "sub_path": "long_term_forecast/scripts/initializedb.py", "file_name": "initializedb.py", "file_ext": "py", "file_size_in_byte": 4935, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.path.basename", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 38, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 41, "usage_type": "attribute"}, {"api_name": "pyramid.scripts.common.parse_vars", "line_number": 45, "usage_type": "call"}, {"api_name": "pyramid.paster.setup_logging", "line_number": 46, "usage_type": "call"}, {"api_name": "pyramid.paster.get_appsettings", "line_number": 47, "usage_type": "call"}, {"api_name": "models.get_engine", "line_number": 49, "usage_type": "call"}, {"api_name": "models.meta.Base.metadata.drop_all", "line_number": 50, "usage_type": "call"}, {"api_name": "models.meta.Base.metadata", "line_number": 50, "usage_type": "attribute"}, {"api_name": "models.meta.Base", "line_number": 50, "usage_type": "name"}, {"api_name": "models.meta.Base.metadata.create_all", "line_number": 51, "usage_type": "call"}, {"api_name": "models.meta.Base.metadata", "line_number": 51, "usage_type": "attribute"}, {"api_name": "models.meta.Base", "line_number": 51, "usage_type": "name"}, {"api_name": "models.get_session_factory", "line_number": 52, "usage_type": "call"}, {"api_name": "transaction.manager", "line_number": 54, "usage_type": "attribute"}, {"api_name": "models.get_tm_session", "line_number": 55, "usage_type": "call"}, {"api_name": "transaction.manager", "line_number": 55, "usage_type": "attribute"}, {"api_name": "models.mymodel.MyModel", "line_number": 57, "usage_type": "call"}, {"api_name": "resources.get_root", "line_number": 61, "usage_type": "call"}, {"api_name": "models.user.User", "line_number": 67, "usage_type": "call"}, {"api_name": "data.entry.EntryCreator", "line_number": 71, "usage_type": "call"}, {"api_name": "data.perspective.PerspectiveCreator", "line_number": 75, "usage_type": "call"}]} +{"seq_id": "624399444", "text": "from django.db import models\nfrom User.models import GyroUser\n\n\nclass ScheduleType(models.Model):\n type_name = models.CharField(max_length=8,\n verbose_name=\"type_name\",\n help_text=\"schedule's type name, \")\n\n\nclass Schedule(models.Model):\n title = models.CharField(max_length=32,\n help_text=\"schedule's title, less than 32 char.\",\n verbose_name=\"title\")\n description = models.TextField(blank=True,\n db_index=True,\n verbose_name=\"description\",\n help_text=\"schedule's description.\")\n notify_time = models.DurationField(verbose_name=\"notify_time\",\n help_text=\"timedelta to notify user.\")\n start_time = models.DateTimeField(verbose_name=\"start_time\",\n help_text=\"schedule's start time.\")\n end_time = models.DateTimeField(verbose_name=\"end_time\",\n help_text=\"schedule's end time.\")\n creator = models.ForeignKey(GyroUser,\n on_delete=models.CASCADE,\n verbose_name=\"creator\",\n help_text=\"schedule's creator.\")\n participator_count = models.IntegerField(default=1,\n verbose_name=\"participator_count\",\n help_text=\"the number of user participate the schedule.\")\n type = models.ForeignKey(ScheduleType,\n on_delete=models.CASCADE,\n verbose_name=\"type\",\n help_text=\"the schedule's type.\")\n\nclass ScheduleParticipator(models.Model):\n schedule = models.ForeignKey(Schedule,\n on_delete=models.CASCADE,\n verbose_name=\"schedule\",\n help_text=\"the schedule that user participate.\")\n participator = models.ForeignKey(GyroUser,\n on_delete=models.CASCADE,\n verbose_name=\"participator\",\n help_text=\"the user who participate the schedule.\")\n\n", "sub_path": "Schedule/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 2348, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "django.db.models.Model", "line_number": 5, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 5, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 6, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 6, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 11, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 11, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 12, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 12, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.DurationField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 25, "usage_type": "call"}, {"api_name": "User.models.GyroUser", "line_number": 25, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 26, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 33, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 37, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 37, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 38, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 38, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 39, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 39, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 42, "usage_type": "call"}, {"api_name": "User.models.GyroUser", "line_number": 42, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 42, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 43, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 43, "usage_type": "name"}]} +{"seq_id": "607286193", "text": "# -*- coding: utf-8 -*-\n#from __future__ import unicode_literals\nimport io\nimport csv\nimport os\nimport time\nimport sys\n#import urllib2\nfrom datetime import datetime\nfrom dateutil.relativedelta import relativedelta\n#from grs import RealtimeWeight\nimport stock_comm\nimport stock_comm as cmm \nimport requests\nimport inspect\nfrom inspect import currentframe, getframeinfo\nimport pandas as pd\n#import pyecharts\n#from pyecharts import Kline\n#from pyecharts import Candlestick\n#import webbrowser\ndef lno():\n cf = currentframe()\n filename = getframeinfo(cf).filename\n return '%s-L(%d)'%(os.path.basename(filename),inspect.currentframe().f_back.f_lineno)\n\ndef check_dst_folder(dstpath):\n if not os.path.isdir(dstpath):\n os.makedirs(dstpath) \n\n \n \nTSE_KLINE_PATH='csv/tse_kline' \ndef download_file(url, filename):\n ''' Downloads file from the url and save it as filename '''\n # check if file already exists\n print('Downloading File')\n response = requests.get(url)\n # Check if the response is ok (200)\n if response.status_code == 200:\n # Open file and write the content\n #print(len(response.content))\n if len(response.content)<10:\n return 0\n with open(filename, 'wb') as file:\n # A chunk of 128 bytes\n for chunk in response:\n file.write(chunk)\n return 1\n return 0 \n \ndef down_otc_3big(startdate,enddate):\n result=[]\n sr_list=[]\n dst_folder='csv/big3'\n check_dst_folder(dst_folder)\n nowdatetime = enddate\n day=0\n while nowdatetime>=startdate :\n #print (lno(),nowdatetime)\n dstpath='%s/%d%02d%02d_otc.csv'%(dst_folder,int(nowdatetime.year), int(nowdatetime.month),int(nowdatetime.day))\n url='https://www.tpex.org.tw/web/stock/3insti/3insti_summary/3itrdsum_result.php?l=zh-tw&t=D&p=1&d=%d/%02d/%02d&o=htm'%(int(nowdatetime.year)-1911,int(nowdatetime.month),int(nowdatetime.day))\n tb = pd.read_html(url)\n #print(lno(),type(tb))\n if isinstance(tb[0], pd.DataFrame):\n if not tb[0].empty :\n print(lno(),len(tb),tb)\n tb[0].to_csv(dstpath, mode='w', encoding='utf_8', header=1, index=0)\n #download_file(url,dstpath)\n \"\"\"\n if os.path.exists(dstpath): \n df = pd.read_csv(dstpath,encoding = 'big5',skiprows=1)\n df.dropna(axis=1,how='all',inplace=True)\n df.dropna(inplace=True)\n print(lno(),len(df),df)\n #產生 日期,\n \"\"\" \n \n time.sleep(2)\n day=day+1\n nowdatetime = enddate - relativedelta(days=day)\ndef generate_otc_3big(startdate,enddate):\n out_file='csv/big3/big3_data_otc.csv'\n dst_folder='csv/big3'\n check_dst_folder(dst_folder)\n nowdatetime = enddate\n day=0\n res=[]\n while nowdatetime>=startdate :\n #print (lno(),nowdatetime)\n nowdate='%d%02d%02d'%(int(nowdatetime.year), int(nowdatetime.month),int(nowdatetime.day))\n dstpath='%s/%s_otc.csv'%(dst_folder,nowdate)\n\n if os.path.exists(dstpath): \n df = pd.read_csv(dstpath,encoding = 'utf8',skiprows=1)\n print(lno(),df)\n df.dropna(axis=1,how='all',inplace=True)\n df.dropna(inplace=True)\n if len(df)==6:\n tmp=[]\n date_str='%d-%02d-%02d'%(int(nowdatetime.year), int(nowdatetime.month),int(nowdatetime.day))\n tmp.append(date_str)\n for i in range(0, len(df)):\n tmp.append(df.at[i,'買進金額'])\n tmp.append(df.at[i,'賣出金額'])\n tmp.append(df.at[i,'買賣差額'])\n \n #print(lno(),tmp)\n res.append(tmp)\n elif len(df)==4 :\n \n tmp=[]\n date_str='%d-%02d-%02d'%(int(nowdatetime.year), int(nowdatetime.month),int(nowdatetime.day))\n tmp.append(date_str)\n tmp.append(df.at[0,'買進金額'])\n tmp.append(df.at[0,'賣出金額'])\n tmp.append(df.at[0,'買賣差額'])\n tmp.append(0)\n tmp.append(0)\n tmp.append(0)\n tmp.append(df.at[1,'買進金額'])\n tmp.append(df.at[1,'賣出金額'])\n tmp.append(df.at[1,'買賣差額'])\n tmp.append(df.at[2,'買進金額'])\n tmp.append(df.at[2,'賣出金額'])\n tmp.append(df.at[2,'買賣差額'])\n tmp.append(0)\n tmp.append(0)\n tmp.append(0)\n tmp.append(df.at[3,'買進金額'])\n tmp.append(df.at[3,'賣出金額'])\n tmp.append(df.at[3,'買賣差額'])\n \n \n #print(lno(),tmp)\n res.append(tmp)\n else :\n print(lno(),len(df),nowdatetime)\n tmp=[]\n date_str='%d-%02d-%02d'%(int(nowdatetime.year), int(nowdatetime.month),int(nowdatetime.day))\n tmp.append(date_str)\n tmp.append(df.at[0,'買進金額'])\n tmp.append(df.at[0,'賣出金額'])\n tmp.append(df.at[0,'買賣差額'])\n tmp.append(df.at[1,'買進金額'])\n tmp.append(df.at[1,'賣出金額'])\n tmp.append(df.at[1,'買賣差額'])\n tmp.append(df.at[2,'買進金額'])\n tmp.append(df.at[2,'賣出金額'])\n tmp.append(df.at[2,'買賣差額'])\n \n tmp.append(df.at[3,'買進金額'])\n tmp.append(df.at[3,'賣出金額'])\n tmp.append(df.at[3,'買賣差額']) \n tmp.append(0)\n tmp.append(0)\n tmp.append(0)\n tmp.append(df.at[4,'買進金額'])\n tmp.append(df.at[4,'賣出金額'])\n tmp.append(df.at[4,'買賣差額'])\n res.append(tmp) \n #return []\n #產生 日期,\n day=day+1\n nowdatetime = enddate - relativedelta(days=day) \n labels = ['date','自營商buy', '自營商sell', '自營商total', '自營商避險buy', '自營商避險sell', '自營商避險total','投信buy', '投信sell', '投信total', \\\n '外資buy', '外資sell', '外資total','外資自營商buy', '外資自營商sell', '外資自營商total','總buy', '總sell', '總total',]\n\n res_df = pd.DataFrame.from_records(res, columns=labels) \n #print (lno(),res_df) \n if os.path.exists(out_file): \n df_s = pd.read_csv(out_file,encoding = 'utf-8')\n df_s.dropna(axis=1,how='all',inplace=True)\n df_s.dropna(inplace=True)\n #print(lno(),df_s['date'].dtype)\n #print(lno(),res_df['date'].dtype)\n df_s=df_s.append(res_df,ignore_index=True)\n #print(lno(),df_s[['date','自營商total']])\n df_s.drop_duplicates(subset=['date'],keep='first',inplace=True)\n #print(lno(),df_s[['date','外資total','投信total','自營商total']])\n df_s.to_csv(out_file,encoding='utf-8', index=False)\n \n else :\n res_df.to_csv(out_file,encoding='utf-8', index=False)\n \ndef get_twse_3big(date):\n out_file='csv/big3/big3_data.csv'\n #print(lno(),date)\n if os.path.exists(out_file): \n \n date_str='%d-%02d-%02d'%(int(date.year), int(date.month),int(date.day))\n df_s = pd.read_csv(out_file,encoding = 'utf-8')\n df_s.dropna(axis=1,how='all',inplace=True)\n df_s.dropna(inplace=True)\n #print(lno(),df_s[(df_s['date'] == date_str)].values.tolist())\n return df_s[(df_s['date'] == date_str)]\n\n \n else :\n return [] \ndef get_foreign_investment(date):\n out_file='csv/big3/big3_data.csv'\n #print(lno(),date)\n if os.path.exists(out_file): \n \n date_str='%d-%02d-%02d'%(int(date.year), int(date.month),int(date.day))\n df_s = pd.read_csv(out_file,encoding = 'utf-8')\n df_s.dropna(axis=1,how='all',inplace=True)\n df_s.dropna(inplace=True)\n #print(lno(),df_s[(df_s['date'] == date_str)].values.tolist())\n df=df_s[(df_s['date'] == date_str)]\n if len(df)==1:\n df.reset_index(inplace=True)\n #total=float(df.iat[0,'外資total'].strip().replace(',', ''))\n try:\n total=float(df.at[0,'外資total'].strip().replace(',', ''))+float(df.at[0,'外資自營商total'].strip().replace(',', ''))\n except:\n print (lno(),df.at[0,'外資total'],df.at[0,'外資自營商total']) \n total_int=int(total/100000000)\n print(lno(),date_str,df.at[0,'外資total'],df.at[0,'外資自營商total'])\n return total_int\n return 0\n \n else :\n return 0 \ndef get_twse_big3_investmentent(date):\n out_file='csv/big3/big3_data.csv'\n #print(lno(),date)\n if os.path.exists(out_file): \n \n date_str='%d-%02d-%02d'%(int(date.year), int(date.month),int(date.day))\n df_s = pd.read_csv(out_file,encoding = 'utf-8')\n df_s.dropna(axis=1,how='all',inplace=True)\n df_s.dropna(inplace=True)\n #print(lno(),df_s[(df_s['date'] == date_str)].values.tolist())\n df=df_s[(df_s['date'] == date_str)]\n if len(df)==1:\n df.reset_index(inplace=True)\n #total=float(df.iat[0,'外資total'].strip().replace(',', ''))\n try:\n total1=float(df.at[0,'外資total'].strip().replace(',', ''))+float(df.at[0,'外資自營商total'].strip().replace(',', ''))\n total2=float(df.at[0,'自營商total'].strip().replace(',', ''))+float(df.at[0,'自營商避險total'].strip().replace(',', ''))\n total3=float(df.at[0,'投信total'].strip().replace(',', ''))\n except:\n print (lno(),df.at[0,'外資total'],df.at[0,'外資自營商total']) \n total1_int=int(total1/100000000)\n total2_int=int(total2/100000000)\n total3_int=int(total3/100000000)\n #print(lno(),date_str,df.at[0,'外資total'],df.at[0,'外資自營商total'])\n return total1_int,total2_int,total3_int\n return 0\n \n else :\n return 0 \nif __name__ == '__main__':\n #print (lno(),sys.path[0])\n #get_cur_twii_list(datetime.today())\n if len(sys.argv)==1:\n startdate=stock_comm.get_date()\n enddate=stock_comm.get_date()\n down_otc_3big(startdate,enddate)\n generate_otc_3big(startdate,enddate)\n #get_list_form_url_and_save(url,dstpath)\n #show_twii(nowdatetime)\n elif sys.argv[1]=='-d' :\n #print (lno(),len(sys.argv))\n if len(sys.argv)==4 :\n\n startdate=datetime.strptime(sys.argv[2],'%Y%m%d')\n enddate=datetime.strptime(sys.argv[3],'%Y%m%d')\n #down_otc_3big(startdate,enddate)\n generate_otc_3big(startdate,enddate) \n\n else :\n \n print(lno(),'fun -d startdate enddate')\n elif sys.argv[1]=='-p' :\n print (lno(),len(sys.argv))\n if len(sys.argv)==4 :\n #參數2:開始日期 參數3:結束日期\n startdate=datetime.strptime(sys.argv[2],'%Y%m%d')\n enddate=datetime.strptime(sys.argv[3],'%Y%m%d')\n generate_otc_3big(startdate,enddate) \n \n\n else :\n print (lno(),'func -p startdata enddate') \n elif sys.argv[1]=='-g' :\n print (lno(),len(sys.argv))\n if len(sys.argv)==3 :\n #參數2:開始日期 \n startdate=datetime.strptime(sys.argv[2],'%Y%m%d')\n foreign =get_foreign_investment(startdate)\n #df['外資buy']=df['外資buy'].astype('float64') \n \n print(lno(),foreign)\n \n\n else :\n print (lno(),'func -g date') \n else: \n objdatetime=datetime.strptime(sys.argv[1],'%Y%m%d')\n \n ", "sub_path": "otc_big3.py", "file_name": "otc_big3.py", "file_ext": "py", "file_size_in_byte": 12106, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "inspect.currentframe", "line_number": 23, "usage_type": "call"}, {"api_name": "inspect.getframeinfo", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "inspect.currentframe", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 29, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.read_html", "line_number": 63, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 65, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 79, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path", "line_number": 94, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 95, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 165, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_records", "line_number": 169, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 169, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 171, "usage_type": "call"}, {"api_name": "os.path", "line_number": 171, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 172, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 189, "usage_type": "call"}, {"api_name": "os.path", "line_number": 189, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 192, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 204, "usage_type": "call"}, {"api_name": "os.path", "line_number": 204, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 207, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 229, "usage_type": "call"}, {"api_name": "os.path", "line_number": 229, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 232, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 258, "usage_type": "attribute"}, {"api_name": "stock_comm.get_date", "line_number": 259, "usage_type": "call"}, {"api_name": "stock_comm.get_date", "line_number": 260, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 265, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 267, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 269, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 269, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 269, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 270, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 270, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 270, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 277, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 278, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 279, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 281, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 281, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 281, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 282, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 282, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 282, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 288, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 289, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 290, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 292, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 292, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 292, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 302, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 302, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 302, "usage_type": "attribute"}]} +{"seq_id": "55673156", "text": "#!/usr/bin/env python\n\n# Given an assembly file of genomic DNA and a file containing target proteins:\n# 1. Use exonerate to determine hits for each contig.\n# 2. Load contigs into a Biopython seqRecord dictionary.\n# 3. For each protein hit, create a FASTA file with all contigs.\n# a. The contigs will be in order of their hit location to the target proteins.\n\n######REQUIREMENTS########\n# Python 2.6 or later\n# exonerate in your $PATH\n# Biopython\n##########################\n\nimport sys, os, subprocess,math,argparse,logging\nfrom Bio import SeqIO\nfrom Bio.Seq import Seq\nfrom Bio.SeqRecord import SeqRecord\n\n#id_threshold = 55 #Percent identity between protein and contig hit.\nfirst_search_filename = \"exonerate_results.fasta\"\n\ndef initial_exonerate(proteinfilename, assemblyfilename,prefix):\n \"\"\"Conduct exonerate search, returns a dictionary of results.\n Using the ryo option in exonerate, the header should contain all the useful information.\"\"\"\n logger = logging.getLogger(\"pipeline\")\n \n outputfilename = \"%s/exonerate_results.fasta\" %prefix\n exonerate_ryo = '\">%ti,%qi,%qab,%qae,%pi,(%tS),%tab,%tae\\\\n%tcs\\\\n\"'\n exonerate_command = \"exonerate -m protein2genome --showalignment no --showvulgar no -V 0 --ryo %s %s %s >%s\" % (exonerate_ryo,proteinfilename,assemblyfilename,outputfilename)\n \n logger.debug(exonerate_command)\n #print exonerate_ryo\n #proc = subprocess.Popen(['exonerate','-m','protein2genome','--showalignment','no','-V','0','--showvulgar','no','--ryo',exonerate_ryo,proteinfilename,assemblyfilename])\n proc = subprocess.call(exonerate_command,shell=True)\n protHitsCount = 0\n #proc.wait()\n records = SeqIO.to_dict(SeqIO.parse(outputfilename,'fasta'))\n #proc.stdout.close()\n \n return records\n\ndef protein_sort(records):\n \"\"\"Given the Biopython dictionary, return a dictionary of proteins indexed by their hits.\"\"\" \n proteinHits = {}\n for contig in records:\n hit = records[contig].id.split(\",\")\n protein = hit[1]\n if protein in proteinHits:\n proteinHits[protein][\"assemblyHits\"].append(\",\".join(hit))\n proteinHits[protein][\"hit_start\"].append(int(hit[2]))\n proteinHits[protein][\"hit_end\"].append(int(hit[3]))\n proteinHits[protein][\"percentid\"].append(float(hit[4]))\n proteinHits[protein][\"hit_strand\"].append(hit[5][1])\n proteinHits[protein][\"target_begin\"].append(int(hit[6]))\n proteinHits[protein][\"target_end\"].append(int(hit[7]))\n else:\n proteinHits[protein] = {\"assemblyHits\" : [\",\".join(hit)],\n \"hit_start\" : [int(hit[2])],\n \"hit_end\" : [int(hit[3])],\n \"percentid\" : [float(hit[4])],\n \"hit_strand\" : [hit[5][1]],\n \"target_begin\" : [int(hit[6])],\n \"target_end\" : [int(hit[7])],\n \"name\" : protein\n }\n return proteinHits\n\ndef sort_key(elem):\n '''Sort by start location (increasing) then by end location (increasing), then by depth (decreasing)'''\n return elem[0],elem[1],-elem[2]\n\ndef get_contig_order(prot):\n \"\"\"Given the dictionary of hits for a protein, return the dictionary with the fields sorted by start location.\"\"\"\n logger = logging.getLogger(\"pipeline\")\n \n tuplist =[(prot[\"hit_start\"][i],prot[\"hit_end\"][i],float(prot[\"assemblyHits\"][i].split(\",\")[0].split(\"_\")[5])) for i in range(len(prot[\"hit_start\"]))]\n logger.debug(\"before sorting: {}\".format(\" \".join(prot[\"assemblyHits\"])))\n logger.debug( tuplist )\n sorting_order = sorted(list(range(len(tuplist))),key=lambda k:sort_key(tuplist[k]))\n \n prot[\"assemblyHits\"] = [prot[\"assemblyHits\"][i] for i in sorting_order]\n prot[\"hit_start\"] = [prot[\"hit_start\"][i] for i in sorting_order]\n prot[\"hit_end\"] = [prot[\"hit_end\"][i] for i in sorting_order]\n prot[\"percentid\"] = [prot[\"percentid\"][i] for i in sorting_order]\n prot[\"hit_strand\"] = [prot[\"hit_strand\"][i] for i in sorting_order]\n \n logger.debug(\"After sorting: {}\".format(\" \".join(prot[\"assemblyHits\"]))) \n return prot\n\ndef filter_by_percentid(prot,thresh):\n \"\"\"Given a protein dictionary, return a protein dictionary minus entries with percentID below a threshold\"\"\"\n kept_indicies = [i for i in range(len(prot[\"percentid\"])) if prot[\"percentid\"][i] > thresh]\n return keep_indicies(kept_indicies,prot) \n\ndef supercontig_exonerate(supercontig,protseq,prefix):\n \"\"\"Given a long, joined contig and a protein sequence, return the exonerate hit(s)\"\"\"\n logger = logging.getLogger(\"pipeline\")\n\n exonerate_ryo = '>%ti,%qi,%qab,%qae,%pi,(%tS)\\\\n%tcs\\\\n'\n temp_prot_filename = \"%s/temp.prot.fa\"%prefix\n temp_contig_filename = \"%s/temp.contig.fa\"%prefix\n SeqIO.write(protseq,temp_prot_filename,'fasta')\n SeqIO.write(supercontig,temp_contig_filename,'fasta')\n logger.debug(\"Conducting exonerate search on supercontig\")\n proc = subprocess.Popen(['exonerate','-m','protein2genome','--showalignment','no','-V','0','--showvulgar','no','--ryo',exonerate_ryo,temp_prot_filename,temp_contig_filename],stdout=subprocess.PIPE,universal_newlines=True)\n\n proc.wait()\n #print proc.stdout.readlines()\n supercontig_cds = [i for i in SeqIO.parse(proc.stdout,'fasta') if float(i.id.split(\",\")[4])>55]\n logger.debug(\"Supercontig lengths: %s\" % \" \".join([str(len(x.seq)) for x in supercontig_cds]))\n return supercontig_cds\n\ndef sort_byhitloc(seqrecord):\n \"\"\"Key function for sorting based on the start location of a hit record.\"\"\"\n return int(seqrecord.id.split(\",\")[2])\n\ndef subsume_supercontigs(supercontigs):\n \"\"\"If one supercontig has a start and end location greater than all the others, throw the rest out\"\"\"\n logger = logging.getLogger(\"pipeline\")\n supercontig_rangelist = [(int(x.id.split(\",\")[2]),int(x.id.split(\",\")[3])) for x in supercontigs]\n supercontig_ids = [x.id for x in supercontigs]\n logger.debug(\"Checking these ranges for supercontig: \")\n logger.debug(supercontig_rangelist)\n seqs_to_keep = range_connectivity(supercontig_rangelist,supercontig_ids)\n logger.debug(\"Keeping these contigs: \")\n logger.debug([supercontigs[x].id for x in seqs_to_keep])\n return [supercontigs[x] for x in seqs_to_keep]\n\ndef write_exonerate_stats(contig_id_list,prefix):\n '''Given a list of IDs from initial exonerate search, write info to a standard file'''\n with open(\"{}/exonerate_stats.csv\".format(prefix),'w') as exonerate_statsfile:\n exonerate_statsfile.write(\"\\n\".join(contig_id_list)+'\\n')\n\n \ndef fullContigs(prot,sequence_dict,assembly_dict,protein_dict,prefix):\n \"\"\"Generates a contig from all hits to a protein. \n If more than one hit, conduct a second exonerate search with the original contigs\n stitched together.\"\"\"\n logger = logging.getLogger(\"pipeline\")\n #logger.setLevel(logger.debug)\n numHits = len(prot[\"assemblyHits\"])\n sequence_list = []\n contigHits = []\n \n logger.debug(\"All hits:\")\n logger.debug(prot[\"assemblyHits\"])\n write_exonerate_stats(prot[\"assemblyHits\"],prefix)\n\n \n #print numHits\n if numHits == 1:\n return str(sequence_dict[prot[\"assemblyHits\"][0]].seq) #If only one hit to this protein.\n else:\n for hit in range(len(prot[\"assemblyHits\"])):\n assembly_seq_name = prot[\"assemblyHits\"][hit].split(\",\")[0]\n logger.debug(\"Protein hit {} from {} to {} with {}% id on strand {}\".format(assembly_seq_name,\n prot[\"hit_start\"][hit],\n prot[\"hit_end\"][hit],\n prot[\"percentid\"][hit],\n prot[\"hit_strand\"][hit]\n ))\n if assembly_seq_name not in contigHits: #Only add each contig once.\n if prot[\"hit_strand\"][hit] == \"+\":\n sequence_list.append(assembly_dict[assembly_seq_name])\n else:\n sequence_list.append(assembly_dict[assembly_seq_name].reverse_complement())\n contigHits.append(assembly_seq_name)\n# logger.debug([i for i in prot[\"assemblyHits\"]])\n# logger.debug([(prot[\"hit_start\"][i],prot[\"hit_end\"][i]) for i in range(len(prot[\"hit_start\"]))])\n# logger.debug(prot[\"hit_strand\"])\n# logger.debug(prot[\"percentid\"])\n# logger.debug(\"\\n\".join([\"{} {}\".format(x,assembly_dict[x].seq) for x in contigHits]))\n supercontig = SeqRecord(Seq(\"\".join(str(b.seq) for b in sequence_list)),id=prot[\"name\"])\n\n #Need to remove contigs if they have the same basename\n supercontig_cds = supercontig_exonerate(supercontig,protein_dict[prot[\"name\"]],prefix)\n \n #Sort the supercontigs by hit location to the protein.\n joined_supercontig_cds = [b for b in supercontig_cds]\n joined_supercontig_cds.sort(key=sort_byhitloc,reverse=True)\n #logger.info([x for x in prot['assemblyHits'] if x in sequence_list])\n #write_exonerate_stats([x for x in prot['assemblyHits'] if x in sequence_list])\n\n #Get rid of supercontig sequences that are subsumed by longer sequences on the same stretch.\n joined_supercontig_cds = subsume_supercontigs(joined_supercontig_cds)\n \n \n SeqIO.write(joined_supercontig_cds,'%s/supercontig_exonerate.fasta'%prefix,'fasta') \n if len(joined_supercontig_cds) == 1:\n logger.debug(\"One sequence remaining\")\n return str(joined_supercontig_cds[0].seq)\n #One more Exonerate, just to be sure.\n superdupercontig = SeqRecord(Seq(\"\".join(str(b.seq) for b in joined_supercontig_cds)),id=prot[\"name\"])\n final_supercontig = [x for x in supercontig_exonerate(superdupercontig,protein_dict[prot[\"name\"]],prefix)]\n final_supercontig.sort(key=sort_byhitloc,reverse=True)\n final_supercontig = subsume_supercontigs(final_supercontig)\n \n \n return str(Seq(\"\".join(str(b.seq) for b in final_supercontig)))\n return str(Seq(\"\".join(str(b.seq) for b in joined_supercontig_cds))) \n #print joined_supercontig_cds\n #print \"\"\n #return joined_supercontig_cds\n\n\ndef find_longest_hit(prot):\n \"\"\"Given a protein dictionary, determine the assembly hit with the longest sequence\"\"\"\n max_hit_length = 0\n max_hit_loc = 0\n for i in range(len(prot[\"hit_start\"])):\n hit_length = abs(int(prot[\"hit_start\"][i]) - int(prot[\"hit_end\"][i]))\n if hit_length > max_hit_length:\n hit_length = max_hit_length\n max_hit_loc = i\n return max_hit_loc\n\ndef keep_indicies(kept_indicies, prot):\n \"\"\"Given a list of indicies to keep and a protein dictionary, return the dictionary with only the specified entries remaining\"\"\"\n assHit = []\n hitstart = []\n hitend = []\n percentid = []\n strands = []\n targetbegin =[]\n targetend =[]\n\n for a in kept_indicies:\n assHit.append(prot[\"assemblyHits\"][a])\n hitstart.append(prot[\"hit_start\"][a])\n hitend.append(prot[\"hit_end\"][a])\n percentid.append(prot[\"percentid\"][a])\n strands.append(prot[\"hit_strand\"][a])\n targetbegin.append(prot[\"target_begin\"][a])\n targetend.append(prot[\"target_end\"][a])\n\n prot[\"assemblyHits\"] = assHit\n prot[\"hit_start\"] = hitstart\n prot[\"hit_end\"] = hitend\n prot[\"percentid\"] = percentid\n prot[\"hit_strand\"] = strands\n prot[\"target_begin\"] = targetbegin\n prot[\"target_end\"] = targetend\n\n return prot\n\ndef overlapping_contigs(prot,length_pct,depth_multiplier):\n \"\"\"Given a protein dictionary, determine whether the hit ranges are overlapping,\n and save only those contigs that are not completely subsumed by other contigs.\"\"\"\n logger = logging.getLogger(\"pipeline\")\n range_list = [(prot[\"hit_start\"][i],prot[\"hit_end\"][i]) for i in range(len(prot[\"hit_start\"]))]\n \n logger.debug(range_list)\n kept_indicies = range_connectivity(range_list,prot[\"assemblyHits\"],prot_length = prot[\"reflength\"],length_pct = length_pct,depth_multiplier=depth_multiplier)\n logger.debug(kept_indicies)\n return keep_indicies(kept_indicies,prot)\n\n\ndef best_by_percent_id(assemblyHits,full_length_indicies):\n '''Given a list of contig names, return the one with the best percent identity (fourth comma delimited field)'''\n logger = logging.getLogger(\"pipeline\")\n max_percentid = 0\n for i in range(len(full_length_indicies)):\n percentid = float(assemblyHits[full_length_indicies[i]].split(\",\")[4])\n if percentid > max_percentid:\n logger.debug(\"percent_id: {}, maxpercent_id: {}\".format(percentid,max_percentid))\n to_keep = full_length_indicies[i]\n max_percentid = percentid\n return to_keep\n \ndef best_by_depth(assemblyHits,full_length_indicies,thresh=10):\n '''If one contig has a depth that is 10x more than all the others, return that one, else return None'''\n logger=logging.getLogger(\"pipeline\")\n depths = []\n for i in range(len(full_length_indicies)):\n depths.append((full_length_indicies[i],float(assemblyHits[full_length_indicies[i]].split(',')[0].split(\"_\")[5])))\n depths.sort(reverse=True,key=lambda x: x[1])\n logger.debug(depths)\n depth_threshold = depths[0][1] / thresh\n logger.debug(\"Depth threshold: {}\".format(depth_threshold))\n top_depth_best = all(i[1] <= depth_threshold for i in depths[1:]) \n if top_depth_best:\n best_depth_contig = depths[0][0]\n logger.debug(\"Contig {} with depth {} is more than {} times greater depth than other contigs\".format(best_depth_contig,depths[0][1],thresh))\n return best_depth_contig\n logger.debug(\"All contigs have similar depth\")\n\n return None \n\n\ndef range_connectivity(range_list,assemblyHits=None,prot_length=None,length_pct = 1,depth_multiplier = None,use_depth=False):\n \"\"\"Given two sorted lists, representing the beginning and end of a range,\n Determine \"connectivity\" between consecutive elements of the list.\n For each connected segment, determine whether one segement \"subsumes\" the other.\"\"\"\n \n logger = logging.getLogger(\"pipeline\")\n\n starts = [a[0] for a in range_list]\n ends = [a[1] for a in range_list]\n \n if depth_multiplier:\n use_depth = True\n \n subsumed_ranges = []\n collapsed_ranges = []\n full_length_indicies = []\n num_breaks = 0\n if prot_length:\n max_length = prot_length\n else:\n max_length = max(ends) - min(starts)\n \n for i in range(len(range_list)):\n if abs(starts[i] - ends[i]) > max_length * length_pct:\n logger.debug(\"including long contig {}\".format(range_list[i])) \n full_length_indicies.append(i)\n subsumed_ranges = [range_list[i]]\n elif starts[i] == min(starts) and ends[i] == max(ends):\n logger.debug(\"Contig {} has range that subsumes all others!\".format(i))\n subsumed_ranges = [range_list[i]]\n full_length_indicies.append(i)\n else:\n if len(full_length_indicies) > 0:\n logger.debug(\"removing {}\".format(range_list[i]))\n else:\n subsumed_ranges.append(range_list[i])\n \n #If there are multiple full length hits, return the one with the best percent identity.\n if assemblyHits:\n if len(full_length_indicies) > 1:\n if use_depth:\n to_keep = best_by_depth(assemblyHits,full_length_indicies,depth_multiplier)\n if to_keep:\n return [to_keep]\n else:\n to_keep = best_by_percent_id(assemblyHits,full_length_indicies) \n return [to_keep]\n else:\n to_keep = best_by_percent_id(assemblyHits,full_length_indicies) \n return [to_keep]\n \n #If multiple contigs start at the same minimum (or end at the same maximum), keep the longest ones.\n if len(subsumed_ranges) > 1:\n best_start_end = 0\n best_end_start = 1000000000\n for j in range(len(subsumed_ranges)):\n if subsumed_ranges[j][0] == min(starts):\n if subsumed_ranges[j][1] > best_start_end:\n best_start_end = subsumed_ranges[j][1]\n longest_left = j\n\n elif subsumed_ranges[j][1] == max(ends):\n if subsumed_ranges[j][0] < best_end_start:\n best_end_start = subsumed_ranges[j][0]\n longest_right = j\n else:\n collapsed_ranges.append(subsumed_ranges[j])\n \n logger.debug(\"Best end start: {}\".format(best_end_start))\n logger.debug(\"Best start end: {}\".format(best_start_end))\n collapsed_ranges.append(subsumed_ranges[longest_left])\n collapsed_ranges.append(subsumed_ranges[longest_right])\n else:\n collapsed_ranges = subsumed_ranges \n\n if False: #num_breaks == 0:\n kept_indicies = [range_list.index(i) for i in connected_ranges]\n return kept_indicies\n else:\n #List contains other lists, need to flatten this to just tuples.\n flattened_list = []\n for a in range(len(collapsed_ranges)):\n if isinstance(collapsed_ranges[a], list):\n for i in collapsed_ranges[a]:\n flattened_list.append(i)\n else:\n flattened_list.append(collapsed_ranges[a])\n kept_indicies = [range_list.index(i) for i in flattened_list]\n return kept_indicies\n \n \ndef tuple_overlap(a,b):\n \"\"\"Given two tuples of length two, determine if the ranges overlap\"\"\"\n return a[0] < b[0] < a[1] or b[0] < a[0] < b[1]\n\ndef reciprocal_best_hit(prot,proteinHits):\n \"\"\"Given a protein dictionary and the dictionary of all protein dictionaries,\n Return the protein dictionary minus any contigs that have higher percentage hits to other proteins.\"\"\"\n logger = logging.getLogger(\"pipeline\")\n\n protname = prot[\"name\"]\n kept_indicies=[]\n for contig in prot[\"assemblyHits\"]:\n contigname = contig.split(\",\")[0]\n contig_idx = prot[\"assemblyHits\"].index(contig)\n maxProt = protname\n for otherProt in proteinHits:\n #print \"checking %s vs %s\" %(protname, proteinHits[otherProt][\"name\"])\n otherprot_contiglist = [x.split(\",\")[0] for x in proteinHits[otherProt][\"assemblyHits\"]]\n if proteinHits[otherProt][\"name\"] != protname:\n if contigname in otherprot_contiglist:\n full_contigname = [b for b in proteinHits[otherProt][\"assemblyHits\"] if contigname in b][0]\n logger.debug(\"%s %s\" %(contig, full_contigname))\n otherHit_idx = proteinHits[otherProt][\"assemblyHits\"].index(full_contigname)\n \n target_ranges = [sorted((prot[\"target_begin\"][contig_idx],prot[\"target_end\"][contig_idx])),sorted((proteinHits[otherProt][\"target_begin\"][otherHit_idx],proteinHits[otherProt][\"target_end\"][otherHit_idx]))]\n logger.debug(repr(target_ranges))\n #Check that the two contig hits have overlapping ranges.\n if tuple_overlap(target_ranges[0],target_ranges[1]): \n logger.debug(\"%s %s\"%(repr(prot[\"percentid\"][contig_idx]),repr(proteinHits[otherProt][\"percentid\"][otherHit_idx])))\n if prot[\"percentid\"][contig_idx] < proteinHits[otherProt][\"percentid\"][otherHit_idx]:\n logger.debug(\"contig %s is a better hit to %s\" %(contigname,otherProt))\n maxProt = proteinHits[otherProt][\"name\"]\n else:\n logger.debug(\"ranges did not overlap\")\n if maxProt == protname:\n kept_indicies.append(contig_idx)\n return keep_indicies(kept_indicies,prot) \n\ndef paralog_test(exonerate_hits,prot,prefix):\n \"\"\"Gives a warning if there are multiple hits of long length to the same protein\"\"\"\n logger = logging.getLogger(\"pipeline\")\n protlength = len(prot)\n hitlengths = [abs(int(x.split(\",\")[2]) - int(x.split(\",\")[3])) for x in exonerate_hits[\"assemblyHits\"]]\n logger.debug(\"protein length: {}\".format(protlength))\n logger.debug(\"Hit lengths:\")\n logger.debug(hitlengths)\n longhits = [x > 0.75*protlength for x in hitlengths]\n if sum(longhits) > 1:\n sys.stderr.write(\"WARNING: Multiple long-length exonerate hits for {}. Check for paralogs!\\n\".format(prot.id))\n with open(\"{}/paralog_warning.txt\".format(prefix),'w') as pw:\n for hit in range(len(exonerate_hits[\"assemblyHits\"])):\n if longhits[hit]:\n pw.write(prot.id+ \"\\t\"+exonerate_hits[\"assemblyHits\"][hit] + \"\\n\") \n\ndef myTranslate(nucl):\n \"\"\"Given a raw sequence of nucleotides, return raw sequence of amino acids.\"\"\"\n #print nucl\n nucseq = Seq(nucl)\n #print nucseq\n aminoseq = nucseq.translate()\n return str(aminoseq)\n\ndef report_no_sequences(protname):\n sys.stderr.write(\"No valid sequences remain for {}!\\n\".format(protname))\n\ndef help():\n print(\"USAGE: python hybseq_pipeline.py proteinfile assemblyfile prefix\")\n print(\"The program Exonerate must be in your $PATH.\")\n print(\"You must have BioPython installed\")\n print(\"A protein and a nucleotide directory will be created in the current directory with the prefix.\")\n return \n\ndef main(): \n \n parser = argparse.ArgumentParser(description=\"exonerate_hits.py; Generate gene-by-gene protein and nucleotide files from Bait Capture Assembly\")\n #parser.add_argument(\"-v\", \"--verbose\",help=\"Report progress of pipeline to stdout\",\n # action=\"store_const\",dest=\"loglevel\",const=logging.INFO, default=logging.WARNING)\n parser.add_argument(\"--debug\",help=\"Print debugging information for development testing.\",\n action=\"store_true\",dest=\"loglevel\",default=False)\n parser.add_argument(\"proteinfile\",help=\"FASTA file containing one 'bait' sequence per protein.\")\n parser.add_argument(\"assemblyfile\",help=\"FASTA file containing DNA sequence assembly.\")\n parser.add_argument(\"--prefix\",help=\"\"\"Prefix for directory, files, and sequences generated from this assembly. \n If not specified, will be extracted from assembly file name.\"\"\",default=None)\n parser.add_argument(\"--no_sequences\",help=\"Do not generate protein and nucleotide sequence files.\", action=\"store_true\",default=False)\n parser.add_argument(\"--first_search_filename\",help=\"Location of previously completed Exonerate results. Useful for testing.\",default=\"no\")\n parser.add_argument(\"-t\",\"--threshold\",help=\"Threshold for Percent Identity between contigs and proteins. default = 55%%\",default=55,type=int)\n parser.add_argument(\"--length_pct\",help=\"Include an exonerate hit if it is at least as long as X percentage of the reference protein length. Default = 100%%\",default=90,type=int)\n parser.add_argument(\"--depth_multiplier\",help=\"Accept any full-length hit if it has a coverage depth X times the next best hit. Set to zero to not use depth. Default = 10\",default=10,type=int)\n \n \n args = parser.parse_args()\n\n proteinfilename = args.proteinfile\n assemblyfilename = args.assemblyfile\n if args.prefix:\n prefix = args.prefix\n if os.path.exists(prefix):\n pass\n else:\n os.mkdir(prefix)\n else:\n prefix = os.path.basename(assemblyfilename).split(\".\")[0] \n\n logger = logging.getLogger(\"pipeline\")\n ch = logging.StreamHandler()\n logger.addHandler(ch)\n if args.loglevel:\n logger.setLevel(logging.DEBUG)\n else:\n logger.setLevel(logging.INFO)\n formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n ch.setFormatter(formatter)\n #formatter = logging.Formatter('[%(levelname)s] %(message)s') #Commenting this out stops messages from appearing twice.\n #handler = logging.StreamHandler()\n #handler.setFormatter(formatter)\n #logger.addHandler(handler)\n try:\n proteinfile = open(proteinfilename)\n except IOError:\n print(\"The file %s could not be opened!\" %proteinfilename)\n return()\n \n try:\n assemblyfile = open(assemblyfilename)\n except IOError:\n print(\"The file %s could not be opened!\" % assemblyfilename)\n return()\n assembly_dict = SeqIO.to_dict(SeqIO.parse(assemblyfile,'fasta'))\n protein_dict = SeqIO.to_dict(SeqIO.parse(proteinfile,'fasta'))\n \n if os.path.exists(args.first_search_filename): #Shortcut for Testing purposes\n logger.info(\"Reading initial exonerate results from file {}.\".format(first_search_filename))\n sequence_dict = SeqIO.to_dict(SeqIO.parse(first_search_filename,'fasta'))\n else:\n #logger.info(\"Starting exonerate search, please wait.\")\n sequence_dict = initial_exonerate(proteinfilename,assemblyfilename,prefix)\n proteinHits = protein_sort(sequence_dict)\n\n sys.stderr.write(\"There were {} exonerate hits for {}.\\n\".format(len(sequence_dict),proteinfilename))\n #print \"There were %i unique proteins hit.\" % len(proteinHits)\n \n directory_name = \"%s/sequences/FNA\" % prefix\n if not os.path.exists(directory_name):\n os.makedirs(directory_name)\n\n directory_name = \"%s/sequences/FAA\" % prefix\n if not os.path.exists(directory_name):\n os.makedirs(directory_name)\n \n for prot in proteinHits:\n \n logger.debug(prot)\n #Put contigs in order along the protein.\n #logging.info(\"Searching for best hit to protein: %s\" % proteinHits[prot][\"name\"])\n# logger.debug(\"Initial hits: %s\" % \" \".join(proteinHits[prot][\"assemblyHits\"]))\n logger.debug(\"Initial hits: %s\" % len(proteinHits[prot][\"assemblyHits\"]))\n\n paralog_test(proteinHits[prot],protein_dict[prot],prefix)\n proteinHits[prot][\"reflength\"] = len(protein_dict[prot])\n \n proteinHits[prot] = get_contig_order(proteinHits[prot])\n# logger.debug(\"After get_contig_order: %s\" % \" \".join(proteinHits[prot][\"assemblyHits\"]))\n logger.debug(\"After get_contig_order: %d\" % len(proteinHits[prot][\"assemblyHits\"]))\n #Remove contigs that are suboptimal hits. Only one protein hit allowed per contig.\n\n proteinHits[prot] = reciprocal_best_hit(proteinHits[prot],proteinHits)\n# logger.debug(\"After RBH: %s\" % \" \".join(proteinHits[prot][\"assemblyHits\"]))\n logger.debug(\"After RBH: %d\" % len(proteinHits[prot][\"assemblyHits\"]))\n if len(proteinHits[prot][\"assemblyHits\"]) == 0:\n report_no_sequences(proteinHits[prot][\"name\"])\n continue #All hits have been filtered out\n \n #Filter out contigs with a hit below a threshold\n proteinHits[prot] = filter_by_percentid(proteinHits[prot],args.threshold)\n# logger.debug(\"After filter_by_percent_id: %s\" % \" \".join(proteinHits[prot][\"assemblyHits\"]))\n logger.debug(\"After filter_by_percent_id: %d\" % len(proteinHits[prot][\"assemblyHits\"]))\n if len(proteinHits[prot][\"assemblyHits\"]) == 0:\n report_no_sequences(proteinHits[prot][\"name\"])\n continue #All hits have been filtered out\n \n #Delete contigs if their range is completely subsumed by another hit's range.\n proteinHits[prot] = overlapping_contigs(proteinHits[prot],args.length_pct*0.01,args.depth_multiplier)\n# logger.debug(\"After overlapping_contigs: %s\" % \" \".join(proteinHits[prot][\"assemblyHits\"]))\n logger.debug(\"After overlapping_contigs: %d\" % len(proteinHits[prot][\"assemblyHits\"]))\n #Stitch together a \"supercontig\" containing all the hits and conduct a second exonerate search. \n if len(proteinHits[prot][\"assemblyHits\"]) == 0:\n report_no_sequences(proteinHits[prot][\"name\"])\n continue #All hits have been filtered out\n\n nucl_sequence = fullContigs(proteinHits[prot],sequence_dict,assembly_dict,protein_dict,prefix)\n\n if args.no_sequences:\n continue\n else:\n amino_sequence = myTranslate(nucl_sequence)\n seqID = prefix.split(\"/\")[-1].strip(\"/\")\n sys.stderr.write(\"Writing amino acid sequence, length: {}\\n\".format(len(amino_sequence)))\n sys.stdout.write(\"{}\\t{}\\n\".format(prot.split(\"-\")[-1],len(amino_sequence)))\n amino_filename = \"%s/sequences/FAA/%s.FAA\" % (prefix,prot.split(\"-\")[-1])\n amino_file = open(amino_filename,'w')\n amino_file.write(\">%s\\n%s\\n\" % (seqID,amino_sequence))\n amino_file.close()\n \n nucleo_filename = \"%s/sequences/FNA/%s.FNA\" % (prefix,prot.split(\"-\")[-1])\n nucleo_file = open(nucleo_filename,'w')\n nucleo_file.write(\">%s\\n%s\\n\" % (seqID,nucl_sequence))\n nucleo_file.close()\n# if \"temp.contig.fa\" in os.listdir(prefix): \n# os.remove(\"%s/temp.contig.fa\" % prefix)\n# os.remove(\"%s/temp.prot.fa\" % prefix)\n proteinfile.close()\n assemblyfile.close()\n \n\nif __name__ == \"__main__\":main()", "sub_path": "hybpiper/program/exonerate_hits.py", "file_name": "exonerate_hits.py", "file_ext": "py", "file_size_in_byte": 29136, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 26, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 35, "usage_type": "call"}, {"api_name": "Bio.SeqIO.to_dict", "line_number": 38, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 38, "usage_type": "name"}, {"api_name": "Bio.SeqIO.parse", "line_number": 38, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 75, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 98, "usage_type": "call"}, {"api_name": "Bio.SeqIO.write", "line_number": 103, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 103, "usage_type": "name"}, {"api_name": "Bio.SeqIO.write", "line_number": 104, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 104, "usage_type": "name"}, {"api_name": "subprocess.Popen", "line_number": 106, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 106, "usage_type": "attribute"}, {"api_name": "Bio.SeqIO.parse", "line_number": 110, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 110, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 120, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 140, "usage_type": "call"}, {"api_name": "Bio.SeqRecord.SeqRecord", "line_number": 174, "usage_type": "call"}, {"api_name": "Bio.Seq.Seq", "line_number": 174, "usage_type": "call"}, {"api_name": "Bio.SeqIO.write", "line_number": 189, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 189, "usage_type": "name"}, {"api_name": "Bio.SeqRecord.SeqRecord", "line_number": 194, "usage_type": "call"}, {"api_name": "Bio.Seq.Seq", "line_number": 194, "usage_type": "call"}, {"api_name": "Bio.Seq.Seq", "line_number": 200, "usage_type": "call"}, {"api_name": "Bio.Seq.Seq", "line_number": 201, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 250, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 261, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 273, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 296, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 389, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 422, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 430, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 430, "usage_type": "attribute"}, {"api_name": "Bio.Seq.Seq", "line_number": 439, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 445, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 445, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 456, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 478, "usage_type": "call"}, {"api_name": "os.path", "line_number": 478, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 481, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 483, "usage_type": "call"}, {"api_name": "os.path", "line_number": 483, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 485, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 486, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 489, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 491, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 492, "usage_type": "call"}, {"api_name": "Bio.SeqIO.to_dict", "line_number": 509, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 509, "usage_type": "name"}, {"api_name": "Bio.SeqIO.parse", "line_number": 509, "usage_type": "call"}, {"api_name": "Bio.SeqIO.to_dict", "line_number": 510, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 510, "usage_type": "name"}, {"api_name": "Bio.SeqIO.parse", "line_number": 510, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 512, "usage_type": "call"}, {"api_name": "os.path", "line_number": 512, "usage_type": "attribute"}, {"api_name": "Bio.SeqIO.to_dict", "line_number": 514, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 514, "usage_type": "name"}, {"api_name": "Bio.SeqIO.parse", "line_number": 514, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 520, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 520, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 524, "usage_type": "call"}, {"api_name": "os.path", "line_number": 524, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 525, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 528, "usage_type": "call"}, {"api_name": "os.path", "line_number": 528, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 529, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 578, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 578, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 579, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 579, "usage_type": "attribute"}]} +{"seq_id": "460616752", "text": "\"\"\"\r\nLOA\r\n\"\"\"\r\nimport arcade\r\nfrom piece import Piece\r\nfrom tile import Tile\r\nfrom board import Board\r\nfrom ai_handler import AI_Handler\r\nimport json\r\nimport subprocess\r\nimport string\r\n\r\n# Screen title and size\r\nSCREEN_WIDTH = 1024\r\nSCREEN_HEIGHT = 768\r\nSCREEN_TITLE = \"LOA\"\r\n\r\nTILE_WIDTH = 80\r\n\r\nBLACK = 1\r\nWHITE = -1\r\nNONE = 0\r\n\r\n\r\nSELECTED = True\r\n\r\nSELECT_SIZE = 0\r\nSELECT_TYPE = 1\r\nGAME_STARTED = 2\r\n\r\nHUMAN_V_HUMAN = 0\r\nHUMAN_V_AI = 1\r\n\r\n\r\nclass MyGame(arcade.Window):\r\n \"\"\" Main application class. \"\"\"\r\n\r\n def __init__(self):\r\n super().__init__(SCREEN_WIDTH, SCREEN_HEIGHT, SCREEN_TITLE)\r\n\r\n arcade.set_background_color(arcade.color.BLACK_OLIVE)\r\n\r\n # TILES\r\n self.tile_list = None\r\n\r\n self.board = None\r\n\r\n self.turn = None\r\n self.state = None\r\n self.winner = None\r\n\r\n self.select_size = None\r\n self.display_state = None\r\n self.game_type = None\r\n\r\n self.ai_process = None\r\n self.error_message = None\r\n\r\n def setup(self):\r\n \"\"\" Set up the game here. Call this function to restart the game. \"\"\"\r\n self.select_size = arcade.SpriteList()\r\n self.display_state = SELECT_TYPE\r\n size6 = arcade.SpriteSolidColor( (int)(2 * SCREEN_WIDTH / 3), (int)(0.2 * SCREEN_HEIGHT), arcade.color.ALLOY_ORANGE)\r\n size8 = arcade.SpriteSolidColor( int(2 * SCREEN_WIDTH / 3), (int)(0.2 * SCREEN_HEIGHT), arcade.color.ALLOY_ORANGE)\r\n size6.set_position(SCREEN_WIDTH/2, SCREEN_HEIGHT * 0.75)\r\n size8.set_position(SCREEN_WIDTH/2, SCREEN_HEIGHT * 0.25)\r\n self.select_size.append(size6)\r\n self.select_size.append(size8)\r\n self.error_message = \"\"\r\n\r\n \r\n \r\n def start_game(self, size):\r\n self.tile_list: arcade.SpriteList = arcade.SpriteList()\r\n self.board = Board(size)\r\n self.winner = NONE\r\n self.display_state = GAME_STARTED\r\n\r\n self.state = not SELECTED\r\n\r\n for i in range(size):\r\n for j in range(size):\r\n color = arcade.color.CERULEAN\r\n if (i+j)%2 == 1:\r\n color = arcade.color.CERULEAN_BLUE\r\n tile = Tile(color, i, j, size)\r\n self.tile_list.append(tile) \r\n\r\n \r\n self.turn = BLACK\r\n\r\n if self.game_type == HUMAN_V_AI:\r\n msg = str(size) + \"\\n\"\r\n self.ai_process = AI_Handler(msg)\r\n\r\n \r\n\r\n def on_draw(self):\r\n \"\"\" Render the screen. \"\"\"\r\n # Clear the screen\r\n arcade.start_render()\r\n\r\n if self.display_state == GAME_STARTED:\r\n self.tile_list.draw()\r\n for i in range(self.board.size):\r\n arcade.draw_text(string.ascii_uppercase[i], self.tile_list[0].START_X + i * TILE_WIDTH, self.tile_list[0].START_Y - TILE_WIDTH, arcade.color.WHITE, 20)\r\n for i in range(self.board.size):\r\n arcade.draw_text(str(i), self.tile_list[0].START_X - TILE_WIDTH, self.tile_list[0].START_Y + i * TILE_WIDTH, arcade.color.WHITE, 20)\r\n self.board.draw()\r\n\r\n if (self.winner == WHITE):\r\n arcade.draw_text(\"WHITE WINS\", SCREEN_WIDTH/3, SCREEN_HEIGHT - 75, arcade.color.WHITE, 50)\r\n elif self.winner == BLACK:\r\n arcade.draw_text(\"BLACK WINS\", SCREEN_WIDTH/3, SCREEN_HEIGHT - 75, arcade.color.WHITE, 50)\r\n elif (self.turn == WHITE):\r\n arcade.draw_text(\"WHITE'S TURN\", SCREEN_WIDTH / 3, SCREEN_HEIGHT - 75, arcade.color.WHITE, 50)\r\n else:\r\n arcade.draw_text(\"BLACK'S TURN\", SCREEN_WIDTH / 3, SCREEN_HEIGHT - 75, arcade.color.WHITE, 50)\r\n\r\n elif self.display_state == SELECT_SIZE:\r\n self.select_size.draw()\r\n arcade.draw_text(\"6x6\", SCREEN_WIDTH / 2 - 50, SCREEN_HEIGHT * 0.75 - 25, arcade.color.WHITE, 50)\r\n arcade.draw_text(\"8x8\", SCREEN_WIDTH / 2 - 50, SCREEN_HEIGHT * 0.25 - 25, arcade.color.WHITE, 50)\r\n elif self.display_state == SELECT_TYPE:\r\n self.select_size.draw()\r\n arcade.draw_text(\"HUMAN vs. HUMAN\", SCREEN_WIDTH / 4, SCREEN_HEIGHT * 0.75 - 25, arcade.color.WHITE, 50)\r\n arcade.draw_text(\"HUMAN vs. AI\", SCREEN_WIDTH / 3, SCREEN_HEIGHT * 0.25 - 25, arcade.color.WHITE, 50)\r\n \r\n arcade.draw_text(self.error_message, SCREEN_WIDTH / 2 - 50, 25, arcade.color.RED, 50)\r\n \r\n\r\n\r\n\r\n\r\n\r\n\r\n def on_mouse_press(self, x, y, button, key_modifiers):\r\n \"\"\" Called when the user presses a mouse button. \"\"\"\r\n if self.display_state == GAME_STARTED:\r\n\r\n pieces = arcade.get_sprites_at_point((x,y), self.board.pieces)\r\n if len(pieces) > 0 and pieces[0].type == self.turn:\r\n #draws lines\r\n self.board.get_valid_moves(pieces[0])\r\n self.state = SELECTED\r\n return\r\n \r\n if self.state == SELECTED:\r\n #piece selected by user\r\n tiles = arcade.get_sprites_at_point((x,y), self.tile_list)\r\n if len(tiles) > 0:\r\n #returns a boolean\r\n init_x = self.board.selected_piece.pos_x\r\n init_y = self.board.selected_piece.pos_y\r\n valid_move = self.board.move_piece(init_x, init_y, tiles[0].pos_x, tiles[0].pos_y, self.turn)\r\n if valid_move:\r\n self.state = not SELECTED\r\n self.winner = self.board.check_end_state(self.turn)\r\n self.turn *= -1\r\n\r\n #disable controls\r\n if self.winner != NONE:\r\n self.turn = NONE\r\n elif self.game_type == HUMAN_V_AI and self.turn == WHITE:\r\n self.handle_ai_move(init_x, init_y, tiles[0].pos_x, tiles[0].pos_y, self.turn)\r\n else:\r\n self.error_message = \"INVALID MOVE\"\r\n \r\n return\r\n elif self.display_state == SELECT_SIZE:\r\n click = arcade.get_sprites_at_point((x,y), self.select_size)\r\n if len(click) > 0:\r\n if click[0].center_y == SCREEN_HEIGHT * 0.75:\r\n self.start_game(6)\r\n else:\r\n self.start_game(8)\r\n elif self.display_state == SELECT_TYPE:\r\n click = arcade.get_sprites_at_point((x,y), self.select_size)\r\n if len(click) > 0:\r\n if click[0].center_y == SCREEN_HEIGHT * 0.75:\r\n self.game_type = HUMAN_V_HUMAN\r\n else:\r\n self.game_type = HUMAN_V_AI\r\n self.display_state = SELECT_SIZE\r\n\r\n\r\n\r\n def handle_ai_move(self, x1, y1, x2, y2, turn):\r\n msg = str(x1) + \",\" + str(y1) + \",\" + str(x2) + \",\" + str(y2) + \"\\n\"\r\n print(\"SENT MOVE\", msg)\r\n self.ai_process.write(msg)\r\n moves = self.ai_process.read()\r\n print(\"Received move: \", moves)\r\n\r\n move_list = moves.split(\",\")\r\n valid_move = self.board.move_piece(int(move_list[0]), int(move_list[1]), int(move_list[2]), int(move_list[3]), turn)\r\n\r\n if valid_move:\r\n self.state = not SELECTED\r\n self.winner = self.board.check_end_state(self.turn)\r\n self.turn *= -1\r\n self.error_message = \"\"\r\n\r\n #disable controls\r\n if self.winner != NONE:\r\n self.turn = NONE\r\n\r\n else:\r\n self.error_message = \"INVALID MOVE BY AI\"\r\n \r\n\r\n\r\n\r\ndef main():\r\n \"\"\" Main method \"\"\"\r\n window = MyGame()\r\n window.setup()\r\n arcade.run()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()", "sub_path": "Game.py", "file_name": "Game.py", "file_ext": "py", "file_size_in_byte": 7737, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "arcade.Window", "line_number": 35, "usage_type": "attribute"}, {"api_name": "arcade.set_background_color", "line_number": 41, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 41, "usage_type": "attribute"}, {"api_name": "arcade.SpriteList", "line_number": 61, "usage_type": "call"}, {"api_name": "arcade.SpriteSolidColor", "line_number": 63, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 63, "usage_type": "attribute"}, {"api_name": "arcade.SpriteSolidColor", "line_number": 64, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 64, "usage_type": "attribute"}, {"api_name": "arcade.SpriteList", "line_number": 74, "usage_type": "attribute"}, {"api_name": "board.Board", "line_number": 75, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 83, "usage_type": "attribute"}, {"api_name": "arcade.color", "line_number": 85, "usage_type": "attribute"}, {"api_name": "tile.Tile", "line_number": 86, "usage_type": "call"}, {"api_name": "ai_handler.AI_Handler", "line_number": 94, "usage_type": "call"}, {"api_name": "arcade.start_render", "line_number": 101, "usage_type": "call"}, {"api_name": "arcade.draw_text", "line_number": 106, "usage_type": "call"}, {"api_name": "string.ascii_uppercase", "line_number": 106, "usage_type": "attribute"}, {"api_name": "arcade.color", "line_number": 106, "usage_type": "attribute"}, {"api_name": "arcade.draw_text", "line_number": 108, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 108, "usage_type": "attribute"}, {"api_name": "arcade.draw_text", "line_number": 112, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 112, "usage_type": "attribute"}, {"api_name": "arcade.draw_text", "line_number": 114, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 114, "usage_type": "attribute"}, {"api_name": "arcade.draw_text", "line_number": 116, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 116, "usage_type": "attribute"}, {"api_name": "arcade.draw_text", "line_number": 118, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 118, "usage_type": "attribute"}, {"api_name": "arcade.draw_text", "line_number": 122, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 122, "usage_type": "attribute"}, {"api_name": "arcade.draw_text", "line_number": 123, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 123, "usage_type": "attribute"}, {"api_name": "arcade.draw_text", "line_number": 126, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 126, "usage_type": "attribute"}, {"api_name": "arcade.draw_text", "line_number": 127, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 127, "usage_type": "attribute"}, {"api_name": "arcade.draw_text", "line_number": 129, "usage_type": "call"}, {"api_name": "arcade.color", "line_number": 129, "usage_type": "attribute"}, {"api_name": "arcade.get_sprites_at_point", "line_number": 141, "usage_type": "call"}, {"api_name": "arcade.get_sprites_at_point", "line_number": 150, "usage_type": "call"}, {"api_name": "arcade.get_sprites_at_point", "line_number": 171, "usage_type": "call"}, {"api_name": "arcade.get_sprites_at_point", "line_number": 178, "usage_type": "call"}, {"api_name": "arcade.run", "line_number": 218, "usage_type": "call"}]} +{"seq_id": "649688827", "text": "# Write a function that takes in Binary Tree and inverts it. In other words\n# the function should swap every left node in the tree for its corresponding right node.\n# Each binary tree node has a value stored in a property called \"value\" and two children\n# nodes stored in properties called \"left\" and \"right\" respectively. Children nodes\n# can either be Binary Tree nodes themselves or the None value.\n\n# Sample Input:\n# 1\n# / \\\n# 2 3\n# / \\ / \\\n# 4 5 6 7\n# / \\ \n# 8 9\n\n# Sample Output:\n\n# 1\n# / \\\n# 3 2\n# / \\ / \\\n# 7 6 5 4\n# / \\ \n# 9 8\nfrom collections import deque\nclass TreeNode:\n def __init__(self, x):\n self.val = x\n self.left = None\n self.right = None\n \n def invertBinaryTree(self, root):\n queue = deque([root])\n while len(queue):\n current = queue.popleft()\n if current is None:\n continue\n self.swapLeftAndRight(current)\n queue.append(current.left)\n queue.append(current.right)\n\n def invertBinaryTreeRecursive(self, root):\n if root is None:\n return\n self.swapLeftAndRight(root)\n self.invertBinaryTreeRecursive(root.left)\n self.invertBinaryTreeRecursive(root.right)\n\n def swapLeftAndRight(self, tree):\n tree.left, tree.right = tree.right, tree.left \n\ndef main():\n oneTree = TreeNode(1)\n twoTree = TreeNode(2)\n threeTree = TreeNode(3)\n fourTree = TreeNode(4)\n fiveTree = TreeNode(5)\n sixTree = TreeNode(6)\n sevenTree = TreeNode(7) \n eightTree = TreeNode(8)\n nineTree = TreeNode(9)\n oneTree.left = twoTree\n oneTree.right = threeTree\n twoTree.left = fourTree\n twoTree.right = fiveTree\n threeTree.left = sixTree\n threeTree.right = sevenTree \n fourTree.left = eightTree\n fourTree.right = nineTree \n\n print(oneTree.invertBinaryTree(oneTree)) \nif __name__ == \"__main__\":\n main()\n", "sub_path": "Questions/invertBinaryTree.py", "file_name": "invertBinaryTree.py", "file_ext": "py", "file_size_in_byte": 2069, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "collections.deque", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "586618938", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# File : LeetCode1830.py\n# Author: WangYu\n# Date : 2021/4/19\n\nimport collections\nimport math\n\nclass Solution:\n def makeStringSorted(self, s: str) -> int:\n # 求字符串的总共组合数量，用到了那个先验知识\n cnt = collections.Counter(s)\n cur = math.factorial(len(s))\n for v in cnt.values():\n cur //= math.factorial(v)\n\n res = 0\n for i, v in enumerate(s):\n for ke, va in cnt.items():\n # 当后续某个字符小于当前字符，累加当前的可能性\n # 以上面的描述为例，当b后面出现个a，可以假定当前位置变成a，这种情况下\n # 还剩下 (x - 1)个a，y个b，z个c, 组合总数为 ((x - 1) + y + z)! / ((x - 1)! * y! * z!)\n # 等同为 (x + y + z)! / (x! * y! * z!) * x / (x + y + z)\n # 也就是下面的 cur * va // (len(s) - i)\n if ke < v:\n res += cur * va // (len(s) - i)\n\n # 当字符往后移动时，更新当前的可能的组合数，同时更新Counter\n cur = cur * cnt[v] // (len(s) - i)\n cnt[v] -= 1\n\n return res % (10 ** 9 + 7)\n\ns = \"aabaa\"\nss = Solution()\nprint(ss.makeStringSorted(s))", "sub_path": "LeetCode1830.py", "file_name": "LeetCode1830.py", "file_ext": "py", "file_size_in_byte": 1310, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "collections.Counter", "line_number": 13, "usage_type": "call"}, {"api_name": "math.factorial", "line_number": 14, "usage_type": "call"}, {"api_name": "math.factorial", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "608227629", "text": "from Bio.Blast import NCBIWWW\nfrom Bio import SeqIO\nimport os \n\ndef sequence_blaster(fasta_path, results_path): \n \n record = SeqIO.read(fasta_path, format=\"fasta\")\n result_path = NCBIWWW.qblast(\"blastn\", \"nt\", record.format(\"fasta\"))\n\n save_file = open(results_path, \"w\")\n save_file.write(result_path.read())\n save_file.close()\n \n assert os.stat(results_path).st_size != 0", "sub_path": "wardpythonpackage/sequence_blaster.py", "file_name": "sequence_blaster.py", "file_ext": "py", "file_size_in_byte": 396, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "Bio.SeqIO.read", "line_number": 7, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 7, "usage_type": "name"}, {"api_name": "Bio.Blast.NCBIWWW.qblast", "line_number": 8, "usage_type": "call"}, {"api_name": "Bio.Blast.NCBIWWW", "line_number": 8, "usage_type": "name"}, {"api_name": "os.stat", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "317933674", "text": "# Default Imports\nimport pandas as pd\nimport numpy as np\nimport matplotlib\nmatplotlib.use('agg')\nimport matplotlib.pyplot as plt\nfrom greyatomlib.descriptive_stats.q01_calculate_statistics.build import calculate_statistics\n\ndata = pd.read_csv('data/house_prices_multivariate.csv')\nsale_price = data.loc[:, \"SalePrice\"]\n\ndef plot():\n mean = sale_price.mean()\n median = sale_price.median()\n mode = sale_price.mode()\n plt.hist(sale_price)\n plt.axvline(mean, color='k', linestyle='--')\n plt.axvline(median, color='r', linestyle='--')\n plt.axvline(mode[0], color='b', linestyle='--')\n plt.show()\nplot()\n", "sub_path": "q02_plot/build.py", "file_name": "build.py", "file_ext": "py", "file_size_in_byte": 622, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "matplotlib.use", "line_number": 5, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}]} +{"seq_id": "520915306", "text": "# -*- coding: UTF-8 -*-\n# Copyright 2009-2015 Luc Saffre\n# License: BSD (see file COPYING for details)\n\"\"\"\n.. autosummary::\n\n\"\"\"\n\nimport logging\nlogger = logging.getLogger(__name__)\n\nimport os\n\nfrom django.conf import settings\n\nfrom lino.utils import curry\nfrom lino.core import actions\n\n\nclass BoundAction(object):\n\n \"\"\"An Action which is bound to an Actor. If an Actor has subclasses,\n each subclass \"inherits\" its actions.\n\n \"\"\"\n\n def __init__(self, actor, action):\n\n if not isinstance(action, actions.Action):\n raise Exception(\"%s : %r is not an Action\" % (actor, action))\n self.action = action\n self.actor = actor\n\n required = dict()\n if action.readonly:\n required.update(actor.required)\n #~ elif isinstance(action,InsertRow):\n #~ required.update(actor.create_required)\n elif isinstance(action, actions.DeleteSelected):\n required.update(actor.delete_required)\n else:\n required.update(actor.update_required)\n required.update(action.required)\n\n if settings.SITE.user_model is not None:\n required.setdefault('auth', True)\n\n #~ print 20120628, str(a), required\n #~ def wrap(a,required,fn):\n #~ return fn\n\n debug_permissions = actor.debug_permissions and \\\n action.debug_permissions\n\n if debug_permissions:\n if settings.DEBUG:\n logger.info(\"debug_permissions active for %r (required=%s)\",\n self, required)\n else:\n raise Exception(\n \"settings.DEBUG is False, but `debug_permissions` \"\n \"for %r (required=%s) is active (settings=%s).\" % (\n self, required, os.environ['DJANGO_SETTINGS_MODULE']))\n\n from lino.modlib.users.utils import (\n make_permission_handler, make_view_permission_handler)\n self.allow_view = curry(make_view_permission_handler(\n self, action.readonly, debug_permissions, **required), action)\n self._allow = curry(make_permission_handler(\n action, actor, action.readonly,\n debug_permissions, **required), action)\n #~ if debug_permissions:\n #~ logger.info(\"20130424 _allow is %s\",self._allow)\n #~ actor.actions.define(a.action_name,ba)\n\n def get_window_layout(self):\n return self.action.get_window_layout(self.actor)\n\n def get_window_size(self):\n return self.action.get_window_size(self.actor)\n\n def full_name(self):\n return self.action.full_name(self.actor)\n\n def request(self, *args, **kw):\n kw.update(action=self)\n return self.actor.request(*args, **kw)\n\n def request_from(self, ar, *args, **kw):\n \"\"\"Create a request of this action from parent request `ar`.\n\n \"\"\"\n kw.update(parent=ar)\n return self.request(*args, **kw)\n\n def get_button_label(self, *args):\n return self.action.get_button_label(self.actor, *args)\n\n #~ def get_panel_btn_handler(self,*args):\n #~ return self.action.get_panel_btn_handler(self.actor,*args)\n\n def setup_action_request(self, *args):\n return self.action.setup_action_request(self.actor, *args)\n\n def get_row_permission(self, ar, obj, state):\n #~ if self.actor is None: return False\n return self.actor.get_row_permission(obj, ar, state, self)\n\n def get_bound_action_permission(self, ar, obj, state):\n if not self.action.get_action_permission(ar, obj, state):\n return False\n return self._allow(ar.get_user(), obj, state)\n\n def get_view_permission(self, profile):\n \"\"\"\n Return True if this bound action is visible for users of this\n profile.\n \"\"\"\n if not self.actor.get_view_permission(profile):\n return False\n if not self.action.get_view_permission(profile):\n return False\n return self.allow_view(profile)\n\n def __repr__(self):\n return \"<%s(%s, %r)>\" % (\n self.__class__.__name__, self.actor, self.action)\n\n\n", "sub_path": "lino/core/boundaction.py", "file_name": "boundaction.py", "file_ext": "py", "file_size_in_byte": 4128, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "lino.core.actions.Action", "line_number": 29, "usage_type": "attribute"}, {"api_name": "lino.core.actions", "line_number": 29, "usage_type": "name"}, {"api_name": "lino.core.actions.DeleteSelected", "line_number": 39, "usage_type": "attribute"}, {"api_name": "lino.core.actions", "line_number": 39, "usage_type": "name"}, {"api_name": "django.conf.settings.SITE", "line_number": 45, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 45, "usage_type": "name"}, {"api_name": "django.conf.settings.DEBUG", "line_number": 56, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 56, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 63, "usage_type": "attribute"}, {"api_name": "lino.utils.curry", "line_number": 67, "usage_type": "call"}, {"api_name": "lino.modlib.users.utils.make_view_permission_handler", "line_number": 67, "usage_type": "call"}, {"api_name": "lino.utils.curry", "line_number": 69, "usage_type": "call"}, {"api_name": "lino.modlib.users.utils.make_permission_handler", "line_number": 69, "usage_type": "call"}]} +{"seq_id": "628194855", "text": "# -*- coding:utf-8 -*-\r\n# Anaconda 4.3.0 環境\r\n\r\nimport numpy\r\nimport pandas\r\nimport matplotlib.pyplot as plt\r\n\r\n# scikit-learn ライブラリ関連\r\nfrom sklearn import datasets\r\n\r\nfrom sklearn.preprocessing import LabelEncoder \r\nfrom sklearn.preprocessing import StandardScaler # scikit-learn の preprocessing モジュールの StandardScaler クラス\r\nfrom sklearn.decomposition import PCA\r\n\r\nfrom sklearn.linear_model import LogisticRegression\r\nfrom sklearn.tree import DecisionTreeClassifier\r\nfrom sklearn.neighbors import KNeighborsClassifier\r\nfrom sklearn.svm import SVC # \r\nfrom sklearn.ensemble import BaggingClassifier # バギング\r\nfrom sklearn.ensemble import AdaBoostClassifier # AdaBoost\r\nfrom sklearn.ensemble import RandomForestClassifier # \r\n\r\nfrom sklearn.metrics import accuracy_score # 正解率の算出\r\nfrom sklearn.metrics import roc_curve # ROC曲線\r\nfrom sklearn.metrics import auc # AUC\r\nfrom sklearn.model_selection import cross_val_score # k-flod CV での各種スコア\r\nfrom sklearn.model_selection import learning_curve # 学習曲線用\r\nfrom sklearn.model_selection import validation_curve # 検証曲線用\r\nfrom sklearn.model_selection import GridSearchCV # グリッドサーチ\r\n\r\nfrom sklearn.pipeline import Pipeline\r\n\r\n# 自作クラス\r\nimport EnsembleModelClassifier\r\nimport DataPreProcess\r\nimport Plot2D\r\n\r\ndef main():\r\n \"\"\"\r\n アンサンブル学習.\r\n 渦巻きデータをアンサンブル法で識別\r\n \"\"\"\r\n print(\"Enter main()\")\r\n\r\n # データの読み込み\r\n # 渦巻きデータ\r\n prePro = DataPreProcess.DataPreProcess()\r\n prePro.setDataFrameFromCsvFile( \"naruto.csv\" )\r\n prePro.setColumns( [\"x\",\"y\",\"class labels\"] )\r\n\r\n prePro.print( \"渦巻きデータ \")\r\n\r\n X_features = prePro.df_[ [\"x\", \"y\" ] ].values\r\n y_labels = prePro.df_[ [\"class labels\"] ].values\r\n\r\n #print( X_features )\r\n\r\n ratio_test = 0.3\r\n\r\n #===========================================\r\n # 前処理 [PreProcessing]\r\n #===========================================\r\n # 欠損データへの対応\r\n #prePro.meanImputationNaN()\r\n\r\n # ラベルデータをエンコード\r\n encoder = LabelEncoder()\r\n y_labels = encoder.fit_transform( y_labels )\r\n\r\n # データをトレードオフデータとテストデータに分割\r\n X_train, X_test, y_train, y_test \\\r\n = DataPreProcess.DataPreProcess.dataTrainTestSplit( X_input = X_features, y_input = y_labels, ratio_test = ratio_test, input_random_state = 1 )\r\n \r\n test_idx = []\r\n #test_idx = range( 26,50 )\r\n\r\n #\r\n stdScaler = StandardScaler()\r\n \r\n # X_train の平均値と標準偏差を計算\r\n stdScaler.fit( X_train )\r\n\r\n # 求めた平均値と標準偏差を用いて標準化\r\n X_train_std = stdScaler.transform( X_train )\r\n X_test_std = stdScaler.transform( X_test )\r\n\r\n # 分割したデータを行方向に結合（後で plot データ等で使用する）\r\n X_combined_std = numpy.vstack( (X_train_std, X_test_std) ) # list:(X_train_std, X_test_std) で指定\r\n y_combined = numpy.hstack( (y_train, y_test) )\r\n\r\n\r\n #print( \"X_train :\\n\", X_train )\r\n #print( \"X_test :\\n\", X_test )\r\n #print( \"y_train :\\n\", y_train )\r\n #print( \"y_test :\\n\", y_test )\r\n\r\n #-------------------------------------------\r\n # モデルの生成\r\n #-------------------------------------------\r\n # 決定木の生成\r\n decition_tree = DecisionTreeClassifier(\r\n criterion = 'entropy', # 不純度として, 交差エントロピー\r\n max_depth = None, # None : If None, then nodes are expanded until all leaves are pure \r\n # or until all leaves contain less than min_samples_split samples.(default=None)\r\n random_state = 0\r\n )\r\n # k-NN\r\n kNN = KNeighborsClassifier(\r\n n_neighbors = 3,\r\n p = 2,\r\n metric = 'minkowski'\r\n )\r\n\r\n # SVM\r\n svm = SVC( \r\n kernel = 'rbf', # rbf : RFBカーネルでのカーネルトリックを指定\r\n gamma = 10.0, # RFBカーネル関数のγ値\r\n C = 0.1, # C-SVM の C 値\r\n random_state = 1, #\r\n probability = True # 学習後の predict_proba method による予想確率を有効にする\r\n )\r\n\r\n # LogisticRegression\r\n logReg = LogisticRegression(\r\n penalty = 'l2', \r\n C = 0.001,\r\n random_state = 1\r\n )\r\n\r\n # バギングの生成\r\n bagging = BaggingClassifier(\r\n base_estimator = decition_tree, # 弱識別器をして決定木を設定\r\n n_estimators = 501, # バギングを構成する弱識別器の数\r\n max_samples = 1.0, # The number of samples to draw from X to train each base estimator.\r\n # If float, then draw max_samples * X.shape[0] samples.\r\n # base_estimator に設定した弱識別器の内, 使用するサンプルの割合\r\n # \r\n max_features = 1.0, # The number of features to draw from X to train each base estimator.\r\n # If float, then draw max_features * X.shape[1] features.\r\n bootstrap = True, # ブートストラップサンプリングを行う \r\n bootstrap_features = False, #\r\n n_jobs = -1, \r\n random_state = 1\r\n )\r\n \r\n # AdaBoost\r\n ada = AdaBoostClassifier(\r\n base_estimator = decition_tree, # 弱識別器をして決定木を設定\r\n n_estimators = 501, # バギングを構成する弱識別器の数 \r\n learning_rate = 0.1, # \r\n random_state = 1 #\r\n )\r\n\r\n # Random Forest\r\n forest = RandomForestClassifier(\r\n criterion = \"gini\", # 不純度関数 [purity]\r\n bootstrap = True, # 決定木の構築に、ブートストラップサンプルを使用するか否か（default:True）\r\n n_estimators = 501, # 弱識別器（決定木）の数\r\n n_jobs = -1, # The number of jobs to run in parallel for both fit and predict ( -1 : 全てのCPUコアで並列計算)\r\n random_state = 1, #\r\n oob_score = True # Whether to use out-of-bag samples to estimate the generalization accuracy.(default=False)\r\n )\r\n \r\n #-------------------------------------------\r\n # 各 Pipeline の設定\r\n #-------------------------------------------\r\n # パイプラインに各変換器、推定器を設定\r\n # タプル (任意の識別文字, 変換器 or 推定器のクラス) で指定\r\n\r\n #-----------------------------------------------------------\r\n # アンサンブル識別器 EnsembleLearningClassifier の設定\r\n #-----------------------------------------------------------\r\n ensemble_clf1 = EnsembleModelClassifier.EnsembleModelClassifier( \r\n classifiers = [ bagging, ada, forest, decition_tree, logReg, kNN, svm ],\r\n class_labels = [ \r\n \"Bagging ( base_estimator = decition_tree, n_estimators = 501 )\" ,\r\n \"AdaBoost (base_estimator = decition_tree, n_estimators = 501 )\"\r\n \"Random Forest ( criterion = 'gini', n_estimators = 501)\"\r\n \"Decision Tree ( criterion = 'entropy' )\", \r\n \"Logistic Regression( penalty = 'l2', C = 0.001 )\",\r\n \"k-NN ( n_neighbors = 3, metric='minkowski' )\",\r\n \"SVM ( kernel = 'rbf', C = 10.0, gamma = 0.1 )\"\r\n ]\r\n )\r\n\r\n #-------------------------------------------\r\n # 全識別器のリストの設定\r\n #-------------------------------------------\r\n # 各種スコア計算時に使用する識別器のリスト ( for 文の in で使用を想定) \r\n all_clf = [ bagging, ada, forest, decition_tree, logReg, kNN, svm, ensemble_clf1 ]\r\n\r\n # 各種スコア計算時に使用する識別器のラベルのリスト ( for 文の in で使用を想定)\r\n all_clf_labels = [ \r\n \"Decision Tree \\n ( criterion = 'entropy' )\",\r\n \"Bagging \\n ( base_estimator = decition_tree, n_estimators = 501 )\",\r\n \"AdaBoost \\n (base_estimator = decition_tree, n_estimators = 501 )\",\r\n \"RamdomForest \\n (base_estimator = decition_tree, n_estimators = 501 )\",\r\n \"Logistic Regression \\n ( penalty = 'l2', C = 0.001 )\",\r\n \"k-NN \\n ( n_neighbors = 3, metric='minkowski' )\",\r\n \"SVM \\n ( kernel = 'rbf', C = 0.1, gamma = 10.0 )\",\r\n \"Ensemble Model \\n ( Bagging, AdaBoost, RandamForest, Decision Tree, LogisticRegression, k-NN, SVM )\"\r\n ]\r\n\r\n print( \"all_clf :\", all_clf )\r\n print( \"len(all_clf) :\", len(all_clf) )\r\n print( \"all_clf_labels :\", all_clf_labels )\r\n print( \"len(all_clf_labels) :\", len(all_clf_labels) )\r\n\r\n #============================================\r\n # Learning Process\r\n #===========================================\r\n # 設定した推定器をトレーニングデータで fitting\r\n decition_tree = decition_tree.fit( X_train_std, y_train )\r\n logReg = logReg.fit( X_train_std, y_train )\r\n kNN = kNN.fit( X_train_std, y_train )\r\n svm = svm.fit( X_train_std, y_train )\r\n \r\n bagging = bagging.fit( X_train_std, y_train )\r\n ada = ada.fit( X_train_std, y_train ) \r\n forest = forest.fit( X_train_std, y_train )\r\n \r\n ensemble_clf1.fit( X_train_std, y_train )\r\n\r\n #print( \"decition_tree : \", decition_tree.tree_.max_depth )\r\n #print( \"bagging : \", bagging )\r\n\r\n #===========================================\r\n # 汎化性能の確認\r\n #===========================================\r\n\r\n #-------------------------------------------\r\n # 正解率, 誤識率\r\n #-------------------------------------------\r\n # k-fold CV を行い, cross_val_score( scoring = 'accuracy' ) で 正解率を算出\r\n print( \"[Accuracy]\")\r\n # train data\r\n for clf, label in zip( all_clf, all_clf_labels ):\r\n scores = cross_val_score(\r\n estimator = clf,\r\n X = X_train_std,\r\n y = y_train,\r\n cv = 10,\r\n n_jobs = -1,\r\n scoring = 'accuracy' # 正解率\r\n )\r\n print( \"Accuracy : %0.2f (+/- %0.2f) [%s]\" % ( scores.mean(), scores.std(), label) ) \r\n \r\n # test data\r\n for clf, label in zip( all_clf, all_clf_labels ):\r\n scores = cross_val_score(\r\n estimator = clf,\r\n X = X_test_std,\r\n y = y_test,\r\n cv = 10,\r\n n_jobs = -1,\r\n scoring = 'accuracy' # 正解率\r\n )\r\n print( \"Accuracy : %0.2f (+/- %0.2f) [%s]\" % ( scores.mean(), scores.std(), label) ) \r\n\r\n \r\n #-------------------------------------------\r\n # AUC 値\r\n #-------------------------------------------\r\n # k-fold CV を行い, cross_val_score( scoring = 'roc_auc' ) で AUC を算出\r\n print( \"[AUC]\")\r\n for clf, label in zip( all_clf, all_clf_labels ):\r\n scores = cross_val_score(\r\n estimator = clf,\r\n X = X_train_std,\r\n y = y_train,\r\n cv = 10,\r\n n_jobs = -1,\r\n scoring = 'roc_auc' # AUC\r\n )\r\n print( \"AUC : %0.2f (+/- %0.2f) [%s]\" % ( scores.mean(), scores.std(), label) )\r\n\r\n for clf, label in zip( all_clf, all_clf_labels ):\r\n scores = cross_val_score(\r\n estimator = clf,\r\n X = X_test_std,\r\n y = y_test,\r\n cv = 10,\r\n n_jobs = -1,\r\n\r\n scoring = 'roc_auc' # AUC\r\n )\r\n print( \"AUC : %0.2f (+/- %0.2f) [%s]\" % ( scores.mean(), scores.std(), label) )\r\n\r\n\r\n #-------------------------------------------\r\n # 識別境界\r\n #-------------------------------------------\r\n plt.clf()\r\n\r\n for (idx, clf, label) in zip( range( 1,len(all_clf)+2 ), all_clf, all_clf_labels ):\r\n print( \"識別境界 for ループ idx : \", idx )\r\n print( \"識別境界 for ループ clf : \", clf )\r\n\r\n # idx 番目の plot\r\n plt.subplot( 2, 4, idx )\r\n\r\n Plot2D.Plot2D.drawDiscriminantRegions( X_combined_std, y_combined, classifier = all_clf[idx-1] )\r\n plt.title( label )\r\n plt.legend(loc = \"best\")\r\n plt.tight_layout()\r\n\r\n plt.savefig(\"./EnsembleLearning_scikit-learn_naruto_x-1.png\", dpi = 300, bbox_inches = 'tight' )\r\n plt.show() \r\n\r\n #-------------------------------------------\r\n # 学習曲線\r\n #-------------------------------------------\r\n plt.clf()\r\n\r\n for (idx, clf, label) in zip( range( 1,len(all_clf)+2 ), all_clf, all_clf_labels ):\r\n print( \"学習曲線 for ループ idx : \", idx )\r\n print( \"学習曲線 for ループ clf : \", clf )\r\n\r\n train_sizes, train_scores, test_scores \\\r\n = learning_curve(\r\n estimator = clf, # 推定器 \r\n X = X_train_std, # トレーニングデータでの正解率を計算するため, トレーニングデータを設定\r\n y = y_train, # \r\n train_sizes = numpy.linspace(0.1, 1.0, 10), # トレードオフサンプルの絶対数 or 相対数\r\n # トレーニングデータサイズに応じた, 等間隔の10 個の相対的な値を設定\r\n cv = 10 # 交差検証の回数（分割数）\r\n )\r\n\r\n # 平均値、分散値を算出\r\n train_means = numpy.mean( train_scores, axis = 1 ) # axis = 1 : 行方向\r\n train_stds = numpy.std( train_scores, axis = 1 )\r\n test_means = numpy.mean( test_scores, axis = 1 )\r\n test_stds = numpy.std( test_scores, axis = 1 )\r\n\r\n print( \"学習曲線 for ループ : \\n\")\r\n print( \"train_sizes\", train_sizes )\r\n print( \"train_means\", train_means )\r\n print( \"train_stds\", train_stds )\r\n print( \"test_means\", test_means )\r\n print( \"test_stds\", test_stds )\r\n\r\n # idx 番目の plot\r\n plt.subplot( 2, 4, idx )\r\n Plot2D.Plot2D.drawLearningCurve(\r\n train_sizes = train_sizes,\r\n train_means = train_means,\r\n train_stds = train_stds,\r\n test_means = test_means,\r\n test_stds = test_stds,\r\n train_label = \"training accuracy\",\r\n test_label = \"k-fold cross validation accuracy (cv=10)\"\r\n )\r\n plt.title( \"Learning Curve \\n\" + label )\r\n plt.xlabel( \"Number of training samples\" )\r\n plt.ylabel( \"Accuracy\" )\r\n plt.legend( loc = \"best\" )\r\n plt.ylim( [0.5, 1.01] )\r\n plt.tight_layout()\r\n\r\n plt.savefig(\"./EnsembleLearning_scikit-learn_naruto_x-2.png\", dpi = 300, bbox_inches = 'tight' )\r\n plt.show() \r\n \r\n #-------------------------------------------\r\n # ROC 曲線\r\n #-------------------------------------------\r\n plt.clf()\r\n Plot2D.Plot2D.drawROCCurveFromClassifiers( \r\n classifilers = all_clf, \r\n class_labels = all_clf_labels, \r\n X_train = X_train_std, y_train = y_train,\r\n X_test = X_test_std, y_test = y_test\r\n )\r\n\r\n plt.savefig(\"./EnsembleLearning_scikit-learn_naruto_x-3.png\", dpi = 300, bbox_inches = 'tight' )\r\n plt.show() \r\n \r\n \r\n print(\"Finish main()\")\r\n return\r\n \r\n\r\nif __name__ == '__main__':\r\n main()\r\n", "sub_path": "EnsembleLearning_scikit-learn/main5.py", "file_name": "main5.py", "file_ext": "py", "file_size_in_byte": 16826, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "DataPreProcess.DataPreProcess", "line_number": 47, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 67, "usage_type": "call"}, {"api_name": "DataPreProcess.DataPreProcess.dataTrainTestSplit", "line_number": 72, "usage_type": "call"}, {"api_name": "DataPreProcess.DataPreProcess", "line_number": 72, "usage_type": "attribute"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 89, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 101, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 108, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 115, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 124, "usage_type": "call"}, {"api_name": "sklearn.ensemble.BaggingClassifier", "line_number": 131, "usage_type": "call"}, {"api_name": "sklearn.ensemble.AdaBoostClassifier", "line_number": 147, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 155, "usage_type": "call"}, {"api_name": "EnsembleModelClassifier.EnsembleModelClassifier", "line_number": 173, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 238, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 250, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 267, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 278, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 293, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 293, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 300, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 300, "usage_type": "name"}, {"api_name": "Plot2D.Plot2D.drawDiscriminantRegions", "line_number": 302, "usage_type": "call"}, {"api_name": "Plot2D.Plot2D", "line_number": 302, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.title", "line_number": 303, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 303, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 304, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 304, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 305, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 305, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 307, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 307, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 308, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 308, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 313, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 313, "usage_type": "name"}, {"api_name": "sklearn.model_selection.learning_curve", "line_number": 320, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 324, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 330, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 331, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 332, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 333, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 343, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 343, "usage_type": "name"}, {"api_name": "Plot2D.Plot2D.drawLearningCurve", "line_number": 344, "usage_type": "call"}, {"api_name": "Plot2D.Plot2D", "line_number": 344, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.title", "line_number": 353, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 353, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 354, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 354, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 355, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 355, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 356, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 356, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 357, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 357, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 358, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 358, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 360, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 360, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 361, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 361, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 366, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 366, "usage_type": "name"}, {"api_name": "Plot2D.Plot2D.drawROCCurveFromClassifiers", "line_number": 367, "usage_type": "call"}, {"api_name": "Plot2D.Plot2D", "line_number": 367, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 374, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 374, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 375, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 375, "usage_type": "name"}]} +{"seq_id": "492392826", "text": "import xlrd\n\n# excel 文件路径\npath = 'E:\\维优\\统计\\超级会员清单20190301..xlsx'\n# sheet名字\nsheetName = '数据'\n# 第几列\ncol = 0\n\n# 设置前缀\nsql_pre = 'select t.account, t.mobile from paydb.T_CMP_TYPE_PAYMENTACCOUNT t where t.mobile in ('\n\n# 设置后缀\nsql_after = ') order by t.mobile;'\n\nworkbook = xlrd.open_workbook(path)\nsheet = workbook.sheet_by_name(sheetName)\n\n\n# 筛选出手机号或者其它号码\ndata = []\nfor i in range(1, sheet.nrows):\n rowInfo = sheet.row_values(i)\n number = int(rowInfo[col])\n data.append(\"'\" + str(number) + \"'\")\n #print(number)\n\nprint(data)\n\n\n# 分割组装\nf = open('sql_output.txt', 'a')\n\n\nstep = 1000 # 需要多少条\nmax_count=len(data) #使用len()获取列表的长度\nn=0\nwhile n/\")\ndef getTimeFromClock(idReloj):\n\ttry:\n\t\treturn jsonify({\n\t\t\t'ok':True,\n\t\t\t'description':{\n\t\t\t\t\"id_reloj\":idReloj,\n\t\t\t\t\"tiempo\":{\n\t\t\t\t\t'hora': relojes[idReloj].hora,\n\t\t\t\t\t'mins': relojes[idReloj].mins,\n\t\t\t\t\t'segs': relojes[idReloj].segs\n\t\t\t\t}\n\t\t\t}\n\t\t})\n\texcept Exception as ex:\n\t\treturn jsonify({'ok':False, 'description': str(ex)})\n\n#Esta ruta/función será la que edite los relojes\n@app.route(\"/relojes/edit///\", methods=['GET', 'POST'])\ndef editaReloj(idReloj,hora, mins):#, segs):\n\ttry:\n\t\trelojes[idReloj].hora = hora\n\t\trelojes[idReloj].mins = mins\n\t\t#Regresa el json de edición correcta\n\t\treturn jsonify({'ok':True, 'description': {'reloj_afectado':idReloj, 'nuevo_valor':str(relojes[idReloj])} } )\n\texcept Exception as ex:\n\t\treturn jsonify({'ok':False, 'description': str(ex)})\n\n@app.route(\"/relojes/pausa//\")\ndef pausaReloj(idReloj, opcion):\n\tif opcion == \"pausa\":\n\t\trelojes[idReloj].paused = True\n\telse:\n\t\trelojes[idReloj].paused = False\n\treturn jsonify({'ok':True, 'description':{'reloj':idReloj, 'pausado':relojes[idReloj].paused}})\n\n@app.route(\"/relojes//\")\ndef cambiaRitmo(idReloj, opcion):\n\tresponse = {'ok':False, 'description':\"\"}\n\ttry:\n\t\tif opcion==\"A\":\n\t\t if(relojes[idReloj].ritmo > 0.1):#es un tope... al llegar a 0 fallaba\n \t\t\trelojes[idReloj].ritmo -= 0.1\n\t\tif opcion == \"D\":\n\t\t\trelojes[idReloj].ritmo += 1\n\t\tresponse['ok'] = True\n\t\tresponse['description'] = \"ritmo modificado: \"+str(relojes[idReloj].ritmo)+\" cambios/seg\"\n\texcept Exception as ex:\n\t\tprint(\"Excepción en cambiaRitmo:\", ex)\n\t\tresponse['description'] = str(ex)\n\treturn jsonify( response )\n\nif __name__ == \"__main__\":\n\tapp.run(host= '25.21.92.128', port=11000, debug=True)\n", "sub_path": "server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 2615, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "flask.Flask", "line_number": 12, "usage_type": "call"}, {"api_name": "classes.Reloj.Reloj", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 32, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 85, "usage_type": "call"}]} +{"seq_id": "14527723", "text": "from math import factorial\nimport itertools\n\ndef createArray(arrayLen, onesCount):\n ones = [1] * onesCount\n zeros = [0] * (arrayLen-onesCount)\n array = zeros + ones\n return array\n \ndef moveRight(array):\n newArray = array[-1:] + array[: -1]\n return newArray\n\ndef is_equal(arr1, arr2):\n buf = arr1\n for i in range(len(arr1)):\n buf = moveRight(buf)\n if (arr2 == buf):\n return True\n return False\n\ndef allCombArrays(origArray):\n posibleArrays = list()\n added_bool = False #есть ли уже в списке данная строка\n allCombs = itertools.permutations(origArray) #\n for array in allCombs:\n added_bool = False\n for arr in posibleArrays:\n if is_equal(list(arr), list(array)):\n added_bool = True\n break\n if not added_bool:\n posibleArrays.append(array)\n return posibleArrays\n\ndef checkSymmetry(array):\n symArrays = list() #записывает индексы симметрии с типом симметрии\n length = len(array)\n midIndex = length // 2\n arrayBuf = array\n if length % 2 == 0:\n for shift in range(midIndex): #проверка палиндромности половин\n if arrayBuf[0:midIndex - 1] == arrayBuf[length - 2:midIndex - 1:-1]:\n symArrays.append([shift, \"points\"])\n if arrayBuf[0:midIndex] == arrayBuf[length - 1:midIndex - 1:-1]:\n symArrays.append([shift, \"lines\"])\n arrayBuf = moveRight(arrayBuf)\n else:\n for shift in range(length): #проверка полной палиндромности(в случае нахождения в середине точки симметрий)\n if arrayBuf == arrayBuf[::-1]:\n symArrays.append([length - shift - 1, \"mixed\"])\n arrayBuf = moveRight(arrayBuf)\n return symArrays\n\ndef printSymArray(array):\n symArrays = checkSymmetry(array)\n for symArray in symArrays:\n string = ''\n pointOfSym = symArray[0]\n symmetryType = symArray[1]\n secondPoint = (pointOfSym + len(array) // 2) % len(array) #в четных случаях\n if symmetryType == \"lines\": #между вершинами палочка\n for i in range(len(array)):\n point = array[i]\n if i is pointOfSym or i is secondPoint:\n string += \"|\"\n string += str(point)\n if symmetryType == \"points\": #вершину в скобочки\n secondPoint = (pointOfSym + len(array) // 2) % len(array)\n for i in range(len(array)):\n point = array[i]\n if i is pointOfSym or i is secondPoint:\n string += '(' + str(point) + ')'\n else:\n string += str(point)\n if symmetryType == \"mixed\": #вершину в скобочки и в противоположной стороне палочка\n secondPoint = (pointOfSym + 1 + len(array) // 2) % len(array) #в этом случае точка высчитывается по другому\n for i in range(len(array)):\n point = array[i]\n if i is secondPoint:\n string += '(' + str(point) + ')'\n else:\n string += str(point)\n if i is pointOfSym:\n string += \"|\"\n print(string)\n\ndef main(length, onesCount):\n combinations = allCombArrays(createArray(length, onesCount))\n for array in combinations:\n printSymArray(array)\n\nlength = int(input(\"Введите длину строки\"))\nonesCount = int(input(\"Введите количество единиц\"))\nmain(length, onesCount)\n", "sub_path": "Darizhapov Yampil/Lab2.py", "file_name": "Lab2.py", "file_ext": "py", "file_size_in_byte": 3823, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "itertools.permutations", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "152699779", "text": "import json\n\nimport pandas as pd\nimport psycopg2\nimport sys\nimport xgboost as xgb\n\n\ndef main(filepath: str):\n with open(filepath) as f:\n config = json.load(f)\n conn = psycopg2.connect(config[\"db\"])\n query = \"\"\"\n SELECT max(point) as max, problem_id FROM submissions AS s\n WHERE s.problem_id IN\n (\n SELECT problems.id FROM problems\n JOIN contests ON problems.contest_id=contests.id\n WHERE contests.start_epoch_second>=1468670400\n AND rate_change!='-'\n )\n GROUP BY s.problem_id\n ORDER BY max\n \"\"\"\n with conn.cursor() as cursor:\n cursor.execute(query)\n for point, problem_id in cursor.fetchall():\n query = \"\"\"\n INSERT INTO points (problem_id, point)\n VALUES (%s, %s)\n ON CONFLICT (problem_id) DO UPDATE\n SET point = %s;\n \"\"\"\n cursor.execute(query, (problem_id, point, point))\n conn.commit()\n\n query = \"\"\"\n SELECT \n m.point,\n s.problem_id,\n s.user_id,\n s.result\n FROM submissions AS s\n LEFT JOIN points AS m ON s.problem_id=m.problem_id\n LEFT JOIN problems AS p ON p.id=m.problem_id\n LEFT JOIN contests AS c ON c.id=p.contest_id\n LEFT JOIN (\n SELECT COUNT(DISTINCT(problem_id)) AS count, user_id FROM submissions WHERE result='AC' GROUP BY user_id\n ) AS u ON u.user_id=s.user_id\n WHERE u.count>300\n \"\"\"\n\n with conn.cursor() as cursor:\n cursor.execute(query)\n submissions = cursor.fetchall()\n user_set = set([s[2] for s in submissions])\n problem_set = set([s[1] for s in submissions])\n ac_set = set()\n wa_set = set()\n problems = {}\n for submission in submissions:\n point = submission[0]\n result = submission[3]\n problem_id = submission[1]\n user_id = submission[2]\n if result == \"AC\":\n ac_set.add((user_id, problem_id))\n else:\n wa_set.add((user_id, problem_id))\n problems[problem_id] = point\n\n df = pd.DataFrame(columns=user_set, index=problem_set)\n for user_id, problem_id in wa_set:\n df.at[problem_id, user_id] = -1\n for user_id, problem_id in ac_set:\n df.at[problem_id, user_id] = 1\n df[\"Point\"] = pd.Series(problems)\n train = df[df.Point.notnull()]\n test = df[df.Point.isnull()]\n x_train = train.iloc[:, :-1].values\n x_test = test.iloc[:, :-1].values\n y_train = train.loc[:, \"Point\"].values\n model = xgb.XGBRegressor()\n model.fit(x_train, y_train)\n y_test_predict = model.predict(x_test)\n test[\"Predict\"] = y_test_predict\n\n with conn.cursor() as cursor:\n for problem_id, point in test[\"Predict\"].to_dict().items():\n query = \"\"\"\n INSERT INTO points (problem_id, predict)\n VALUES (%s, %s)\n ON CONFLICT (problem_id) DO UPDATE\n SET predict = %s;\n \"\"\"\n cursor.execute(query, (problem_id, point, point))\n conn.commit()\n\n\nif __name__ == '__main__':\n main(sys.argv[1])\n", "sub_path": "atcoder-problems-predictor/predictor.py", "file_name": "predictor.py", "file_ext": "py", "file_size_in_byte": 3038, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "json.load", "line_number": 11, "usage_type": "call"}, {"api_name": "psycopg2.connect", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 72, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 77, "usage_type": "call"}, {"api_name": "xgboost.XGBRegressor", "line_number": 83, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 101, "usage_type": "attribute"}]} +{"seq_id": "5148359", "text": "from flask import Flask, render_template, session, url_for, redirect, flash, request\nfrom app import app\nimport re\nimport pymysql\nimport mysql.connector\nimport ctypes\n\n\nmydb = mysql.connector.connect(host='localhost', user='root', passwd='Hundo978!', auth_plugin='mysql_native_password', database=\"cs4400spring2020\")\nmycursor = mydb.cursor(buffered=True)\n\n'''\n-------------------------------------------------------------------------------------\npages\n-------------------------------------------------------------------------------------\n'''\n\n@app.route('/')\n@app.route('/home.html')\ndef home():\n return render_template('home.html', title='Home')\n\n@app.route('/login.html', methods=['POST', 'GET'])\ndef login():\n if request.method == 'POST':\n username = request.form['username']\n password = request.form['password']\n if validate_login(username, password):\n session['username'] = username\n \n # get the uer type\n sql_Q =(f'SELECT username, userType FROM login_classifier WHERE username = \"{username}\"')\n mycursor.execute(sql_Q)\n result = mycursor.fetchone()\n print(\"user type: \" , result[1])\n session['user_type'] = result[1]\n\n flash(\"Logged in\")\n return redirect('/about.html')\n else:\n flash(\"Log in denied\")\n return render_template('login.html', title='Login')\n\n@app.route('/about.html')\ndef about():\n return render_template('about.html', title='About')\n\n@app.route('/register.html', methods=['POST', 'GET'])\ndef register():\n if request.method == 'POST':\n username = request.form['username']\n email = request.form['email']\n firstName = request.form['firstName']\n lastName = request.form['lastName']\n password = request.form['password']\n balance = request.form['balance']\n balance = emptyStringToNone(balance)\n user_type = request.form['user_type']\n if register_user(username, email, firstName, lastName, password, balance, user_type):\n flash(\"Registered\")\n return redirect('/login.html')\n else:\n flash(\"Registration Invalid\")\n return render_template('register.html', title='Register')\n\n@app.route('/logout')\ndef logout():\n session.pop('username', None)\n return redirect('/home.html')\n # NOTE: cookies remian between sessions unless logged out, \n # you can also change the key varibale in __init__.py to clear session cookies\n\n@app.route('/screen4.html', methods=['POST', 'GET'])\ndef screen4():\n if request.method == 'POST':\n if request.form['submit_button'] == 'Filter':\n buildingName = request.form['buildingName']\n buildingName = emptyStringToNone(buildingName)\n buildingTagContain = request.form['buildingTagContain']\n buildingTagContain = emptyStringToNone(buildingTagContain)\n stationName = request.form['stationName']\n stationName = emptyStringToNone(stationName)\n minCapacity = request.form['capacityMin']\n minCapacity = emptyStringToNone(minCapacity)\n maxCapacity = request.form['capacityMax']\n maxCapacity = emptyStringToNone(maxCapacity)\n resultTable = filter_building_station(buildingName, buildingTagContain, stationName, minCapacity, maxCapacity)\n return render_template('screen4.html', title='Screen4', data = resultTable)\n \n elif request.form['submit_button'] == 'Create Building':\n return redirect('/screen5.html')\n\n elif request.form['submit_button'] == 'Update Building':\n rowBuildingName = request.form.get('rowBuildingName')\n return redirect(url_for('screen6', building=rowBuildingName)) \n\n elif request.form['submit_button'] == 'Delete Building':\n rowBuildingName = request.form.get('rowBuildingName')\n if rowBuildingName != \"\" and rowBuildingName != \"None\" and delete_building(rowBuildingName):\n print(\"DELETED BUILDING\") \n return render_template('screen4.html', title='Screen4')\n\n elif request.form['submit_button'] == 'Create Station':\n return redirect('/screen7.html')\n\n elif request.form['submit_button'] == 'Update Station':\n rowBuildingName = request.form.get('rowBuildingName')\n # rowBuildingTags = request.form.get('rowBuildingTags')\n rowStation = request.form.get('rowStation')\n rowCapacity = request.form.get('rowCapacity')\n # rowFoodTrucks = request.form.get('rowFoodTrucks')\n if rowStation != \"None\" and rowStation != \"\": # prevents a row with None station or no row being selected form going to STATION UPDATE page\n return redirect(url_for('screen8', building=rowBuildingName, station = rowStation, capacity = rowCapacity, ))\n else:\n print(\"station is null!!!!\")\n pass\n elif request.form['submit_button'] == 'Delete Station':\n rowStation = request.form.get('rowStation')\n if rowStation != \"\" and rowStation != \"None\" and delete_station(rowStation):\n print(\"DELETED STATION\") \n return render_template('screen4.html', title='Screen4')\n\n return render_template('screen4.html', title='Screen4')\n\n@app.route('/screen5.html', methods=['POST', 'GET'])\ndef screen5():\n if request.method == 'POST':\n #create building request\n buildingName = request.form['buildingName'] \n description = request.form['description']\n tag = request.form['tag']\n tags = tag.split(',')\n if buildingName != \"\" and description != \"\" and tag != \"\" and create_building(buildingName, description):\n print(\"BUILDING CREATED\")\n for tag in tags:\n add_building_tag(buildingName, tag)\n print(\"TAG(S) ADDED\")\n else:\n return render_template('screen5.html', title='Screen5', buildingName = buildingName, description = description)\n return render_template('screen5.html', title='Screen5')\n\n@app.route('/screen6.html', methods=['POST', 'GET'])\ndef screen6():\n if request.method == 'GET':\n gotBuildingName = request.args.get('building')\n result = get_building_info(gotBuildingName)\n if not result:\n result = \"\"\n else:\n result = result[0][0]\n resultTable = get_tags(gotBuildingName)\n return render_template('screen6.html', title='Screen6', buildingName = gotBuildingName, data = resultTable, description = result)\n if request.method == 'POST':\n if request.form['submit_button'] == 'Add Tag':\n buildingName = request.form['buildingName']\n newBuildingName = request.form['newBuildingName']\n description = request.form['description']\n tag = request.form['tag']\n if buildingName != \"\" and tag != \"\" and add_building_tag(buildingName, tag):\n print(\"TAG ADDED\")\n result = get_building_info(buildingName)\n if not result:\n result = \"\"\n else:\n result = result[0][0]\n resultTable = get_tags(buildingName) #BUG: possibly fix the way description is got cause it will undo changes if ADD TAG is called\n return render_template('screen6.html', title='Screen6', data = resultTable, buildingName = buildingName, newBuildingName = newBuildingName, description = description)\n else:\n resultTable = get_tags(buildingName)\n return render_template('screen6.html', title='Screen6', data = resultTable, buildingName = buildingName, newBuildingName = newBuildingName, description = description)\n\n\n elif request.form['submit_button'] == 'Delete Tag':\n buildingName = request.form['buildingName']\n newBuildingName = request.form['newBuildingName']\n description = request.form['description']\n tag = request.form['tag']\n if buildingName != \"\" and tag != \"\" and delete_building_tag(buildingName, tag): \n print(\"TAG DELETED\")\n result = get_building_info(buildingName)\n if not result:\n result = \"\"\n else:\n result = result[0][0]\n resultTable = get_tags(buildingName)\n return render_template('screen6.html', title='Screen6', data = resultTable, buildingName = buildingName, newBuildingName = newBuildingName, description = result)\n else:\n resultTable = get_tags(buildingName)\n return render_template('screen6.html', title='Screen6', data = resultTable, buildingName = buildingName, newBuildingName = newBuildingName, description = description)\n \n elif request.form['submit_button'] == 'Get Tags And Description':\n buildingName = request.form['buildingName']\n newBuildingName = request.form['newBuildingName']\n description = request.form['description']\n tag = request.form['tag']\n result = get_building_info(buildingName)\n if not result:\n result = \"\"\n else:\n result = result[0][0]\n resultTable = get_tags(buildingName)\n return render_template('screen6.html', title='Screen6', data = resultTable, buildingName = buildingName, newBuildingName = newBuildingName, description = result)\n\n else: #update building request\n buildingName = request.form['buildingName']\n newBuildingName = request.form['newBuildingName'] \n description = request.form['description']\n if buildingName != \"\" and description != \"\" and update_building(buildingName, newBuildingName, description):\n print(\"BUILDING UPDATED\")\n else:\n return render_template('screen6.html', title='Screen6', buildingName = buildingName, newBuildingName = newBuildingName, description = description)\n return render_template('screen6.html', title='Screen6')\n\n@app.route('/screen7.html', methods=['POST', 'GET'])\ndef screen7():\n if request.method == 'POST':\n if request.form['submit_button'] == 'Create Station':\n stationName = request.form['stationName'] \n capacity = request.form['capacity']\n sponsBuilding = request.form.get('sponsBuilding')\n if stationName != \"\" and capacity != \"\" and sponsBuilding != \"\" and create_station(stationName, sponsBuilding, capacity):\n print(\"STATION CREATED\")\n else:\n print(\"uhhhh\")\n buildings = get_available_buildings()\n return render_template('screen7.html', title='Screen7', stationName = stationName, data = buildings)\n else:\n stationName = request.form['stationName'] \n capacity = request.form['capacity']\n sponsBuilding = request.form.get('sponsBuilding')\n buildings = get_available_buildings()\n return render_template('screen7.html', title='Screen7', stationName = stationName, data = buildings)\n return render_template('screen7.html', title='Screen7')\n\n@app.route('/screen8.html', methods=['POST', 'GET'])\ndef screen8():\n if request.method == 'GET':\n gotBuildingName = request.args.get('building')\n gotStation = request.args.get('station')\n gotCapacity = request.args.get('capacity')\n\n buildings = get_available_buildings()\n return render_template('screen8.html', title='Screen8', stationName = gotStation, currSponsBuilding = gotBuildingName, capacity = gotCapacity, data = buildings)\n \n if request.method == 'POST':\n print(\"text1\") \n if request.form['submit_button'] == 'Update Station':\n stationName = request.form.get('stationNameAccess') # NOTE: you get station from here becuase the regular station is disabled from access \n capacity = request.form['capacity']\n sponsBuilding = request.form.get('sponsBuilding')\n if stationName != \"\" and capacity != \"\" and sponsBuilding != \"\" and update_station(stationName, capacity, sponsBuilding):\n print(\"STATION UPDATED\")\n return render_template('home.html')\n else:\n print(\"STATION NOT UPDATED\")\n buildings = get_available_buildings()\n return render_template('screen8.html', title='Screen8', stationName = stationName, currSponsBuilding = sponsBuilding, data = buildings, capacity = capacity)\n return render_template('screen8.html', title='Screen8')\n\n@app.route('/screen9.html', methods=['POST', 'GET'])\ndef screen9():\n if request.method == 'POST':\n if request.form['submit_button'] == 'Filter':\n foodName = request.form.get('foodName')\n sort = 'name' # can be: 'name', 'menuCount', 'purchaseCount'\n order = 'ASC' # can be: 'ASC', 'DESC'\n\n foodNamesList = get_food_names()\n foodNamesList.insert(0, \"\")\n resultTable = filter_food(foodName, sort, order)\n\n return render_template('screen9.html', title='Screen9', data = resultTable, foodNames = foodNamesList)\n\n elif request.form['submit_button'] == 'Delete Food':\n rowFoodName = request.form.get('rowFoodName')\n if rowFoodName != \"\" and rowFoodName != \"None\" and delete_food(rowFoodName):\n print(\"DELETED FOOD\") \n foodNamesList = get_food_names()\n foodNamesList.insert(0, \"\")\n return render_template('screen9.html', title='Screen9', foodNames = foodNamesList)\n \n elif request.form['submit_button'] == 'Create Food':\n return redirect(url_for('screen10'))\n\n foodNamesList = get_food_names()\n foodNamesList.insert(0, \"\")\n return render_template('screen9.html', title='Screen9', foodNames = foodNamesList)\n\n@app.route('/screen10.html', methods=['POST', 'GET'])\ndef screen10():\n if request.method == 'POST':\n if request.form['submit_button'] == 'Create Food':\n foodName = request.form['foodName'] \n if create_food(foodName):\n print(\"FOOD CREATED\")\n return render_template('screen10.html', title='Screen10')\n\n@app.route('/screen11.html')\ndef screen11():\n return render_template('screen11.html', title='Screen11')\n\n@app.route('/screen12.html')\ndef screen12():\n return render_template('screen12.html', title='Screen12')\n\n@app.route('/screen13.html')\ndef screen13():\n return render_template('screen13.html', title='Screen13')\n\n@app.route('/screen14.html')\ndef screen14():\n return render_template('screen14.html', title='Screen14')\n\n@app.route('/screen15.html')\ndef screen15():\n return render_template('screen15.html', title='Screen15')\n\n@app.route('/screen16.html')\ndef screen16():\n return render_template('screen16.html', title='Screen16')\n\n@app.route('/screen17.html')\ndef screen17():\n return render_template('screen17.html', title='Screen17')\n\n@app.route('/screen18.html')\ndef screen18():\n return render_template('screen18.html', title='Screen18')\n\n@app.route('/screen19.html')\ndef screen19():\n return render_template('screen19.html', title='Screen19')\n\n\n\n'''\n-------------------------------------------------------------------------------------\nfunctional methods\n-------------------------------------------------------------------------------------\n'''\n#validates the login credentials\ndef validate_login(username, password): \n global mycursor\n sql_Q = (f'call login(\"{username}\", \"{password}\")') # calls the login() procedure\n mycursor.execute(sql_Q)\n return 1 == mycursor._affected_rows\n\n# TODO: possibly imporve error notification to end user\n# SCREEN 2 (validates the registeration)\ndef register_user(username, email, firstName, lastName, password, balance, user_type): \n global mycursor\n sqlErrorOccured = False\n try:\n mycursor.callproc('register',args=(username, email, firstName, lastName, password, balance, user_type))\n except mysql.connector.Error as err:\n print(err)\n print(\"Error Code:\", err.errno)\n print(\"SQLSTATE\", err.sqlstate)\n print(\"Message\", err.msg)\n sqlErrorOccured = True\n else:\n mydb.commit()\n finally:\n if sqlErrorOccured:\n return False\n else:\n return True\n\n# SCREEN 4 (filters buildings and stations)\ndef filter_building_station(buildingName=None, buildingTagContain=None, stationName=None, minCapacity=None, maxCapacity=None): \n global mycursor\n sqlErrorOccured = False\n try:\n mycursor.callproc('ad_filter_building_station',args=(buildingName, buildingTagContain, stationName, minCapacity, maxCapacity ))\n except mysql.connector.Error as err:\n print(err)\n print(\"Error Code:\", err.errno)\n print(\"SQLSTATE\", err.sqlstate)\n print(\"Message\", err.msg)\n sqlErrorOccured = True\n else:\n #mydb.commit()\n pass\n finally:\n if sqlErrorOccured:\n print(\"failed\")\n return None\n else:\n #return 1 == mycursor._affected_rows\n sql_Q = 'SELECT * FROM cs4400spring2020.ad_filter_building_station_result'\n mycursor.execute(sql_Q)\n result = mycursor.fetchall()\n for a in result:\n print(a)\n return result\n\n# SCREEN 5 (creates building)\ndef create_building(buildingName, description): \n global mycursor\n sqlErrorOccured = False\n try:\n mycursor.callproc('ad_create_building',args=(buildingName, description))\n except mysql.connector.Error as err:\n print(err)\n print(\"Error Code:\", err.errno)\n print(\"SQLSTATE\", err.sqlstate)\n print(\"Message\", err.msg)\n Mbox('WARNING', \"Building already exists\", 0)\n sqlErrorOccured = True\n else:\n mydb.commit()\n finally:\n if sqlErrorOccured:\n return False\n else:\n return True\n\ndef add_building_tag(buildingName, tag): \n global mycursor\n sqlErrorOccured = False\n try:\n mycursor.callproc('ad_add_building_tag',args=(buildingName, tag))\n mydb.commit()\n except mysql.connector.Error as err:\n print(err)\n print(\"Error Code:\", err.errno)\n print(\"SQLSTATE\", err.sqlstate)\n print(\"Message\", err.msg)\n Mbox('WARNING', \"Tag already exists\", 0)\n sqlErrorOccured = True\n finally:\n if sqlErrorOccured:\n return False\n else:\n return True\n\ndef delete_building_tag(buildingName, tag): \n global mycursor\n sqlErrorOccured = False\n try:\n mycursor.callproc('ad_remove_building_tag',args=(buildingName, tag))\n mydb.commit()\n except mysql.connector.Error as err:\n print(err)\n print(\"Error Code:\", err.errno)\n print(\"SQLSTATE\", err.sqlstate)\n print(\"Message\", err.msg)\n Mbox('WARNING', \"Tag doesn't exist\", 0)\n sqlErrorOccured = True\n finally:\n if sqlErrorOccured:\n return False\n else:\n return True\n\ndef get_tags(buildingName): \n global mycursor\n sqlErrorOccured = False\n try:\n sql_Q = (f'call ad_view_building_tags(\"{buildingName}\")')\n mycursor.execute(sql_Q)\n #mycursor.callproc('ad_view_building_tags',args=(buildingName))\n except mysql.connector.Error as err:\n print(err)\n print(\"Error Code:\", err.errno)\n print(\"SQLSTATE\", err.sqlstate)\n print(\"Message\", err.msg)\n sqlErrorOccured = True\n else:\n mydb.commit()\n pass\n finally:\n if sqlErrorOccured:\n return None\n else:\n #return 1 == mycursor._affected_rows\n sql_Q = 'SELECT * FROM cs4400spring2020.ad_view_building_tags_result'\n mycursor.execute(sql_Q)\n result = mycursor.fetchall()\n for a in result:\n print(a)\n return result\n\ndef get_building_info(buildingName): \n global mycursor\n sqlErrorOccured = False\n try:\n sql_Q = (f'call ad_view_building_general(\"{buildingName}\")')\n mycursor.execute(sql_Q)\n # mycursor.callproc('call ad_view_building_general',args=(buildingName))\n except mysql.connector.Error as err:\n print(err)\n print(\"Error Code:\", err.errno)\n print(\"SQLSTATE\", err.sqlstate)\n print(\"Message\", err.msg)\n sqlErrorOccured = True\n else:\n mydb.commit()\n pass\n finally:\n if sqlErrorOccured:\n return None\n else:\n #return 1 == mycursor._affected_rows\n sql_Q = 'SELECT description FROM cs4400spring2020.ad_view_building_general_result'\n mycursor.execute(sql_Q)\n result = mycursor.fetchall()\n for a in result:\n print(a)\n return result\n\n#Screen 6 (update building)\ndef update_building(buildingName, newBuildingName, description): \n global mycursor\n sqlErrorOccured = False\n try:\n if newBuildingName == \"\":\n mycursor.callproc('ad_update_building',args=(buildingName, buildingName, description))\n else:\n mycursor.callproc('ad_update_building',args=(buildingName, newBuildingName, description))\n except mysql.connector.Error as err:\n print(err)\n print(\"Error Code:\", err.errno)\n print(\"SQLSTATE\", err.sqlstate)\n print(\"Message\", err.msg)\n Mbox('WARNING', \"Building doesn't exist\", 0)\n sqlErrorOccured = True\n else:\n mydb.commit()\n finally:\n if sqlErrorOccured:\n return False\n else:\n return True\n\n# Screen 7 (create station)\ndef create_station(stationName, sponsBuilding, capacity): \n global mycursor\n sqlErrorOccured = False\n try:\n mycursor.callproc('ad_create_station',args=(stationName, sponsBuilding, capacity))\n except mysql.connector.Error as err:\n print(err)\n print(\"Error Code:\", err.errno)\n print(\"SQLSTATE\", err.sqlstate)\n print(\"Message\", err.msg)\n Mbox('WARNING', \"Station already exists\", 0)\n sqlErrorOccured = True\n else:\n mydb.commit()\n finally:\n if sqlErrorOccured:\n return False\n else:\n return True\n\ndef get_available_buildings(): \n global mycursor\n sqlErrorOccured = False\n try:\n sql_Q = \"call ad_get_available_building()\"\n mycursor.execute(sql_Q)\n #mycursor.callproc('ad_view_building_tags',args=(buildingName))\n except mysql.connector.Error as err:\n print(err)\n print(\"Error Code:\", err.errno)\n print(\"SQLSTATE\", err.sqlstate)\n print(\"Message\", err.msg)\n sqlErrorOccured = True\n else:\n mydb.commit()\n pass\n finally:\n if sqlErrorOccured:\n return None\n else:\n #return 1 == mycursor._affected_rows\n sql_Q = 'SELECT * FROM cs4400spring2020.ad_get_available_building_result'\n mycursor.execute(sql_Q)\n result = mycursor.fetchall()\n for a in result:\n print(a)\n return result\n\ndef get_station_info(stationName): \n global mycursor\n sqlErrorOccured = False\n try:\n sql_Q = (f'call ad_view_station(\"{stationName}\")')\n mycursor.execute(sql_Q)\n # mycursor.callproc('call ad_view_building_general',args=(buildingName))\n except mysql.connector.Error as err:\n print(err)\n print(\"Error Code:\", err.errno)\n print(\"SQLSTATE\", err.sqlstate)\n print(\"Message\", err.msg)\n sqlErrorOccured = True\n else:\n mydb.commit()\n pass\n finally:\n if sqlErrorOccured:\n return None\n else:\n #return 1 == mycursor._affected_rows\n sql_Q = 'SELECT * FROM cs4400spring2020.ad_view_station_result'\n mycursor.execute(sql_Q)\n result = mycursor.fetchall()\n for a in result:\n print(a)\n return result\n\n# Screen 8\ndef update_station(stationName, capacity, buildingName): \n global mycursor\n sqlErrorOccured = False\n try:\n mycursor.callproc('ad_update_station',args=(stationName, capacity, buildingName))\n except mysql.connector.Error as err:\n print(err)\n print(\"Error Code:\", err.errno)\n print(\"SQLSTATE\", err.sqlstate)\n print(\"Message\", err.msg)\n Mbox('WARNING', \"Station doesn't exist??\", 0)\n sqlErrorOccured = True\n else:\n mydb.commit()\n finally:\n if sqlErrorOccured:\n return False\n else:\n return True\n\ndef delete_building(buildingName): \n global mycursor\n sqlErrorOccured = False\n try:\n sql_Q = (f'call ad_delete_building(\"{buildingName}\")')\n mycursor.execute(sql_Q)\n\n # mycursor.callproc('ad_delete_building',args=(buildingName))\n except mysql.connector.Error as err:\n print(err)\n print(\"Error Code:\", err.errno)\n print(\"SQLSTATE\", err.sqlstate)\n print(\"Message\", err.msg)\n Mbox('WARNING', \"Building doesn't exist or can't be deleted due to constraint\", 0)\n sqlErrorOccured = True\n else:\n mydb.commit()\n finally:\n if sqlErrorOccured:\n return False\n else:\n return True\n\ndef delete_station(stationName): \n global mycursor\n sqlErrorOccured = False\n try:\n sql_Q = (f'call ad_delete_station(\"{stationName}\")')\n mycursor.execute(sql_Q)\n\n # mycursor.callproc('ad_delete_station',args=(stationName))\n except mysql.connector.Error as err:\n print(err)\n print(\"Error Code:\", err.errno)\n print(\"SQLSTATE\", err.sqlstate)\n print(\"Message\", err.msg)\n Mbox('WARNING', \"Station doesn't exist or can't be deleted due to constraint\", 0)\n sqlErrorOccured = True\n else:\n mydb.commit()\n finally:\n if sqlErrorOccured:\n return False\n else:\n return True\n\n# screen 9\ndef delete_food(foodName): \n global mycursor\n sqlErrorOccured = False\n try:\n sql_Q = (f'call ad_delete_food(\"{foodName}\")')\n mycursor.execute(sql_Q)\n\n except mysql.connector.Error as err:\n print(err)\n print(\"Error Code:\", err.errno)\n print(\"SQLSTATE\", err.sqlstate)\n print(\"Message\", err.msg)\n Mbox('WARNING', \"Food doesn't exist or can't be deleted due to constraint\", 0)\n sqlErrorOccured = True\n else:\n mydb.commit()\n finally:\n if sqlErrorOccured:\n return False\n else:\n return True\n\ndef filter_food(foodName, sort, order): \n global mycursor\n sqlErrorOccured = False\n try:\n # print(f'foodName={foodName}, sort={sort}, order={order}')\n # mycursor.callproc('ad_filter_food',args=(foodName, sort, order))\n\n mycursor.callproc('ad_filter_food',args=(foodName, sort, order))\n except mysql.connector.Error as err:\n print(err)\n print(\"Error Code:\", err.errno)\n print(\"SQLSTATE\", err.sqlstate)\n print(\"Message\", err.msg)\n sqlErrorOccured = True\n else:\n mydb.commit()\n pass\n finally:\n if sqlErrorOccured:\n print(\"failed\")\n return None\n else:\n #return 1 == mycursor._affected_rows\n sql_Q = 'SELECT * FROM cs4400spring2020.ad_filter_food_result'\n mycursor.execute(sql_Q)\n result = mycursor.fetchall()\n for a in result:\n print(a)\n return result\n\ndef get_food_names(): \n global mycursor\n sqlErrorOccured = False\n try:\n foodName = None\n sort = 'name'\n order = 'ASC'\n mycursor.callproc('ad_filter_food',args=(foodName, sort, order))\n except mysql.connector.Error as err:\n print(err)\n print(\"Error Code:\", err.errno)\n print(\"SQLSTATE\", err.sqlstate)\n print(\"Message\", err.msg)\n sqlErrorOccured = True\n else:\n mydb.commit()\n pass\n finally:\n if sqlErrorOccured:\n print(\"failed\")\n return None\n else:\n #return 1 == mycursor._affected_rows\n sql_Q = 'SELECT foodName FROM cs4400spring2020.ad_filter_food_result'\n mycursor.execute(sql_Q)\n result = mycursor.fetchall()\n for a in result:\n print(a)\n return result\n\n# Screen 10 \ndef create_food(foodName): \n global mycursor\n sqlErrorOccured = False\n try:\n sql_Q = (f'call ad_create_food(\"{foodName}\")')\n mycursor.execute(sql_Q)\n\n # mycursor.callproc('ad_create_food',args=(foodName))\n except mysql.connector.Error as err:\n print(err)\n print(\"Error Code:\", err.errno)\n print(\"SQLSTATE\", err.sqlstate)\n print(\"Message\", err.msg)\n Mbox('WARNING', \"Food already exists\", 0)\n sqlErrorOccured = True\n else:\n mydb.commit()\n finally:\n if sqlErrorOccured:\n return False\n else:\n return True\n'''\n-------------------------------------------------------------------------------------\n technical methods\n -------------------------------------------------------------------------------------\n'''\n\ndef emptyStringToNone(s):\n if s is '':\n return None\n return str(s)\n\ndef Mbox(title, text, style):\n return ctypes.windll.user32.MessageBoxW(0, text, title, style)\n\n@app.before_request #runs this request prior to each page request\ndef require_login():\n non_logged_routes = ['home', 'login', 'register']\n do_not_allow_relog = ['login']\n customer_only_routes = []\n admin_only_routes = []\n manager_only_routes = []\n staff_only_routes = []\n\n if request.endpoint not in non_logged_routes and 'username' not in session:\n flash('Please log in first.', 'error')\n return redirect('/login.html')\n\n #prevents relogging in while active session\n if request.endpoint in do_not_allow_relog and 'username' in session:\n flash('Cannot relog until logged out', 'error')\n print(\"CANNOT RELOG UNTIL LOGGED OUT\")\n return redirect('/home.html')\n \n # restricts pages based on user type\n elif 'user_type' in session:\n if request.endpoint in customer_only_routes and not(session['user_type'] == 'Customer' or session['user_type'] == 'Admin-Customer' or session['user_type'] == 'Staff-Customer' or session['user_type'] == 'Manager-Customer'):\n print(\"ONLY ACCESSIBLE FOR CUSTOMERS\")\n return redirect(url_for('home'))\n \n if request.endpoint in admin_only_routes and not(session['user_type'] == 'Admin' or session['user_type'] == 'Admin-Customer'): \n print(\"ONLY ACCESSIBLE FOR ADMINS\")\n return redirect(url_for('home'))\n\n if request.endpoint in admin_only_routes and not(session['user_type'] == 'Manager' or session['user_type'] == 'Manager-Customer'): \n print(\"ONLY ACCESSIBLE FOR MANAGER\")\n return redirect(url_for('home'))\n\n if request.endpoint in admin_only_routes and not(session['user_type'] == 'Staff' or session['user_type'] == 'Staff-Customer'): \n print(\"ONLY ACCESSIBLE FOR STAFF\")\n return redirect(url_for('home'))\n\n\n", "sub_path": "app/routes.py", "file_name": "routes.py", "file_ext": "py", "file_size_in_byte": 31518, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "mysql.connector.connector.connect", "line_number": 9, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 9, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 9, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 21, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 18, "usage_type": "call"}, {"api_name": "app.app", "line_number": 18, "usage_type": "name"}, {"api_name": "app.app.route", "line_number": 19, "usage_type": "call"}, {"api_name": "app.app", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 36, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 42, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 23, "usage_type": "call"}, {"api_name": "app.app", "line_number": 23, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 46, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 44, "usage_type": "call"}, {"api_name": "app.app", "line_number": 44, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 50, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 50, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 51, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 51, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 52, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 52, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 53, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 53, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 54, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 55, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 55, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 56, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 56, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 58, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 58, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 60, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 64, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 48, "usage_type": "call"}, {"api_name": "app.app", "line_number": 48, "usage_type": "name"}, {"api_name": "flask.session.pop", "line_number": 68, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 68, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 69, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 66, "usage_type": "call"}, {"api_name": "app.app", "line_number": 66, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 75, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 75, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 76, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 76, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 77, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 77, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 79, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 79, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 81, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 81, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 83, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 83, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 85, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 85, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 88, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 90, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 90, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 91, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 93, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 93, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 94, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 94, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 94, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 95, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 95, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 97, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 97, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 98, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 98, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 98, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 101, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 103, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 103, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 104, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 106, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 106, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 107, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 107, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 107, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 109, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 109, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 109, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 110, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 110, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 110, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 113, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 113, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 117, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 117, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 118, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 118, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 118, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 121, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 123, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 73, "usage_type": "call"}, {"api_name": "app.app", "line_number": 73, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 127, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 127, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 129, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 129, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 130, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 130, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 131, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 131, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 139, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 140, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 125, "usage_type": "call"}, {"api_name": "app.app", "line_number": 125, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 144, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 144, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 145, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 145, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 145, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 152, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 153, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 153, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 154, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 154, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 155, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 155, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 156, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 156, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 157, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 157, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 158, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 158, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 167, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 170, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 173, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 173, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 174, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 174, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 175, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 175, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 176, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 176, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 177, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 177, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 186, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 189, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 191, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 191, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 192, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 192, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 193, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 193, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 194, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 194, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 195, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 195, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 202, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 205, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 205, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 206, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 206, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 207, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 207, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 211, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 212, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 142, "usage_type": "call"}, {"api_name": "app.app", "line_number": 142, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 216, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 216, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 217, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 217, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 218, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 218, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 219, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 219, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 220, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 220, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 220, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 226, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 228, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 228, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 229, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 229, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 230, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 230, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 230, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 232, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 233, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 214, "usage_type": "call"}, {"api_name": "app.app", "line_number": 214, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 237, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 237, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 238, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 238, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 238, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 239, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 239, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 239, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 240, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 240, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 240, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 243, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 245, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 245, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 247, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 247, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 248, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 248, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 248, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 249, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 249, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 250, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 250, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 250, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 253, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 257, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 258, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 235, "usage_type": "call"}, {"api_name": "app.app", "line_number": 235, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 262, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 262, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 263, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 263, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 264, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 264, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 264, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 272, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 274, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 274, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 275, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 275, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 275, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 280, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 282, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 282, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 283, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 283, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 287, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 260, "usage_type": "call"}, {"api_name": "app.app", "line_number": 260, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 291, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 291, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 292, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 292, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 293, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 293, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 296, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 289, "usage_type": "call"}, {"api_name": "app.app", "line_number": 289, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 300, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 298, "usage_type": "call"}, {"api_name": "app.app", "line_number": 298, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 304, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 302, "usage_type": "call"}, {"api_name": "app.app", "line_number": 302, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 308, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 306, "usage_type": "call"}, {"api_name": "app.app", "line_number": 306, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 312, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 310, "usage_type": "call"}, {"api_name": "app.app", "line_number": 310, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 316, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 314, "usage_type": "call"}, {"api_name": "app.app", "line_number": 314, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 320, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 318, "usage_type": "call"}, {"api_name": "app.app", "line_number": 318, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 324, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 322, "usage_type": "call"}, {"api_name": "app.app", "line_number": 322, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 328, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 326, "usage_type": "call"}, {"api_name": "app.app", "line_number": 326, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 332, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 330, "usage_type": "call"}, {"api_name": "app.app", "line_number": 330, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 355, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 355, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 375, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 375, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 403, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 403, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 424, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 424, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 443, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 443, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 463, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 463, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 491, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 491, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 521, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 521, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 542, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 542, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 564, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 564, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 592, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 592, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 619, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 619, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 642, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 642, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 665, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 665, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 688, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 688, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 711, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 711, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 741, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 741, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 772, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 772, "usage_type": "name"}, {"api_name": "ctypes.windll.user32.MessageBoxW", "line_number": 798, "usage_type": "call"}, {"api_name": "ctypes.windll", "line_number": 798, "usage_type": "attribute"}, {"api_name": "flask.request.endpoint", "line_number": 809, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 809, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 809, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 810, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 811, "usage_type": "call"}, {"api_name": "flask.request.endpoint", "line_number": 814, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 814, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 814, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 815, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 817, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 820, "usage_type": "name"}, {"api_name": "flask.request.endpoint", "line_number": 821, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 821, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 821, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 823, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 823, "usage_type": "call"}, {"api_name": "flask.request.endpoint", "line_number": 825, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 825, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 825, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 827, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 827, "usage_type": "call"}, {"api_name": "flask.request.endpoint", "line_number": 829, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 829, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 829, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 831, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 831, "usage_type": "call"}, {"api_name": "flask.request.endpoint", "line_number": 833, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 833, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 833, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 835, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 835, "usage_type": "call"}, {"api_name": "app.app.before_request", "line_number": 800, "usage_type": "attribute"}, {"api_name": "app.app", "line_number": 800, "usage_type": "name"}]} +{"seq_id": "43347006", "text": "#!/usr/bin/env python\nimport argparse\nimport networkx as nx\n\nfrom graph import draw\n\n\ndef parse_args():\n parser = argparse.ArgumentParser()\n\n parser.add_argument('graph', help='input graph file path (gpickle)', nargs='+')\n\n return parser.parse_args()\n\ndef main():\n args = parse_args()\n\n for graph_path in args.graph:\n graph = nx.read_gpickle(graph_path)\n draw(graph)\n\n\nif __name__ == '__main__':\n main()\n \n", "sub_path": "draw.py", "file_name": "draw.py", "file_ext": "py", "file_size_in_byte": 441, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 9, "usage_type": "call"}, {"api_name": "networkx.read_gpickle", "line_number": 19, "usage_type": "call"}, {"api_name": "graph.draw", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "647504268", "text": "\"\"\"Authorized users 2: Electric Boogaloo\n\nRevision ID: 35b41a74130\nRevises: 2bbb7832872\nCreate Date: 2015-12-20 19:24:19.301136\n\n\"\"\"\n\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '35b41a74130'\ndown_revision = '2bbb7832872'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n op.drop_table('authorized_users')\n op.create_table(\n 'authorized_users',\n sa.Column('id', sa.Integer, primary_key=True),\n sa.Column('email', sa.String(length=255), nullable=False),\n sa.Column('name', sa.Unicode, nullable=False),\n sa.PrimaryKeyConstraint('id'),\n sa.UniqueConstraint('email'),\n )\n op.drop_column('audit_entries', 'credential_id')\n op.add_column('audit_entries', sa.Column('user_id', sa.Integer(), nullable=False))\n op.create_foreign_key(None, 'audit_entries', 'authorized_users', ['user_id'], ['id'])\n\n\ndef downgrade():\n op.drop_column('audit_entries', 'user_id')\n op.add_column('audit_entries', sa.Column('credential_id', sa.Integer, nullable=False))\n op.create_foreign_key('audit_entries_credential_id_fkey',\n 'audit_entries', 'credentials', ['credential_id'], ['id'])\n op.drop_table('authorized_users')\n op.create_table(\n 'authorized_users',\n sa.Column('email', sa.String(length=255), nullable=False),\n sa.PrimaryKeyConstraint('email')\n )\n", "sub_path": "mimir/alembic/versions/35b41a74130_authorized_users_2_electric_boogaloo.py", "file_name": "35b41a74130_authorized_users_2_electric_boogaloo.py", "file_ext": "py", "file_size_in_byte": 1417, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "alembic.op.drop_table", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "alembic.op.create_table", "line_number": 22, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 22, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 24, "usage_type": "attribute"}, {"api_name": "sqlalchemy.Column", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.Unicode", "line_number": 26, "usage_type": "attribute"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy.UniqueConstraint", "line_number": 28, "usage_type": "call"}, {"api_name": "alembic.op.drop_column", "line_number": 30, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 30, "usage_type": "name"}, {"api_name": "alembic.op.add_column", "line_number": 31, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 31, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 31, "usage_type": "call"}, {"api_name": "alembic.op.create_foreign_key", "line_number": 32, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 32, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 36, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 36, "usage_type": "name"}, {"api_name": "alembic.op.add_column", "line_number": 37, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 37, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 37, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 37, "usage_type": "attribute"}, {"api_name": "alembic.op.create_foreign_key", "line_number": 38, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 38, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 40, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 40, "usage_type": "name"}, {"api_name": "alembic.op.create_table", "line_number": 41, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 41, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 43, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 43, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "276340480", "text": "# -*- coding:utf-8 -*-\nimport xlrd\nimport xlwt\n\nworkbook=xlwt.Workbook(encoding='utf-8')\nbooksheet=workbook.add_sheet(\"sheet1\",cell_overwrite_ok=True)\n\n\ndef hebingExcel():\n try:\n L = []\n data1=xlrd.open_workbook(\"技能点申报_假想敌.xlsx\")\n data2=xlrd.open_workbook(\"技能点申报-本人.xlsx\")\n table1=data1.sheets()[0]\n table2=data2.sheets()[0]\n nrows1=table1.nrows\n nrows2=table2.nrows\n for i in range(nrows1):\n if i!=0:\n L.append(table1.row_values(i))\n for j,row in enumerate(L):\n for k,col in enumerate(row):\n booksheet.write(j,k,col)\n workbook.save('book.xls')\n except Exception as e:\n print(e)\n\nif __name__==\"__main__\":\n hebingExcel()", "sub_path": "基础学习/操作excel/test_HebingExcel.py", "file_name": "test_HebingExcel.py", "file_ext": "py", "file_size_in_byte": 752, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "xlwt.Workbook", "line_number": 5, "usage_type": "call"}, {"api_name": "xlrd.open_workbook", "line_number": 12, "usage_type": "call"}, {"api_name": "xlrd.open_workbook", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "428607710", "text": "import os, re, sys, threading, signal\nfrom bottle import route, run, debug, request, validate, static_file, error, abort,response\nfrom bottle import jinja2_view as view, jinja2_template as template\nfrom utils import MPDInterface\nimport json\nimport settings\n\n# only needed when you run Bottle on mod_wsgi\nfrom bottle import default_app\n\n@route('/')\n@view('index.html')\ndef main_page():\n mpdi = MPDInterface()\n\n nextsongs = mpdi.nextsong(number = 10)\n nowplaying = mpdi.currentsong()\n if nowplaying is not None:\n if os.path.isfile(os.path.join(settings.MPD_SOURCE, os.path.dirname(nowplaying[\"file\"]), \"cover.jpg\")):\n nowplaying[\"coverpath\"] = u\"/covers/{0}/cover.jpg\".format(os.path.dirname(nowplaying[\"file\"]))\n\n return dict(nextup = nextsongs, nowplaying = nowplaying, mpdurl = settings.MPD_URL)\n\n@route(\"/covers/\")\ndef covers(coverpath):\n if re.match(r\".*cover\\.jpg\", coverpath) is None:\n abort(404, \"Cover file not found\")\n\n return static_file(coverpath, root=settings.MPD_SOURCE)\n\n@route('/static/')\ndef static(filename):\n return static_file(filename, root='static')\n\n@route('/api/artists')\ndef api_artists():\n mpdi = MPDInterface()\n artists = mpdi.artists()\n for artist in artists:\n artist[\"albums\"] = mpdi.albums(artist[\"artist\"])\n\n response.content_type = \"application/json\"\n return json.dumps(artists)\n\n@route('/api/songs//')\ndef api_songs(artist, album):\n mpdi = MPDInterface()\n songs = mpdi.songs(unicode(artist, \"utf-8\"), unicode(album, \"utf-8\"))\n\n response.content_type = \"application/json\"\n return json.dumps(songs)\n\n@route(\"/api/queue/\")\ndef api_queue_add(id):\n mpdi = MPDInterface()\n mpdi.add_to_queue(\"WebUser\", id)\n\n@route(\"/api/queue\")\ndef api_queue():\n mpdi = MPDInterface()\n queue = mpdi.get_queue()\n\n response.content_type = \"application/json\"\n return json.dumps(queue)\n\n@route(\"/api/queue//\")\ndef api_queue_album(artist, album):\n mpdi = MPDInterface()\n mpdi.add_album_to_queue(\"WebUser\", unicode(artist, \"utf-8\"), unicode(album, \"utf-8\"))\n\n@route(\"/api/upcoming/\")\ndef api_upcoming(numsongs):\n mpdi = MPDInterface()\n nextsongs = mpdi.nextsong(number = numsongs)\n\n response.content_type = \"application/json\"\n return json.dumps(nextsongs)\n \n@route(\"/api/currentsong\")\ndef api_currentsong():\n mpdi = MPDInterface()\n nowplaying = mpdi.currentsong()\n if nowplaying is not None:\n if os.path.isfile(os.path.join(settings.MPD_SOURCE, os.path.dirname(nowplaying[\"file\"]), \"cover.jpg\")):\n nowplaying[\"coverpath\"] = u\"/covers/{0}/cover.jpg\".format(os.path.dirname(nowplaying[\"file\"]))\n\n return nowplaying\n\ndef _check_secret(secret):\n if settings.SECRET == secret:\n return\n raise Exception(\"Secret is not correct, preventing load!\")\n\n@route(\"/initialize/\")\ndef initialize_db(secret):\n _check_secret(secret)\n\n mpdi = MPDInterface()\n mpdi.initialize_db()\n\n return \"Done!\"\n\n@route(\"/loadlinks/\")\ndef initialize_db(secret):\n _check_secret(secret)\n\n mpdi = MPDInterface()\n mpdi.load_links()\n\n return \"Done!\"\n\nclass MPDThread:\n quit = False\n\n def thread_listener(self):\n mpdi = MPDInterface()\n while not self.quit:\n mpdi.listen_for_events()\n\n def handle_controlc(self, signal, frame):\n self.quit = True\n sys.exit(0)\n\nif __name__ == \"__main__\":\n mpdthread = MPDThread()\n signal.signal(signal.SIGINT, mpdthread.handle_controlc)\n interface_thread = threading.Thread(target=mpdthread.thread_listener)\n interface_thread.start()\n run(host='0.0.0.0', port=8080)\n", "sub_path": "metalbot/frontend.py", "file_name": "frontend.py", "file_ext": "py", "file_size_in_byte": 3732, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "utils.MPDInterface", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "settings.MPD_SOURCE", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "settings.MPD_URL", "line_number": 22, "usage_type": "attribute"}, {"api_name": "bottle.route", "line_number": 11, "usage_type": "call"}, {"api_name": "bottle.jinja2_view", "line_number": 12, "usage_type": "call"}, {"api_name": "re.match", "line_number": 26, "usage_type": "call"}, {"api_name": "bottle.abort", "line_number": 27, "usage_type": "call"}, {"api_name": "bottle.static_file", "line_number": 29, "usage_type": "call"}, {"api_name": "settings.MPD_SOURCE", "line_number": 29, "usage_type": "attribute"}, {"api_name": "bottle.route", "line_number": 24, "usage_type": "call"}, {"api_name": "bottle.static_file", "line_number": 33, "usage_type": "call"}, {"api_name": "bottle.route", "line_number": 31, "usage_type": "call"}, {"api_name": "utils.MPDInterface", "line_number": 37, "usage_type": "call"}, {"api_name": "bottle.response.content_type", "line_number": 42, "usage_type": "attribute"}, {"api_name": "bottle.response", "line_number": 42, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 43, "usage_type": "call"}, {"api_name": "bottle.route", "line_number": 35, "usage_type": "call"}, {"api_name": "utils.MPDInterface", "line_number": 47, "usage_type": "call"}, {"api_name": "bottle.response.content_type", "line_number": 50, "usage_type": "attribute"}, {"api_name": "bottle.response", "line_number": 50, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 51, "usage_type": "call"}, {"api_name": "bottle.route", "line_number": 45, "usage_type": "call"}, {"api_name": "utils.MPDInterface", "line_number": 55, "usage_type": "call"}, {"api_name": "bottle.route", "line_number": 53, "usage_type": "call"}, {"api_name": "utils.MPDInterface", "line_number": 60, "usage_type": "call"}, {"api_name": "bottle.response.content_type", "line_number": 63, "usage_type": "attribute"}, {"api_name": "bottle.response", "line_number": 63, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 64, "usage_type": "call"}, {"api_name": "bottle.route", "line_number": 58, "usage_type": "call"}, {"api_name": "utils.MPDInterface", "line_number": 68, "usage_type": "call"}, {"api_name": "bottle.route", "line_number": 66, "usage_type": "call"}, {"api_name": "utils.MPDInterface", "line_number": 73, "usage_type": "call"}, {"api_name": "bottle.response.content_type", "line_number": 76, "usage_type": "attribute"}, {"api_name": "bottle.response", "line_number": 76, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 77, "usage_type": "call"}, {"api_name": "bottle.route", "line_number": 71, "usage_type": "call"}, {"api_name": "utils.MPDInterface", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 84, "usage_type": "call"}, {"api_name": "settings.MPD_SOURCE", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path", "line_number": 85, "usage_type": "attribute"}, {"api_name": "bottle.route", "line_number": 79, "usage_type": "call"}, {"api_name": "settings.SECRET", "line_number": 90, "usage_type": "attribute"}, {"api_name": "utils.MPDInterface", "line_number": 98, "usage_type": "call"}, {"api_name": "bottle.route", "line_number": 94, "usage_type": "call"}, {"api_name": "utils.MPDInterface", "line_number": 107, "usage_type": "call"}, {"api_name": "bottle.route", "line_number": 103, "usage_type": "call"}, {"api_name": "utils.MPDInterface", "line_number": 116, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 122, "usage_type": "call"}, {"api_name": "signal.signal", "line_number": 126, "usage_type": "call"}, {"api_name": "signal.SIGINT", "line_number": 126, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 127, "usage_type": "call"}, {"api_name": "bottle.run", "line_number": 129, "usage_type": "call"}]} +{"seq_id": "456549808", "text": "import pytest\nfrom conftest import (\n nx,\n ArangoDB_Networkx_Adapter,\n Base_ADBNX_Controller,\n get_grid_graph,\n get_football_graph,\n get_karate_graph,\n adbnx_adapter,\n imdb_adbnx_adapter,\n grid_adbnx_adapter,\n football_adbnx_adapter,\n karate_adbnx_adapter,\n)\n\nfrom arango.graph import Graph as ArangoGraph\nfrom networkx.classes.graph import Graph as NxGraph\n\n\n@pytest.mark.unit\n@pytest.mark.parametrize(\n \"bad_connection\",\n [\n {\n \"dbName\": \"_system\",\n \"hostname\": \"localhost\",\n \"protocol\": \"http\",\n \"port\": 8529,\n # \"username\": \"root\",\n # \"password\": \"password\",\n }\n ],\n)\ndef test_validate_attributes(bad_connection):\n with pytest.raises(ValueError):\n ArangoDB_Networkx_Adapter(bad_connection)\n\n\n@pytest.mark.unit\n@pytest.mark.parametrize(\n \"adapter, name, attributes\",\n [\n (\n adbnx_adapter,\n \"fraud-detection\",\n {\n \"vertexCollections\": {\n \"account\": {\"Balance\", \"account_type\", \"customer_id\", \"rank\"},\n \"bank\": {\"Country\", \"Id\", \"bank_id\", \"bank_name\"},\n \"branch\": {\n \"City\",\n \"Country\",\n \"Id\",\n \"bank_id\",\n \"branch_id\",\n \"branch_name\",\n },\n \"Class\": {\"concrete\", \"label\", \"name\"},\n \"customer\": {\"Name\", \"Sex\", \"Ssn\", \"rank\"},\n },\n \"edgeCollections\": {\n \"accountHolder\": {\"_from\", \"_to\"},\n \"Relationship\": {\n \"_from\",\n \"_to\",\n \"label\",\n \"name\",\n \"relationshipType\",\n },\n \"transaction\": {\"_from\", \"_to\"},\n },\n },\n ),\n (\n imdb_adbnx_adapter,\n \"IMDBGraph\",\n {\n \"vertexCollections\": {\"Users\": {}, \"Movies\": {}},\n \"edgeCollections\": {\"Ratings\": {\"_from\", \"_to\", \"ratings\"}},\n },\n ),\n ],\n)\ndef test_create_networkx_graph(\n adapter: ArangoDB_Networkx_Adapter, name: str, attributes: dict\n):\n assert_adapter_type(adapter)\n nx_g = adapter.create_networkx_graph(name, attributes)\n assert_networkx_data(\n nx_g,\n attributes[\"vertexCollections\"],\n attributes[\"edgeCollections\"],\n )\n\n\n@pytest.mark.unit\n@pytest.mark.parametrize(\n \"adapter, name, vcols, ecols\",\n [\n (\n adbnx_adapter,\n \"fraud-detection\",\n {\"account\", \"bank\", \"branch\", \"Class\", \"customer\"},\n {\"accountHolder\", \"Relationship\", \"transaction\"},\n )\n ],\n)\ndef test_create_networkx_graph_from_arangodb_collections(\n adapter: ArangoDB_Networkx_Adapter, name: str, vcols: set, ecols: set\n):\n assert_adapter_type(adapter)\n nx_g = adapter.create_networkx_graph_from_arangodb_collections(\n name,\n vcols,\n ecols,\n )\n assert_networkx_data(nx_g, vcols, ecols)\n\n\n@pytest.mark.unit\n@pytest.mark.parametrize(\n \"adapter, name, edge_definitions\",\n [(adbnx_adapter, \"fraud-detection\", None)],\n)\ndef test_create_networkx_graph_from_arangodb_graph(\n adapter: ArangoDB_Networkx_Adapter, name: str, edge_definitions\n):\n assert_adapter_type(adapter)\n\n # Re-create the graph if defintions are provided\n if edge_definitions:\n adapter.db.delete_graph(name, ignore_missing=True)\n adapter.db.create_graph(name, edge_definitions=edge_definitions)\n\n arango_graph = adapter.db.graph(name)\n v_cols = arango_graph.vertex_collections()\n e_cols = {col[\"edge_collection\"] for col in arango_graph.edge_definitions()}\n\n nx_g = adbnx_adapter.create_networkx_graph_from_arangodb_graph(name)\n assert_networkx_data(nx_g, v_cols, e_cols)\n\n\n@pytest.mark.unit\n@pytest.mark.parametrize(\n \"adapter, name, nx_g, edge_definitions\",\n [\n (\n grid_adbnx_adapter,\n \"Grid\",\n get_grid_graph(),\n [\n {\n \"edge_collection\": \"to\",\n \"from_vertex_collections\": [\"Grid_Node\"],\n \"to_vertex_collections\": [\"Grid_Node\"],\n }\n ],\n ),\n (\n football_adbnx_adapter,\n \"Football\",\n get_football_graph(),\n [\n {\n \"edge_collection\": \"played\",\n \"from_vertex_collections\": [\"Football_Team\"],\n \"to_vertex_collections\": [\"Football_Team\"],\n }\n ],\n ),\n (\n karate_adbnx_adapter,\n \"Karate\",\n get_karate_graph(),\n [\n {\n \"edge_collection\": \"knows\",\n \"from_vertex_collections\": [\"Karate_Student\"],\n \"to_vertex_collections\": [\"Karate_Student\"],\n }\n ],\n ),\n ],\n)\ndef test_create_arangodb_graph(\n adapter: ArangoDB_Networkx_Adapter,\n name: str,\n nx_g: NxGraph,\n edge_definitions: list,\n):\n assert_adapter_type(adapter)\n adb_g = adapter.create_arangodb_graph(name, nx_g, edge_definitions)\n assert_arangodb_data(adapter, nx_g, adb_g)\n\n\n@pytest.mark.unit\ndef test_full_cycle_from_arangodb():\n name = \"fraud-detection\"\n original_fraud_adb_g = adbnx_adapter.db.graph(name)\n fraud_nx_g = adbnx_adapter.create_networkx_graph_from_arangodb_graph(name)\n\n edge_definitions = [\n {\n \"edge_collection\": \"accountHolder_nx\",\n \"from_vertex_collections\": [\"customer_nx\"],\n \"to_vertex_collections\": [\"account_nx\"],\n },\n {\n \"edge_collection\": \"transaction_nx\",\n \"from_vertex_collections\": [\"account_nx\"],\n \"to_vertex_collections\": [\"account_nx\"],\n },\n ]\n\n new_name = name + \"-nx\"\n new_fraud_adb_g = adbnx_adapter.create_arangodb_graph(\n new_name, fraud_nx_g, edge_definitions, keyify_edges=True\n )\n\n col: str\n for col in original_fraud_adb_g.vertex_collections():\n new_col = col + \"_nx\"\n for vertex in original_fraud_adb_g.vertex_collection(col):\n assert new_fraud_adb_g.vertex_collection(new_col).has(vertex[\"_key\"])\n\n e_cols = {col[\"edge_collection\"] for col in original_fraud_adb_g.edge_definitions()}\n for col in e_cols:\n new_col = col + \"_nx\"\n for edge in original_fraud_adb_g.edge_collection(col):\n assert new_fraud_adb_g.edge_collection(new_col).has(edge[\"_key\"])\n\n\n@pytest.mark.unit\ndef test_full_cycle_from_arangodb_with_overwrite():\n name = \"fraud-detection\"\n original_fraud_adb_g = adbnx_adapter.db.graph(name)\n edge_definitions = original_fraud_adb_g.edge_definitions()\n\n col: str\n original_doc_count = dict()\n for col in original_fraud_adb_g.vertex_collections():\n original_doc_count[col] = original_fraud_adb_g.vertex_collection(col).count()\n\n e_cols = {col[\"edge_collection\"] for col in original_fraud_adb_g.edge_definitions()}\n for col in e_cols:\n original_doc_count[col] = original_fraud_adb_g.edge_collection(col).count()\n\n fraud_nx_g = adbnx_adapter.create_networkx_graph_from_arangodb_graph(name)\n\n for _, node in fraud_nx_g.nodes(data=True):\n node[\"new_vertex_data\"] = [\"new\", \"vertex\", \"data\", \"here\"]\n\n for _, _, edge in fraud_nx_g.edges(data=True):\n edge[\"new_edge_data\"] = [\"new\", \"edge\", \"data\", \"here\"]\n\n updated_fraud_adb_g = adbnx_adapter.create_arangodb_graph(\n name, fraud_nx_g, edge_definitions, overwrite=True, keyify_edges=True\n )\n\n for col in updated_fraud_adb_g.vertex_collections():\n new_doc_count = updated_fraud_adb_g.vertex_collection(col).count()\n assert original_doc_count[col] == new_doc_count\n for vertex in updated_fraud_adb_g.vertex_collection(col):\n assert \"new_vertex_data\" in vertex\n\n e_cols = {col[\"edge_collection\"] for col in updated_fraud_adb_g.edge_definitions()}\n for col in e_cols:\n new_doc_count = updated_fraud_adb_g.edge_collection(col).count()\n assert original_doc_count[col] == new_doc_count\n for edge in updated_fraud_adb_g.edge_collection(col):\n assert \"new_edge_data\" in edge\n\n\n@pytest.mark.unit\ndef test_full_cycle_from_networkx():\n name = \"Grid\"\n if grid_adbnx_adapter.db.has_graph(name):\n grid_adbnx_adapter.db.delete_graph(name, drop_collections=True)\n\n original_grid_nx_g = nx.grid_2d_graph(5, 5)\n grid_edge_definitions = [\n {\n \"edge_collection\": \"to\",\n \"from_vertex_collections\": [\"Grid_Node\"],\n \"to_vertex_collections\": [\"Grid_Node\"],\n }\n ]\n\n grid_adbnx_adapter.create_arangodb_graph(\n name, original_grid_nx_g, grid_edge_definitions\n )\n\n new_grid_nx_g = grid_adbnx_adapter.create_networkx_graph_from_arangodb_graph(name)\n\n for id, _ in original_grid_nx_g.nodes(data=True):\n assert new_grid_nx_g.has_node(id)\n\n for from_node, to_node, _ in original_grid_nx_g.edges(data=True):\n assert new_grid_nx_g.has_edge(from_node, to_node)\n\n\ndef assert_adapter_type(adapter: ArangoDB_Networkx_Adapter):\n assert type(adapter) is ArangoDB_Networkx_Adapter and issubclass(\n type(adapter.cntrl), Base_ADBNX_Controller\n )\n\n\ndef assert_networkx_data(nx_g: NxGraph, v_cols, e_cols):\n for col in v_cols:\n for vertex in adbnx_adapter.db.collection(col):\n assert nx_g.has_node(vertex[\"_id\"])\n\n for col in e_cols:\n for edge in adbnx_adapter.db.collection(col):\n assert nx_g.has_edge(edge[\"_from\"], edge[\"_to\"])\n\n\ndef assert_arangodb_data(\n adapter: ArangoDB_Networkx_Adapter, nx_g: NxGraph, adb_g: ArangoGraph\n):\n overwrite = False\n for id, node in nx_g.nodes(data=True):\n col = adapter.cntrl._identify_nx_node(id, node, overwrite)\n key = adapter.cntrl._keyify_nx_node(id, node, col, overwrite)\n assert adb_g.vertex_collection(col).has(key)\n\n for from_node_id, to_node_id, edge in nx_g.edges(data=True):\n from_node = {\"id\": from_node_id, **nx_g.nodes[from_node_id]}\n to_node = {\"id\": to_node_id, **nx_g.nodes[to_node_id]}\n\n col = adapter.cntrl._identify_nx_edge(edge, from_node, to_node, overwrite)\n assert adb_g.edge_collection(col).find(\n {\n \"_from\": adapter.cntrl.adb_map.get(from_node[\"id\"])[\"_id\"],\n \"_to\": adapter.cntrl.adb_map.get(to_node[\"id\"])[\"_id\"],\n }\n )\n", "sub_path": "adbnx_adapter/tests/test_adbnx_adapter.py", "file_name": "test_adbnx_adapter.py", "file_ext": "py", "file_size_in_byte": 10742, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "pytest.raises", "line_number": 35, "usage_type": "call"}, {"api_name": "conftest.ArangoDB_Networkx_Adapter", "line_number": 36, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 21, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 21, "usage_type": "attribute"}, {"api_name": "conftest.ArangoDB_Networkx_Adapter", "line_number": 85, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 40, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 40, "usage_type": "attribute"}, {"api_name": "conftest.adbnx_adapter", "line_number": 44, "usage_type": "name"}, {"api_name": "conftest.imdb_adbnx_adapter", "line_number": 75, "usage_type": "name"}, {"api_name": "conftest.ArangoDB_Networkx_Adapter", "line_number": 109, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 96, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 97, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 97, "usage_type": "attribute"}, {"api_name": "conftest.adbnx_adapter", "line_number": 101, "usage_type": "name"}, {"api_name": "conftest.ArangoDB_Networkx_Adapter", "line_number": 126, "usage_type": "name"}, {"api_name": "conftest.adbnx_adapter.create_networkx_graph_from_arangodb_graph", "line_number": 139, "usage_type": "call"}, {"api_name": "conftest.adbnx_adapter", "line_number": 139, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 120, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 121, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 121, "usage_type": "attribute"}, {"api_name": "conftest.adbnx_adapter", "line_number": 123, "usage_type": "name"}, {"api_name": "conftest.ArangoDB_Networkx_Adapter", "line_number": 186, "usage_type": "name"}, {"api_name": "networkx.classes.graph.Graph", "line_number": 188, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 143, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 144, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 144, "usage_type": "attribute"}, {"api_name": "conftest.grid_adbnx_adapter", "line_number": 148, "usage_type": "name"}, {"api_name": "conftest.get_grid_graph", "line_number": 150, "usage_type": "call"}, {"api_name": "conftest.football_adbnx_adapter", "line_number": 160, "usage_type": "name"}, {"api_name": "conftest.get_football_graph", "line_number": 162, "usage_type": "call"}, {"api_name": "conftest.karate_adbnx_adapter", "line_number": 172, "usage_type": "name"}, {"api_name": "conftest.get_karate_graph", "line_number": 174, "usage_type": "call"}, {"api_name": "conftest.adbnx_adapter.db.graph", "line_number": 199, "usage_type": "call"}, {"api_name": "conftest.adbnx_adapter.db", "line_number": 199, "usage_type": "attribute"}, {"api_name": "conftest.adbnx_adapter", "line_number": 199, "usage_type": "name"}, {"api_name": "conftest.adbnx_adapter.create_networkx_graph_from_arangodb_graph", "line_number": 200, "usage_type": "call"}, {"api_name": "conftest.adbnx_adapter", "line_number": 200, "usage_type": "name"}, {"api_name": "conftest.adbnx_adapter.create_arangodb_graph", "line_number": 216, "usage_type": "call"}, {"api_name": "conftest.adbnx_adapter", "line_number": 216, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 196, "usage_type": "attribute"}, {"api_name": "conftest.adbnx_adapter.db.graph", "line_number": 236, "usage_type": "call"}, {"api_name": "conftest.adbnx_adapter.db", "line_number": 236, "usage_type": "attribute"}, {"api_name": "conftest.adbnx_adapter", "line_number": 236, "usage_type": "name"}, {"api_name": "conftest.adbnx_adapter.create_networkx_graph_from_arangodb_graph", "line_number": 248, "usage_type": "call"}, {"api_name": "conftest.adbnx_adapter", "line_number": 248, "usage_type": "name"}, {"api_name": "conftest.adbnx_adapter.create_arangodb_graph", "line_number": 256, "usage_type": "call"}, {"api_name": "conftest.adbnx_adapter", "line_number": 256, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 233, "usage_type": "attribute"}, {"api_name": "conftest.grid_adbnx_adapter.db.has_graph", "line_number": 277, "usage_type": "call"}, {"api_name": "conftest.grid_adbnx_adapter.db", "line_number": 277, "usage_type": "attribute"}, {"api_name": "conftest.grid_adbnx_adapter", "line_number": 277, "usage_type": "name"}, {"api_name": "conftest.grid_adbnx_adapter.db.delete_graph", "line_number": 278, "usage_type": "call"}, {"api_name": "conftest.grid_adbnx_adapter.db", "line_number": 278, "usage_type": "attribute"}, {"api_name": "conftest.grid_adbnx_adapter", "line_number": 278, "usage_type": "name"}, {"api_name": "conftest.nx.grid_2d_graph", "line_number": 280, "usage_type": "call"}, {"api_name": "conftest.nx", "line_number": 280, "usage_type": "name"}, {"api_name": "conftest.grid_adbnx_adapter.create_arangodb_graph", "line_number": 289, "usage_type": "call"}, {"api_name": "conftest.grid_adbnx_adapter", "line_number": 289, "usage_type": "name"}, {"api_name": "conftest.grid_adbnx_adapter.create_networkx_graph_from_arangodb_graph", "line_number": 293, "usage_type": "call"}, {"api_name": "conftest.grid_adbnx_adapter", "line_number": 293, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 274, "usage_type": "attribute"}, {"api_name": "conftest.ArangoDB_Networkx_Adapter", "line_number": 302, "usage_type": "name"}, {"api_name": "conftest.ArangoDB_Networkx_Adapter", "line_number": 303, "usage_type": "name"}, {"api_name": "conftest.Base_ADBNX_Controller", "line_number": 304, "usage_type": "argument"}, {"api_name": "networkx.classes.graph.Graph", "line_number": 308, "usage_type": "name"}, {"api_name": "conftest.adbnx_adapter.db.collection", "line_number": 310, "usage_type": "call"}, {"api_name": "conftest.adbnx_adapter.db", "line_number": 310, "usage_type": "attribute"}, {"api_name": "conftest.adbnx_adapter", "line_number": 310, "usage_type": "name"}, {"api_name": "conftest.adbnx_adapter.db.collection", "line_number": 314, "usage_type": "call"}, {"api_name": "conftest.adbnx_adapter.db", "line_number": 314, "usage_type": "attribute"}, {"api_name": "conftest.adbnx_adapter", "line_number": 314, "usage_type": "name"}, {"api_name": "conftest.ArangoDB_Networkx_Adapter", "line_number": 319, "usage_type": "name"}, {"api_name": "networkx.classes.graph.Graph", "line_number": 319, "usage_type": "name"}, {"api_name": "arango.graph.Graph", "line_number": 319, "usage_type": "name"}]} +{"seq_id": "451373167", "text": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\n\nimport os\nimport sys\nimport glob\nimport numpy as np\nimport pandas as pd\nimport json\nimport argparse\nfrom collections import defaultdict\nimport shutil\n\ncurrent_dir = os.path.dirname(__file__)\n\nsys.path.append(os.path.join(current_dir, '.'))\nsys.path.append(os.path.join(current_dir, '..', '..', 'src'))\n\n\nfrom path_tools import *\nfrom process_common import *\n\n\n\ndef read_meta(file_in, cell_id_row_idx=3, cell_type_row_idx=4, sep='\\t'):\n cell_type_row, cell_id_row = '', ''\n with open(file_in, 'r') as reader:\n for idx, line in enumerate(reader, start=1):\n if idx == cell_type_row_idx:\n cell_type_row = line.split('Cell_type')[1].strip().split(sep)\n if idx == cell_id_row_idx:\n cell_id_row = line.split('Cell_ID')[1].strip().split(sep)\n if cell_id_row and cell_type_row:\n break\n columns=['ID', 'Cluster']\n arr = np.array([cell_id_row, cell_type_row]).T\n df = pd.DataFrame(arr, columns=columns)\n return df\n\n\ndef main(args):\n CHECK_EXIST(args.input, 'f')\n \n MAKE_EXIST(args.output, 'd')\n\n output_path = os.path.join(args.output, 'cell_meta.txt')\n\n df_meta = read_meta(args.input)\n ### df_meta = parse_meta(args.input, cell_id_row_idx=3, cell_type_row_idx=4, sep=',')\n # print(df_meta.head())\n # print(df_meta.describe())\n write_csv(df_meta, output_path, index=False)\n\n print('All done.')\n \n\ndef parse_arguments(argv):\n parser = argparse.ArgumentParser()\n parser.add_argument('--input', type=str, required=True, \n help='Input cell annotation file.')\n parser.add_argument('-o', '--output', type=str, default='./output', \n help='Directory to save cell meta file.')\n return parser.parse_args(argv)\n\n\nif __name__ == '__main__':\n main(parse_arguments(sys.argv[1:]))\n", "sub_path": "scripts/MouseMidbrain_SingleCell_Manno2016/create_cell_meta.py", "file_name": "create_cell_meta.py", "file_ext": "py", "file_size_in_byte": 1982, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "os.path.dirname", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 20, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 21, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 40, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 62, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 71, "usage_type": "attribute"}]} +{"seq_id": "448426752", "text": "from contextlib import contextmanager\n\n@contextmanager\ndef keep_plots_open(keep_show_open_on_exit=True, even_when_error=True):\n '''\n To continue executing code when plt.show() is called\n and keep the plot on displaying before this context manager exits\n (even if an error caused the exit).\n '''\n import matplotlib.pyplot\n show_original = matplotlib.pyplot.show\n\n def show_replacement(*args, **kwargs):\n kwargs['block'] = False\n show_original(*args, **kwargs)\n\n matplotlib.pyplot.show = show_replacement\n\n pylab_exists = True\n\n try:\n import pylab\n except ImportError:\n pylab_exists = False\n if pylab_exists:\n pylab.show = show_replacement\n\n try:\n yield\n except Exception as err:\n if keep_show_open_on_exit and even_when_error:\n print(\"*********************************************\")\n print(\"Error early edition while waiting for show():\")\n print(\"*********************************************\")\n import traceback\n print(traceback.format_exc())\n show_original()\n print(\"*********************************************\")\n raise\n finally:\n matplotlib.pyplot.show = show_original\n if pylab_exists:\n pylab.show = show_original\n\n if keep_show_open_on_exit:\n show_original()\n\n# ***********************\n# Running example\n# ***********************\nimport pylab as pl\nimport time\nif __name__ == '__main__':\n with keep_plots_open():\n pl.figure('a')\n pl.plot([1,2,3], [4,5,6])\n pl.plot([3,2,1], [4,5,6])\n pl.show()\n\n pl.figure('b')\n pl.plot([1,2,3], [4,5,6])\n pl.show()\n\n time.sleep(1)\n print('...')\n time.sleep(1)\n print('...')\n time.sleep(1)\n print('...')\n\n this_will_surely_cause_an_error\n", "sub_path": "trying_plotstuff.py", "file_name": "trying_plotstuff.py", "file_ext": "py", "file_size_in_byte": 1898, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "matplotlib.pyplot.pyplot", "line_number": 11, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 17, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "pylab.show", "line_number": 26, "usage_type": "attribute"}, {"api_name": "traceback.format_exc", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 41, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "pylab.show", "line_number": 43, "usage_type": "attribute"}, {"api_name": "contextlib.contextmanager", "line_number": 3, "usage_type": "name"}, {"api_name": "pylab.figure", "line_number": 55, "usage_type": "call"}, {"api_name": "pylab.plot", "line_number": 56, "usage_type": "call"}, {"api_name": "pylab.plot", "line_number": 57, "usage_type": "call"}, {"api_name": "pylab.show", "line_number": 58, "usage_type": "call"}, {"api_name": "pylab.figure", "line_number": 60, "usage_type": "call"}, {"api_name": "pylab.plot", "line_number": 61, "usage_type": "call"}, {"api_name": "pylab.show", "line_number": 62, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 64, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 66, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 68, "usage_type": "call"}]} +{"seq_id": "364427482", "text": "import gym\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport random\n\nclass TrainingAgent:\n\n\tdef __init__(self, learning_rate, epsilon, epsilon_min, epsilon_decay, moves, reward_decay):\n\t\tself.env = gym.make(\"Taxi-v3\")\n\t\tself.learning_rate = learning_rate\n\t\tself.epsilon = epsilon\n\t\tself.epsilon_min = epsilon_min\n\t\tself.epsilon_decay = epsilon_decay\n\t\tself.moves = moves\n\t\tself.reward_decay = reward_decay\n\t\tself.Q = np.zeros((self.env.observation_space.n, self.env.action_space.n))\n\t\n\tdef train(self):\n\t\tstate = self.env.reset()\n\t\tscore = 0.0\n\n\t\tfor _ in range(self.moves):\n\n\t\t\tif random.uniform(0, 1) > self.epsilon:\n\t\t\t\taction = np.argmax(self.Q[state])\n\t\t\telse:\n\t\t\t\taction = self.env.action_space.sample()\n\t\t\t\n\t\t\tnext_state, reward, done, info = self.env.step(action)\n\t\t\tQ_max = np.max(self.Q[next_state])\n\n\t\t\tscore += reward\n\n\t\t\tself.Q[state][action] = (1 - self.learning_rate) * self.Q[state][action] + \\\n\t\t\t\t\t\t\t\t\tself.learning_rate * (reward + self.reward_decay * Q_max)\n\t\t\t\n\t\t\tif done:\n\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\tstate = next_state\n\t\t\n\n\t\tif self.epsilon >= self.epsilon_min:\n\t\t\tself.epsilon *= self.epsilon_decay\n\t\t\n\t\treturn score\n\n\tdef test(self):\n\t\tscore = 0.0\n\t\tstate = self.env.reset()\n\n\t\tfor _ in range(self.moves):\n\t\t\taction = np.argmax(self.Q[state])\n\n\t\t\tnext_state, reward, done, info = self.env.step(action)\n\t\t\t\n\t\t\tscore += reward\n\n\t\t\tif done:\n\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\tstate = next_state\n\n\t\treturn score", "sub_path": "Vestacka inteligencija/Neural Networks/RL/Q_table_taxi/training_agent.py", "file_name": "training_agent.py", "file_ext": "py", "file_size_in_byte": 1422, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "gym.make", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 16, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "500760835", "text": "\"\"\"Print the name of all pods in the cluster.\"\"\"\nimport asyncio\n\nimport aiokubernetes as k8s\n\n\nasync def main():\n # Create a client instance and load the credentials from ~/.kube/kubeconfig\n api_client = k8s.config.new_client_from_config()\n\n # Ask for all Pods.\n v1 = k8s.api.CoreV1Api(api_client)\n ret = await v1.list_pod_for_all_namespaces()\n\n # Ensure the API call to Kubernetes succeeded.\n assert ret.http.status == 200\n\n # Print the pod names.\n for i in ret.obj.items:\n print(f\"{i.metadata.namespace} {i.metadata.name}\")\n\n # Close all pending connections.\n await api_client.close()\n\n\nif __name__ == '__main__':\n loop = asyncio.get_event_loop()\n loop.run_until_complete(main())\n loop.close()\n", "sub_path": "examples/list_pods.py", "file_name": "list_pods.py", "file_ext": "py", "file_size_in_byte": 745, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "aiokubernetes.config.new_client_from_config", "line_number": 9, "usage_type": "call"}, {"api_name": "aiokubernetes.config", "line_number": 9, "usage_type": "attribute"}, {"api_name": "aiokubernetes.api.CoreV1Api", "line_number": 12, "usage_type": "call"}, {"api_name": "aiokubernetes.api", "line_number": 12, "usage_type": "attribute"}, {"api_name": "asyncio.get_event_loop", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "91448233", "text": "\"\"\"Script to calculate the % variance explained from the PCA models\"\"\"\n\nimport pandas as pd\nfrom joblib import load\nfrom pathlib import Path\n\nPROJECT_ROOT = Path.cwd()\n\n\ndef main():\n pca_path = PROJECT_ROOT / 'outputs' / 'pca' / 'models'\n\n # Get list of file names for pca models\n n_repetitions = 10\n n_folds = 10\n\n pca_name_ls = []\n for i_repetition in range(n_repetitions):\n for i_fold in range(n_folds):\n pca_name = f'{i_repetition:02d}_{i_fold:02d}_pca.joblib'\n pca_name_ls.append(pca_name)\n\n # Loop over pca model file names, load models and get variance explained\n pca_var_ls = []\n for i_model in pca_name_ls:\n print(i_model)\n pca_model = load(pca_path / i_model)\n var_explained = pca_model.explained_variance_ratio_.sum()\n pca_var_ls.append(var_explained)\n\n # Create df for % variance explained per model iteration\n pca_var_df = pd.DataFrame({'variance_explained':pca_var_ls})\n\n # Get mean and standard deviation for % variance explained across iterations\n var_mean = pca_var_df['variance_explained'].mean()\n var_std = pca_var_df['variance_explained'].stdev()\n print(var_mean, var_std)\n\n # Save % variance explained per model\n file_name = 'pca_variance_explained.csv'\n file_path = PROJECT_ROOT / 'outputs' / 'pca'\n pca_var_df.to_csv(file_path / file_name)\n\n\n\nif __name__ == '__main__':\n main()", "sub_path": "src/preprocessing/compute_pca_variance_explained.py", "file_name": "compute_pca_variance_explained.py", "file_ext": "py", "file_size_in_byte": 1417, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "pathlib.Path.cwd", "line_number": 7, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 7, "usage_type": "name"}, {"api_name": "joblib.load", "line_number": 27, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "576065677", "text": "import pygame\nimport math\nimport platform\nimport random\nfrom math import *\nfrom pygame.locals import *\n# from functions.values import screenborder\nfrom functions.values import *\n# from functions.values import damagetype\n\nclass MP(object):\n \"\"\"\n specify ribbon mana value\n \"\"\"\n _base_mp = 10000\n def __init__(self, ribbon):\n self.ribbon = ribbon\n self.mp = 10000\n self.max_mp = 10000\n self.base_mp = 10000\n\n def __setattr__(self, name, value):\n value = value.__int__()\n if name in 'max_mp':\n if self.mp > self.max_mp:\n self.mp = self.max_mp\n return super().__setattr__(name, value)\n\n def add(self, value):\n self.mp += value\n\n def sub(self, value):\n self.mp -= value\n\n def full(self):\n self.mp = self.max_mp\n\n def empty(self):\n self.mp = 0\n\n def boost(self, max_boost=False):\n h = self.max_mp//2\n if self.mp > h:\n self.mp -= h\n elif self.mp == self.max_mp:\n if self.max_boost:\n self.empty()\n else:\n self.mp -= h\n\n def __repr__(self):\n return \"< ribbon MP: %d/%d >\" % (self.mp, self.max_mp)\n\n def __str__(self):\n return \"MP: %d\\nMaxMP: %d\\nBaseMP: %d\" % (self.mp, self.max_mp, self.base_mp)\n\nclass green_danmaku(pygame.sprite.Sprite):\n def __init__(self, me_ribbon):\n pygame.sprite.Sprite.__init__(self)\n if platform.system() == 'Linux' or platform.system()=='Darwin':\n oimage = pygame.image.load(\"images/character/Ribbon/ribbon_green.png\").convert_alpha()\n if platform.system() == 'Windows':\n oimage = pygame.image.load(\"images\\\\character\\\\Ribbon\\\\ribbon_green.png\").convert_alpha()\n self.image = oimage\n self.rect = self.image.get_rect()\n \n self.radius = 10\n self.damage = 10+random.randint(0,1)\n \n self.center = [me_ribbon.center[0], me_ribbon.center[1]]\n self.direction = [0,-1]\n self.rect.left = self.center[0] - 10\n self.rect.top = self.center[1] - 10\n self.delete = False\n \n self.speed = 8 + random.random()*2\n\n self._type = damagetype.MAGIC\n \n def move(self, *erina):\n self.center[0] += self.speed * self.direction[0]\n self.center[1] += self.speed * self.direction[1]\n self.rect.top = self.center[0] - 10\n self.rect.left = self.center[1] - 10\n if self.rect.top < BATTLE_SCREEN_TOP or \\\n self.rect.left < BATTLE_SCREEN_LEFT or \\\n self.rect.right > BATTLE_SCREEN_RIGHT or \\\n self.rect.bottom > BATTLE_SCREEN_BOTTOM:\n self.delete = True\n\n def print_screen(self, screen):\n screen.blit(self.image, self.rect)\n\nclass purple_danmaku(pygame.sprite.Sprite):\n def __init__(self, me_ribbon):\n pygame.sprite.Sprite.__init__(self)\n if platform.system() == 'Linux' or platform.system()=='Darwin':\n oimage = pygame.image.load(\"images/character/Ribbon/ribbon_purple.png\").convert_alpha()\n if platform.system() == 'Windows':\n oimage = pygame.image.load(\"images\\\\character\\\\Ribbon\\\\ribbon_purple.png\").convert_alpha()\n self.image = oimage\n self.rect = self.image.get_rect()\n \n self.radius = 5\n self.damage = 10\n \n self.center = [me_ribbon.center[0], me_ribbon.center[1]]\n self.direction = [0,-1]\n self.rect.left = self.center[0] - 5\n self.rect.top = self.center[1] - 5\n self.delete = False\n \n self.speed = 10\n\n self._type = damagetype.MAGIC\n \n def move(self, *erina):\n self.center[0] += self.speed * self.direction[0]\n self.center[1] += self.speed * self.direction[1]\n self.rect.top = self.center[0] - 5\n self.rect.left = self.center[1] - 5\n if self.rect.top < BATTLE_SCREEN_TOP or \\\n self.rect.left < BATTLE_SCREEN_LEFT or \\\n self.rect.right > BATTLE_SCREEN_RIGHT or \\\n self.rect.bottom > BATTLE_SCREEN_BOTTOM:\n self.delete = True\n\n def print_screen(self, screen):\n screen.blit(self.image, self.rect)\n\nclass Ribbon(pygame.sprite.Sprite):\n def __init__(self):\n pygame.sprite.Sprite.__init__(self)\n if platform.system()=='Windows':\n oimage = pygame.image.load(\"images\\\\character\\\\Ribbon\\\\Ribbon.png\").convert_alpha()\n if platform.system()=='Linux' or platform.system()=='Darwin':\n oimage = pygame.image.load(\"images/character/Ribbon/Ribbon.png\").convert_alpha()\n self.image = pygame.transform.scale(oimage, (16,20))\n self.rect = self.image.get_rect()\n self.center = [225, 370]\n self.rect.left = self.center[0] - 8\n self.rect.top = self.center[1] - 10\n \n self.energy = 500\n self.max_energy = 1000\n self.radius = 1\n self.shouting_delay = 0\n \n self.danmaku_mode = [\"red\", \"yellow\", \"blue\", \"green\", \"purple\", \"carrot\", \"egg\"]\n self.danmaku_type = \"green\"\n \n def move(self, master):\n destination = [master.center[1] - 30, master.center[0]]\n distance = math.sqrt( \\\n (destination[0] - self.center[0]) ** 2 + \\\n (destination[1] - self.center[1]) ** 2 )\n direction = [ \\\n (destination[0] - self.center[0]) / distance, \\\n (destination[1] - self.center[1]) / distance]\n\n speed = distance ** 2 / 500.0\n \"\"\"\n if distance > 50:\n speed = int(exp(distance))\n else:\n speed = int(sqrt(sqrt(distance)))\n\"\"\"\n self.center[0] += speed * direction[0]\n self.center[1] += speed * direction[1]\n\n self.rect.top = int(self.center[0] - 8)\n self.rect.left = int(self.center[1] - 10)\n \n def purple_attack(self, shouting_group, key_pressed):\n if not self.shouting_delay:\n key = key_pressed\n try:\n if key[K_z]:\n if not self.shouting_delay:\n for i in range(-10,11):\n temp = purple_danmaku(self)\n temp.direction = [\\\n cos((314 + 10*i) / 100), \\\n sin((314 + 10*i) / 100)]\n shouting_group.add(temp)\n self.shouting_delay += 1\n except:\n self.shouting_delay += 1\n if self.shouting_delay > 15:\n self.shouting_delay = 0\n\n def red_attack(self, shouting_group):\n pass\n \n def blue_attack(self, shouting_group):\n pass\n \n def green_attack(self, shouting_group, key_pressed):\n if not self.shouting_delay:\n key = key_pressed\n try:\n if key[K_z]:\n if not self.shouting_delay:\n for i in range(-3,4):\n temp = green_danmaku(self)\n temp.direction = [\\\n cos((314 + 5*i + random.random()*5) / 100), \\\n sin((314 + 5*i + random.random()*5) / 100)]\n shouting_group.add(temp)\n self.shouting_delay += 1\n except:\n self.shouting_delay += 1\n if self.shouting_delay > 10:\n self.shouting_delay = 0\n\n def yellow_attack(self, shouting_group):\n pass\n \n def carrot_attack(self, shouting_group):\n pass\n\n def attack(self, shouting_group, key_pressed):\n self.__getattribute__(self.danmaku_type + '_attack')(shouting_group, key_pressed)\n \"\"\"\n if self.danmaku_type == \"purple\":\n self.purple_attack(shouting_group, key_pressed)\n elif self.danmaku_type == \"red\":\n self.red_attack(shouting_group)\n elif self.danmaku_type == \"blue\":\n self.blue_attack(shouting_group)\n elif self.danmaku_type == \"green\":\n self.green_attack(shouting_group)\n elif self.danmaku_type == \"yellow\":\n self.yellow_attack(shouting_group)\n elif self.danmaku_type == \"carrot\":\n self.carrot_attack(shouting_group)\n \"\"\"\n\nclass energy(pygame.sprite.Sprite):\n def __init__(self, boss):\n pass\n \n def boost_attract(energy_group, me_ribbon):\n pass\n", "sub_path": "rabiribi-danmaku/character/ribbon.py", "file_name": "ribbon.py", "file_ext": "py", "file_size_in_byte": 8439, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pygame.sprite", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Sprite.__init__", "line_number": 59, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 59, "usage_type": "attribute"}, {"api_name": "platform.system", "line_number": 60, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 61, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 61, "usage_type": "attribute"}, {"api_name": "platform.system", "line_number": 62, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 63, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 63, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 68, "usage_type": "call"}, {"api_name": "random.random", "line_number": 76, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 94, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Sprite.__init__", "line_number": 96, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 96, "usage_type": "attribute"}, {"api_name": "platform.system", "line_number": 97, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 98, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 98, "usage_type": "attribute"}, {"api_name": "platform.system", "line_number": 99, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 100, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 100, "usage_type": "attribute"}, {"api_name": "pygame.sprite", "line_number": 131, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Sprite.__init__", "line_number": 133, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 133, "usage_type": "attribute"}, {"api_name": "platform.system", "line_number": 134, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 135, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 135, "usage_type": "attribute"}, {"api_name": "platform.system", "line_number": 136, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 137, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 137, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 138, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 138, "usage_type": "attribute"}, {"api_name": "math.sqrt", "line_number": 154, "usage_type": "call"}, {"api_name": "random.random", "line_number": 207, "usage_type": "call"}, {"api_name": "random.random", "line_number": 208, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 239, "usage_type": "attribute"}]} +{"seq_id": "265454518", "text": "#!/usr/bin/env python\n\nimport sys\nfrom scipy.stats import kendalltau as kt\n\ndef ktnativecontacts():\n \"\"\"Read in two lists and calculate kendall tau correlation.\n It's important that the lists have the same system order e.g. if I want\n to calc kt for experimental dG and MELD Tms, I will have two files each\n with two columns of data - column 1 is system ID, column 2 is dG or Tm -\n but the files will have the data in the same order, according to system ID.\n \"\"\"\n # open experimental dG file\n dGfile = open(sys.argv[1], 'r')\n lines = dGfile.readlines()\n dG = []\n for x in lines:\n dG.append(x.split( )[1])\n dGfile.close()\n\n # open meld folding data file\n tmfile = open(sys.argv[2], 'r')\n lines = tmfile.readlines()\n tm = []\n for x in lines:\n tm.append(x.split( )[1])\n tmfile.close()\n\n # calc kt corr\n ktcorr = kt(dG, tm)\n\n print(dG)\n \n print(tm)\n\n print(ktcorr)\n\n# call ktnativecontacts\nktnativecontacts()\n", "sub_path": "kendall-tau.py", "file_name": "kendall-tau.py", "file_ext": "py", "file_size_in_byte": 991, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sys.argv", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 22, "usage_type": "attribute"}, {"api_name": "scipy.stats.kendalltau", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "464546309", "text": "# Licensed under an MIT style license -- see LICENSE.md\n\nfrom pesummary.gw.file.psd import PSDDict, PSD\nimport numpy as np\nimport os\nimport shutil\n\n__author__ = [\"Charlie Hoy \"]\n\n\nclass TestPSDDict(object):\n \"\"\"Test that the PSDDict works as expected\n \"\"\"\n def setup(self):\n \"\"\"Setup the testing class\n \"\"\"\n self.psd_data = {\n \"H1\": [[0.00000e+00, 2.50000e-01],\n [1.25000e-01, 2.50000e-01],\n [2.50000e-01, 2.50000e-01]],\n \"V1\": [[0.00000e+00, 2.50000e-01],\n [1.25000e-01, 2.50000e-01],\n [2.50000e-01, 2.50000e-01]]\n }\n \n def test_initiate(self):\n \"\"\"Test that the PSDDict class can be initalized correctly\n \"\"\"\n psd_dict = PSDDict(self.psd_data.keys(), self.psd_data.values())\n assert sorted(list(psd_dict.detectors)) == [\"H1\", \"V1\"]\n assert isinstance(psd_dict[\"H1\"], PSD)\n np.testing.assert_almost_equal(\n psd_dict[\"H1\"].frequencies, [0, 0.125, 0.25]\n )\n np.testing.assert_almost_equal(\n psd_dict[\"V1\"].strains, [0.25, 0.25, 0.25]\n )\n\n psd_dict = PSDDict(self.psd_data)\n assert sorted(list(psd_dict.detectors)) == [\"H1\", \"V1\"]\n assert isinstance(psd_dict[\"H1\"], PSD)\n np.testing.assert_almost_equal(\n psd_dict[\"H1\"].frequencies, [0, 0.125, 0.25]\n )\n np.testing.assert_almost_equal(\n psd_dict[\"V1\"].strains, [0.25, 0.25, 0.25]\n )\n\n def test_plot(self):\n \"\"\"Test the plotting function works correctly\n \"\"\"\n import matplotlib\n\n psd_dict = PSDDict(self.psd_data)\n assert isinstance(psd_dict.plot(), matplotlib.figure.Figure)\n\n\nclass TestPSD(object):\n \"\"\"Test the PSD class\n \"\"\"\n def setup(self):\n \"\"\"Setup the testing class\n \"\"\"\n self.obj = PSD([[10, 20], [10, 20]])\n if not os.path.isdir(\".outdir\"):\n os.mkdir(\".outdir\")\n\n def teardown(self):\n \"\"\"Remove all files and directories created from this class\n \"\"\"\n if os.path.isdir(\".outdir\"):\n shutil.rmtree(\".outdir\")\n\n def test_save_to_file(self):\n \"\"\"Test the save to file method\n \"\"\"\n self.obj.save_to_file(\".outdir/test.dat\")\n data = np.genfromtxt(\".outdir/test.dat\")\n np.testing.assert_almost_equal(data.T[0], [10, 10])\n np.testing.assert_almost_equal(data.T[1], [20, 20])\n\n def test_invalid_input(self):\n \"\"\"Test that the appropiate error is raised if the input is wrong\n \"\"\"\n import pytest\n\n with pytest.raises(IndexError):\n obj = PSD([10, 10])\n", "sub_path": "pesummary/tests/psd_test.py", "file_name": "psd_test.py", "file_ext": "py", "file_size_in_byte": 2725, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "pesummary.gw.file.psd.PSDDict", "line_number": 29, "usage_type": "call"}, {"api_name": "pesummary.gw.file.psd.PSD", "line_number": 31, "usage_type": "argument"}, {"api_name": "numpy.testing.assert_almost_equal", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.testing.assert_almost_equal", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pesummary.gw.file.psd.PSDDict", "line_number": 39, "usage_type": "call"}, {"api_name": "pesummary.gw.file.psd.PSD", "line_number": 41, "usage_type": "argument"}, {"api_name": "numpy.testing.assert_almost_equal", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.testing.assert_almost_equal", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 45, "usage_type": "attribute"}, {"api_name": "pesummary.gw.file.psd.PSDDict", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.figure", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pesummary.gw.file.psd.PSD", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.genfromtxt", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.testing.assert_almost_equal", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 79, "usage_type": "attribute"}, {"api_name": "numpy.testing.assert_almost_equal", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 80, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 87, "usage_type": "call"}, {"api_name": "pesummary.gw.file.psd.PSD", "line_number": 88, "usage_type": "call"}]} +{"seq_id": "99231501", "text": "import os\nimport shutil\nimport filecmp\n\nfrom dvc import utils\nfrom tests.basic_env import DirHierarchyEnvironment\n\nclass TestExecutor(DirHierarchyEnvironment):\n def setUp(self):\n DirHierarchyEnvironment.init_environment(self)\n\n def tearDown(self):\n pass\n\n def test_rmtree(self):\n root = 'testdir'\n\n os.makedirs(root + '/subdir')\n with open(root + '/file1', 'w+') as f:\n f.write('file1contents')\n with open(root + '/subdir/file2', 'w+') as f:\n f.write('file2contents')\n\n utils.rmtree(root)\n self.assertFalse(os.path.exists(root))\n\n def test_copyfile(self):\n src = 'file1'\n dest = 'file2'\n dest_dir = 'testdir'\n\n with open(src, 'w+') as f:\n f.write('file1contents')\n\n os.mkdir(dest_dir)\n\n utils.copyfile(src, dest)\n self.assertTrue(filecmp.cmp(src, dest))\n\n utils.copyfile(src, dest_dir)\n self.assertTrue(filecmp.cmp(src, '{}/{}'.format(dest_dir, src)))\n\n shutil.rmtree(dest_dir)\n os.remove(src)\n os.remove(dest)\n\n def test_map_progress(self):\n def f(target):\n with open(target, 'w+') as o:\n o.write(target)\n\n targets = ['map{}'.format(i) for i in range(1, 10)]\n n_threads = [1, 10, 20]\n\n for n in n_threads:\n utils.map_progress(f, targets, n)\n", "sub_path": "tests/test_utils.py", "file_name": "test_utils.py", "file_ext": "py", "file_size_in_byte": 1395, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "tests.basic_env.DirHierarchyEnvironment", "line_number": 8, "usage_type": "name"}, {"api_name": "tests.basic_env.DirHierarchyEnvironment.init_environment", "line_number": 10, "usage_type": "call"}, {"api_name": "tests.basic_env.DirHierarchyEnvironment", "line_number": 10, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 18, "usage_type": "call"}, {"api_name": "dvc.utils.rmtree", "line_number": 24, "usage_type": "call"}, {"api_name": "dvc.utils", "line_number": 24, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 35, "usage_type": "call"}, {"api_name": "dvc.utils.copyfile", "line_number": 37, "usage_type": "call"}, {"api_name": "dvc.utils", "line_number": 37, "usage_type": "name"}, {"api_name": "filecmp.cmp", "line_number": 38, "usage_type": "call"}, {"api_name": "dvc.utils.copyfile", "line_number": 40, "usage_type": "call"}, {"api_name": "dvc.utils", "line_number": 40, "usage_type": "name"}, {"api_name": "filecmp.cmp", "line_number": 41, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 43, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 44, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 45, "usage_type": "call"}, {"api_name": "dvc.utils.map_progress", "line_number": 56, "usage_type": "call"}, {"api_name": "dvc.utils", "line_number": 56, "usage_type": "name"}]} +{"seq_id": "30222014", "text": "# Copyright (c) 2019,20-21 NVIDIA CORPORATION & AFFILIATES.\n# 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\nimport torch\n\nfrom ..mesh.trianglemesh import _unbatched_subdivide_vertices\nfrom .pointcloud import _base_points_to_voxelgrids\n\n__all__ = ['trianglemeshes_to_voxelgrids']\n\n\ndef trianglemeshes_to_voxelgrids(\n vertices,\n faces,\n resolution,\n origin=None,\n scale=None,\n return_sparse=False\n):\n r\"\"\"Converts meshes to surface voxelgrids of a given resolution. It first upsamples \n triangle mesh's vertices to given resolution, then it performs a box test. \n If a voxel contains a triangle vertex, set that voxel to 1. Vertex will be \n offset and scaled as following: \n :math:`\\text{normalized_vertices} = (\\text{vertices} - \\text{origin}) / \\text{scale}`\n the voxelgrids will only be generated in the range [0, 1] of normalized_vertices.\n\n Args:\n vertices (torch.tensor): Batched vertices of the input meshes, of shape\n :math:`(\\text{batch_size}, \\text{num_vertices}, 3)`.\n faces (torch.tensor): Unbatched faces of the meshes, of shape\n :math:`(\\text{num_faces}, 3)`.\n resolution (int): desired resolution of generated voxelgrid.\n origin (torch.tensor): Origin of the voxelgrid in the mesh coordinates,\n of shape :math:`(\\text{batch_size}, 3)`.\n Default: ``torch.min(vertices, dim=1)[0]``.\n scale (torch.tensor): The scale by which we divide the vertex position,\n of shape :math:`(\\text{batch_size})`.\n Default: ``torch.max(torch.max(vertices, dim=1)[0] - origin, dim=1)[0]``.\n return_sparse (optional, bool): If True, sparse tensor is returned. Default: False.\n\n Returns:\n (torch.Tensor or torch.FloatTensor):\n Binary batched voxelgrids, of shape\n :math:`(\\text{batch_size}, \\text{resolution}, \\text{resolution}, \\text{resolution})`.\n If return_sparse is True, sparse tensor is returned.\n\n Example:\n >>> vertices = torch.tensor([[[0, 0, 0],\n ... [1, 0, 0],\n ... [0, 0, 1]]], dtype=torch.float)\n >>> faces = torch.tensor([[0, 1, 2]], dtype=torch.long)\n >>> origin = torch.zeros((1, 3))\n >>> scale = torch.ones((1))\n >>> trianglemeshes_to_voxelgrids(vertices, faces, 3, origin, scale)\n tensor([[[[1., 1., 1.],\n [0., 0., 0.],\n [0., 0., 0.]],\n \n [[1., 1., 0.],\n [0., 0., 0.],\n [0., 0., 0.]],\n \n [[1., 0., 0.],\n [0., 0., 0.],\n [0., 0., 0.]]]])\n \"\"\"\n if not isinstance(resolution, int):\n raise TypeError(f\"Expected resolution to be int \"\n f\"but got {type(resolution)}.\")\n\n if origin is None:\n min_val = torch.min(vertices, dim=1)[0]\n origin = min_val\n\n if scale is None:\n max_val = torch.max(vertices, dim=1)[0]\n scale = torch.max(max_val - origin, dim=1)[0]\n\n batch_size = vertices.shape[0]\n voxelgrids = []\n batched_points = (vertices - origin.unsqueeze(1)) / scale.view(-1, 1, 1)\n\n for i in range(batch_size):\n\n points = _unbatched_subdivide_vertices(batched_points[i], faces, resolution)\n\n voxelgrid = _base_points_to_voxelgrids(\n points.unsqueeze(0), resolution, return_sparse=return_sparse\n )\n\n voxelgrids.append(voxelgrid)\n\n return torch.cat(voxelgrids)\n", "sub_path": "kaolin/ops/conversions/trianglemesh.py", "file_name": "trianglemesh.py", "file_ext": "py", "file_size_in_byte": 4214, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "torch.min", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 89, "usage_type": "call"}, {"api_name": "mesh.trianglemesh._unbatched_subdivide_vertices", "line_number": 97, "usage_type": "call"}, {"api_name": "pointcloud._base_points_to_voxelgrids", "line_number": 99, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 105, "usage_type": "call"}]} +{"seq_id": "49975325", "text": "import os\nimport pytagcloud\nimport matplotlib.pyplot as plt\nimport collections\n\n\n\nRESULT_DIRECTORY = \"__result__/visualization\"\n\n\ndef wordcloud(filename,wordfreq):\n taglist = pytagcloud.make_tags(wordfreq.items(),maxsize=80)\n #print(taglist)\n save_filename = '%s/wordcloud_%s.jpg' % (RESULT_DIRECTORY,filename)\n pytagcloud.create_tag_image(taglist,\n save_filename,\n size = (900,600),\n fontname= 'Malgun',\n rectangular = False,\n background = (0,0,0)\n )\ndef graph_bar(\n # bar그래프 필수 파라미터\n title = None, xlabel = None, ylabel = None, showgrid = False,\n values = None,ticks = None, filename = None, showgraph = True):\n\n fig, subplots = plt.subplots(1, 1)\n subplots.bar(range(len(values)), values, align = 'center')\n\n # ticks -> x축\n if ticks is not None and isinstance(ticks,collections.Sequence):\n subplots.set_xticks(range(len(ticks)))\n subplots.set_xticklabels(ticks, rotation=80, fontsize='xx-small')\n #title\n if title is not None and isinstance(title, str):\n subplots.set_title(title)\n\n #xlabel\n if title is not None and isinstance(title, str):\n subplots.set_xlabel(xlabel)\n #ylabel\n\n if title is not None and isinstance(title, str):\n subplots.set_ylabel(ylabel)\n\n # showgrid -- > 데코\n subplots.grid(showgrid)\n\n\n if filename is not None and isinstance(filename,str):\n save_filename = '%s/bar_%s.png' % (RESULT_DIRECTORY,filename)\n plt.savefig(save_filename, dpi = 400, bbox_inches = 'tight')\n # 해상도 400, 여백을 주지않음\n\n\n # show graph\n if showgraph:\n plt.show()\n\n\n # 디렉토리 여부 조사\nif os.path.exists(RESULT_DIRECTORY) is False:\n os.mkdir(RESULT_DIRECTORY)", "sub_path": "visualize/visualizer.py", "file_name": "visualizer.py", "file_ext": "py", "file_size_in_byte": 1933, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pytagcloud.make_tags", "line_number": 12, "usage_type": "call"}, {"api_name": "pytagcloud.create_tag_image", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "collections.Sequence", "line_number": 31, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "174174269", "text": "from django.contrib import admin\nfrom .models import Pattern, Product\n\nclass PatternAdmin(admin.ModelAdmin):\n list_filter = ['pattern_name','related_pattern_module','creation_date','is_in_progress']\n list_display = ('pattern_name','related_pattern_module','creation_date', 'last_updated','is_in_progress')\n fieldsets = [\n (None,{'fields':[\n 'pattern_name',\n 'related_pattern_module',\n 'is_in_progress'\n ]}),\n ('Date fields',{'fields': [\n 'creation_date',\n 'last_updated']}),\n ('General pattern desctiption', {'fields': [\n 'command_used',\n 'command_reason',\n 'command_platform',\n 'command_priveleges_required',\n 'command_always_executed',\n 'file_used',\n 'file_reason',\n 'file_platform',\n 'file_priveleges',\n 'file_always_retrieved',\n 'tku_last_updated',\n 'tku_last_used'\n ]}),\n ]\nclass ProductAdmin(admin.ModelAdmin):\n list_filter = ['product_name']\n list_display = ('product_name','creation_date')\n\nadmin.site.register(Pattern, PatternAdmin)\nadmin.site.register(Product, ProductAdmin)", "sub_path": "matrix/bmc_matrix/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 1232, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.contrib.admin.ModelAdmin", "line_number": 4, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 4, "usage_type": "name"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 31, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 31, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 35, "usage_type": "call"}, {"api_name": "models.Pattern", "line_number": 35, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 35, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 35, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 36, "usage_type": "call"}, {"api_name": "models.Product", "line_number": 36, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 36, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 36, "usage_type": "name"}]} +{"seq_id": "540373418", "text": "# Import Splinter, BeautifulSoup, and Pandas\nfrom splinter import Browser\nfrom bs4 import BeautifulSoup as soup\nfrom webdriver_manager.chrome import ChromeDriverManager\nimport pandas as pd\n\n#set the executable path and initialize a browser.\nexecutable_path = {'executable_path': 'C:/Users/Mobeen/.wdm/drivers/chromedriver/win32/89.0.4389.23/chromedriver'}\nbrowser = Browser('chrome', **executable_path, headless=False)\n\n#Visit the mars nasa news site\nurl = 'https://data-class-mars.s3.amazonaws.com/Mars/index.html'\nbrowser.visit(url)\n#Optional delay for loading the page\nbrowser.is_element_present_by_css('div.list_text', wait_time=1)\n\nhtml = browser.html\nnews_soup = soup(html, 'html.parser')\nslide_elem = news_soup.select_one('div.list_text')\n\nslide_elem.find('div', class_='content_title')\n\nnews_title = slide_elem.find('div', class_='content_title').get_text()\nnews_title\n\nnews_p = slide_elem.find('div', class_='article_teaser_body').get_text()\nnews_p\n\n#Scrape Mars Data: Featured Image\n\n# Visit Url \nurl = 'https://data-class-jpl-space.s3.amazonaws.com/JPL_Space/index.html'\nbrowser.visit(url)\n\n# Find and click the full image button \nfull_image_elem = browser.find_by_tag('button')[1] # we want to click on the second button \nfull_image_elem.click()\n\n#Parse the resulting html with soup\nhtml = browser.html\nimg_soup = soup(html, 'html.parser')\n\n# Find the relative image url \nimg_url_rel = img_soup.find('img', class_='fancybox-image').get('src')\nimg_url_rel\n\n#We pulled the link of the image \nimg_url = f'https://data-class-jpl-space.s3.amazonaws.com/JPL_Space/{img_url_rel}'\nimg_url\n\n# # Scrape Mars Data: Mars Facts\ndf = pd.read_html('https://data-class-mars-facts.s3.amazonaws.com/Mars_Facts/index.html')[0]\ndf.columns=['description', 'Mars', 'Earth']\ndf.set_index('description', inplace=True)\ndf\n\ndf.to_html\n\nbrowser.quit()\n\n\n", "sub_path": "Mars_Scraping/Mission_to_Mars.py", "file_name": "Mission_to_Mars.py", "file_ext": "py", "file_size_in_byte": 1839, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "splinter.Browser", "line_number": 9, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 18, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 41, "usage_type": "call"}, {"api_name": "pandas.read_html", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "399683907", "text": "import os\nfrom itertools import chain\n\nfrom leapp.exceptions import StopActorExecutionError\nfrom leapp.libraries.actor import preparetransaction\nfrom leapp.libraries.stdlib import api, run, CalledProcessError\nfrom leapp.models import BootContent\n\n_REQUIRED_PACKAGES = [\n 'biosdevname',\n 'binutils',\n 'cifs-utils',\n 'device-mapper-multipath',\n 'dracut',\n 'dracut-config-generic',\n 'dracut-config-rescue',\n 'dracut-network',\n 'dracut-tools',\n 'fcoe-utils',\n 'hostname',\n 'iscsi-initiator-utils',\n 'kbd',\n 'kernel',\n 'kernel-core',\n 'kernel-modules',\n 'keyutils',\n 'lldpad',\n 'lvm2',\n 'mdadm',\n 'nfs-utils',\n 'openssh-clients',\n 'plymouth',\n 'rpcbind',\n 'systemd-container',\n 'tar',\n]\n\n\ndef prepare_el8_userspace(overlayfs, userspace_dir, enabled_repos):\n overlay_target = os.path.join(overlayfs.merged, 'el8target')\n run(['rm', '-rf', userspace_dir])\n run(['mkdir', '-p', userspace_dir])\n run(['mkdir', '-p', overlay_target])\n try:\n run(['mount', '--bind', userspace_dir, overlay_target])\n repos_opt = [['--enablerepo', repo] for repo in enabled_repos]\n repos_opt = list(chain(*repos_opt))\n preparetransaction.guard_container_call(\n overlayfs_info=overlayfs,\n cmd=[\n 'dnf',\n 'install',\n '-y',\n '--nogpgcheck',\n '--setopt=module_platform_id=platform:el8',\n '--releasever', '8',\n '--installroot', '/el8target',\n '--disablerepo', '*'\n ] + repos_opt + _REQUIRED_PACKAGES,\n print_output=True\n )\n finally:\n run(['umount', '-fl', overlay_target])\n run(['mkdir', '-p', os.path.join(userspace_dir, 'artifacts')])\n\n\ndef copy_modules(userspace_dir):\n sysuprh_path = api.get_actor_folder_path('dracut/85sys-upgrade-redhat')\n sysup_path = api.get_actor_folder_path('dracut/90sys-upgrade')\n if not sysup_path or not sysuprh_path:\n raise StopActorExecutionError(\n message='Could not find required dracut modules to generate '\n 'initram disk'\n )\n run([\n 'cp', '-a',\n api.get_actor_folder_path('dracut'),\n userspace_dir\n ])\n\n\ndef generate_initram_disk(userspace_dir):\n # Copy dracut modules to el8 userspace\n copy_modules(userspace_dir)\n # Copy generate-initram.sh\n run([\n 'cp',\n '-a',\n api.get_actor_file_path('generate-initram.sh'),\n userspace_dir\n ])\n run([\n 'systemd-nspawn',\n '--register=no',\n '--quiet',\n '-D', userspace_dir,\n '/bin/sh', '/generate-initram.sh'\n ])\n\ndef remove_userspace(userspace_dir):\n try:\n run(['rm', '-rf', userspace_dir])\n except Exception:\n api.current_logger().info('Removal of el8 userspace failed - Failure ignored', exc_info=True)\n\ndef copy_boot_files(userspace_dir):\n kernel = 'vmlinuz-upgrade.x86_64'\n initram = 'initramfs-upgrade.x86_64.img'\n content = BootContent(\n kernel_path=os.path.join('/boot', kernel),\n initram_path=os.path.join('/boot', initram)\n )\n\n run(['cp', '-a', os.path.join(userspace_dir, 'artifacts', kernel), content.kernel_path])\n run(['cp', '-a', os.path.join(userspace_dir, 'artifacts', initram), content.initram_path])\n\n api.produce(content)\n", "sub_path": "repos/system_upgrade/el7toel8/actors/prepareupgradetransaction/libraries/xinitramgen.py", "file_name": "xinitramgen.py", "file_ext": "py", "file_size_in_byte": 3405, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.path.join", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "leapp.libraries.stdlib.run", "line_number": 41, "usage_type": "call"}, {"api_name": "leapp.libraries.stdlib.run", "line_number": 42, "usage_type": "call"}, {"api_name": "leapp.libraries.stdlib.run", "line_number": 43, "usage_type": "call"}, {"api_name": "leapp.libraries.stdlib.run", "line_number": 45, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 47, "usage_type": "call"}, {"api_name": "leapp.libraries.actor.preparetransaction.guard_container_call", "line_number": 48, "usage_type": "call"}, {"api_name": "leapp.libraries.actor.preparetransaction", "line_number": 48, "usage_type": "name"}, {"api_name": "leapp.libraries.stdlib.run", "line_number": 63, "usage_type": "call"}, {"api_name": "leapp.libraries.stdlib.run", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "leapp.libraries.stdlib.api.get_actor_folder_path", "line_number": 68, "usage_type": "call"}, {"api_name": "leapp.libraries.stdlib.api", "line_number": 68, "usage_type": "name"}, {"api_name": "leapp.libraries.stdlib.api.get_actor_folder_path", "line_number": 69, "usage_type": "call"}, {"api_name": "leapp.libraries.stdlib.api", "line_number": 69, "usage_type": "name"}, {"api_name": "leapp.exceptions.StopActorExecutionError", "line_number": 71, "usage_type": "call"}, {"api_name": "leapp.libraries.stdlib.run", "line_number": 75, "usage_type": "call"}, {"api_name": "leapp.libraries.stdlib.api.get_actor_folder_path", "line_number": 77, "usage_type": "call"}, {"api_name": "leapp.libraries.stdlib.api", "line_number": 77, "usage_type": "name"}, {"api_name": "leapp.libraries.stdlib.run", "line_number": 86, "usage_type": "call"}, {"api_name": "leapp.libraries.stdlib.api.get_actor_file_path", "line_number": 89, "usage_type": "call"}, {"api_name": "leapp.libraries.stdlib.api", "line_number": 89, "usage_type": "name"}, {"api_name": "leapp.libraries.stdlib.run", "line_number": 92, "usage_type": "call"}, {"api_name": "leapp.libraries.stdlib.run", "line_number": 102, "usage_type": "call"}, {"api_name": "leapp.libraries.stdlib.api.current_logger", "line_number": 104, "usage_type": "call"}, {"api_name": "leapp.libraries.stdlib.api", "line_number": 104, "usage_type": "name"}, {"api_name": "leapp.models.BootContent", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path", "line_number": 110, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "leapp.libraries.stdlib.run", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "leapp.libraries.stdlib.run", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 115, "usage_type": "attribute"}, {"api_name": "leapp.libraries.stdlib.api.produce", "line_number": 117, "usage_type": "call"}, {"api_name": "leapp.libraries.stdlib.api", "line_number": 117, "usage_type": "name"}]} +{"seq_id": "383004391", "text": "import pymysql\r\nimport pymysql.cursors\r\nfrom pymysql import MySQLError\r\n\r\nfrom Models.Address import Address\r\nfrom Models.PaymentCard import PaymentCard\r\nfrom Models.PromoCode import PromoCode\r\nfrom Models.Sale import Sale\r\nfrom Models.Tarif import Tarif\r\nfrom Models.User import User\r\n\r\n\r\nclass DBHelper:\r\n def __init__(self):\r\n self.connect = pymysql.connect(\r\n host='34.107.9.48',\r\n user='student',\r\n password='student',\r\n db='sql_taxi',\r\n charset='utf8',\r\n cursorclass=pymysql.cursors.DictCursor\r\n )\r\n self.cursor = self.connect.cursor()\r\n\r\n def login_user(self, login, password):\r\n self.cursor.execute(f\"CALL login_credential('{login}', '{password}');\")\r\n result = self.cursor.fetchone()\r\n return result['result']\r\n\r\n def get_user(self, login):\r\n self.cursor.execute(f\"CALL get_credential('{login}');\")\r\n user = User(self.cursor.fetchone())\r\n return user\r\n\r\n def insert_user(self, login, password, name1, name2, name3, role):\r\n error = None\r\n try:\r\n self.cursor.execute(\r\n f\"CALL insert_credential('{name1}','{name2}','{name3}','{login}','{password}','{role}');\")\r\n self.connect.commit()\r\n except MySQLError as e:\r\n if e.args[0] == 1062:\r\n error = \"User with this username is already registered\"\r\n else:\r\n error = e.args[1]\r\n finally:\r\n return error\r\n\r\n def insert_passport(self, id, series, number, identification_number, gender, date_experation, date_receiving):\r\n self.cursor.execute(\r\n f\"CALL insert_passport({id} ,'{series}','{number}','{identification_number}','{gender}',\"\r\n f\" '{date_experation}', '{date_receiving}');\")\r\n self.connect.commit()\r\n\r\n def insert_car(self, id, model, number):\r\n self.cursor.execute(f\"CALL insert_car({id}, '{model}', '{number}')\")\r\n self.connect.commit()\r\n\r\n def insert_address(self, country, city_type, city, region, district, street_type, street, house, building, flat):\r\n self.cursor.execute(\r\n f\"CALL insert_address('{country}', '{region}', '{district}', '{street}', '{city}',\"\r\n f\" '{street_type}', '{city_type}', '{house}', '{building}', '{flat}')\"\r\n )\r\n self.connect.commit()\r\n id = self.cursor.fetchone()['id']\r\n return id\r\n\r\n def get_address(self, id):\r\n self.cursor.execute(f\"CALL get_address('{id}');\")\r\n address = Address(self.cursor.fetchone())\r\n return address\r\n\r\n def insert_card(self, login, name, card_number, expiration_date, security_code):\r\n self.cursor.execute(f\"CALL insert_credit_card({id}, '{name}', {card_number}, '{expiration_date}',\"\r\n f\" {security_code});\")\r\n self.connect.commit()\r\n\r\n def get_cards(self, login):\r\n self.connect.commit()\r\n self.cursor.execute(f\"CALL get_credit_card('{login}');\")\r\n result = self.cursor.fetchall()\r\n cards = []\r\n for c in result:\r\n cards.append(PaymentCard(c))\r\n return cards\r\n\r\n def get_tariffs(self):\r\n self.connect.commit()\r\n self.cursor.execute(f\"CALL get_tariffs();\")\r\n result = self.cursor.fetchall()\r\n tariffs = []\r\n for t in result:\r\n tariffs.append(Tarif(t))\r\n return tariffs\r\n\r\n def check_promo(self, promo):\r\n self.connect.commit()\r\n self.cursor.execute(f\"CALL check_promo('{promo}');\")\r\n result = self.cursor.fetchone()\r\n return result['discount_procent']\r\n\r\n def set_driver_status(self, login, type):\r\n self.cursor.execute(f\"CALL set_driver_status('{login}', '{type}')\")\r\n self.connect.commit()\r\n\r\n def get_free_driver(self):\r\n self.connect.commit()\r\n self.cursor.execute(f\"CALL get_free_driver();\")\r\n result = self.cursor.fetchone()\r\n if result is None:\r\n return 0\r\n else:\r\n return result['id']\r\n\r\n def order_taxi(self, login_client, login_driver, id_payment_card, if_tariff, cost, address_from, address_to):\r\n print(f\"CALL order_taxi('{login_client}', {if_tariff}, {id_payment_card}, '{login_driver}', {cost}, {address_from}, {address_to})\")\r\n self.cursor.execute(f\"CALL order_taxi('{login_client}', {if_tariff}, {id_payment_card}, '{login_driver}', {cost}, {address_from}, {address_to})\")\r\n self.connect.commit()\r\n result = self.cursor.fetchone()\r\n return result['id_s']\r\n\r\n def end_order_taxi(self, id, comment, raiting):\r\n self.cursor.execute(f\"CALL end_order_taxi({id}, '{comment}', {raiting})\")\r\n self.connect.commit()\r\n\r\n def get_all_user(self, name1, name2, name3, role, status):\r\n self.connect.commit()\r\n self.cursor.execute(\r\n f\"CALL get_all_users('{name1}', '{name2}', '{name3}', '{role}', '{status}')\")\r\n result = self.cursor.fetchall()\r\n users = []\r\n for u in result:\r\n users.append(User(u))\r\n return users\r\n\r\n def get_all_tariffs(self):\r\n self.connect.commit()\r\n self.cursor.execute(\r\n f\"CALL get_all_tariff()\")\r\n result = self.cursor.fetchall()\r\n tariffs = []\r\n for t in result:\r\n tariffs.append(Tarif(t))\r\n return tariffs\r\n\r\n def get_all_promo_codes(self):\r\n self.connect.commit()\r\n self.cursor.execute(\r\n f\"CALL get_all_promo()\")\r\n result = self.cursor.fetchall()\r\n promo_codes = []\r\n for p in result:\r\n promo_codes.append(PromoCode(p))\r\n return promo_codes\r\n\r\n def insert_tariff(self, tariff, city, no_city):\r\n self.cursor.execute(f\"CALL insert_tariff('{tariff}', {city}, {no_city})\")\r\n self.connect.commit()\r\n\r\n def insert_promo(self, promo, date, discount):\r\n self.cursor.execute(f\"CALL insert_promo('{promo}', '{date}', {discount})\")\r\n self.connect.commit()\r\n\r\n def get_history(self, login):\r\n self.connect.commit()\r\n print(login)\r\n print(f\"CALL get_history('{login}')\")\r\n self.cursor.execute(f\"CALL get_history('{login}')\")\r\n result = self.cursor.fetchall()\r\n sales = []\r\n for s in result:\r\n sales.append(Sale(s))\r\n return sales\r\n", "sub_path": "6term/SQL/sql_taxi_app/Service/DBHelper.py", "file_name": "DBHelper.py", "file_ext": "py", "file_size_in_byte": 6403, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pymysql.connect", "line_number": 15, "usage_type": "call"}, {"api_name": "pymysql.cursors", "line_number": 21, "usage_type": "attribute"}, {"api_name": "Models.User.User", "line_number": 32, "usage_type": "call"}, {"api_name": "pymysql.MySQLError", "line_number": 41, "usage_type": "name"}, {"api_name": "Models.Address.Address", "line_number": 70, "usage_type": "call"}, {"api_name": "Models.PaymentCard.PaymentCard", "line_number": 84, "usage_type": "call"}, {"api_name": "Models.Tarif.Tarif", "line_number": 93, "usage_type": "call"}, {"api_name": "Models.User.User", "line_number": 133, "usage_type": "call"}, {"api_name": "Models.Tarif.Tarif", "line_number": 143, "usage_type": "call"}, {"api_name": "Models.PromoCode.PromoCode", "line_number": 153, "usage_type": "call"}, {"api_name": "Models.Sale.Sale", "line_number": 172, "usage_type": "call"}]} +{"seq_id": "362591262", "text": "__author__ = 'Davide Tampellini'\n__copyright__ = '2015 Davide Tampellini - FabbricaBinaria'\n__license__ = 'GNU GPL version 3 or later'\n\nimport os\nimport datetime\nimport csv\nimport sys\nimport re\nimport colorama\nfrom lib.detector.trash import TrashDetector\nfrom lib.detector.hash import HashDetector\nfrom lib.runner.abstract import AbstractCommand\nfrom lib.detector.plain import PlainDetector\nfrom lib.exceptions.exceptions import RunningError\n\n\nclass DumpScraperGetscore(AbstractCommand):\n def check(self):\n if not os.path.exists('data/raw'):\n raise RunningError(colorama.Fore.RED + \"There aren't any dump files to process. Scrape them before continuing.\")\n\n def run(self, **keyargs):\n if 'training' in keyargs and keyargs['training']:\n targetfolder = 'training'\n folders = ['trash', 'hash', 'plain']\n else:\n targetfolder = 'raw'\n folders = [self.parentArgs.since]\n\n if self.parentArgs.until:\n date = datetime.datetime.strptime(self.parentArgs.since, \"%Y-%m-%d\").date()\n end = datetime.datetime.strptime(self.parentArgs.until, \"%Y-%m-%d\").date()\n\n date += datetime.timedelta(days=1)\n\n while end >= date:\n folders.append(date.strftime('%Y-%m-%d'))\n date += datetime.timedelta(days=1)\n\n regex_empty_lines = re.compile(r'^\\n', re.M)\n organizers = [TrashDetector(), PlainDetector(), HashDetector()]\n\n features_handle = open('data/' + targetfolder + '/features.csv', 'w')\n features_writer = csv.DictWriter(features_handle, fieldnames=['trash', 'plain', 'hash', 'label', 'file'])\n features_writer.writerow({'trash': 'Trash score', 'plain': 'Plain score', 'hash': 'Hash score', 'label': 'Label', 'file': 'Filename'})\n\n for folder in folders:\n source = 'data/' + targetfolder + '/' + folder\n\n if not os.path.exists(source):\n print(\"Directory \" + source + \" does not exist!\")\n print(\"\")\n continue\n\n print(\"Directory : \" + folder)\n\n for root, dirs, files in os.walk(source):\n for dump in files:\n sys.stdout.write('.')\n sys.stdout.flush()\n\n with open(root + \"/\" + dump, 'r+') as handle:\n data = handle.read()\n\n # Remove /r since they could mess up regex\n data = data.replace(\"\\r\", \"\")\n\n # Let's count only the amount of non-empty lines\n all_lines = data.count(\"\\n\")\n empty_lines = len(re.findall(regex_empty_lines, data))\n\n # Guess what? You need to pass a float during a division, otherwise Python will truncate the result\n # For crying it loud!!!\n lines = float(max(all_lines - empty_lines, 1))\n\n info = {'data': data, 'lines': lines}\n csvline = {}\n results = {'trash': 0, 'plain': 0, 'hash': 0}\n\n for organizer in organizers:\n organizer.reset().setinfo(info).analyze(results)\n\n score = min(organizer.score, 3)\n csvline[organizer.returnkey()] = round(score, 4)\n results[organizer.returnkey()] = round(score, 4)\n\n label = os.path.basename(root)\n\n if label == 'hash':\n csvline['label'] = 1\n elif label == 'plain':\n csvline['label'] = 2\n elif label == 'trash':\n csvline['label'] = 0\n else:\n csvline['label'] = ''\n\n csvline['file'] = os.path.basename(root) + \"/\" + dump\n\n features_writer.writerow(csvline)\n\n print(\"\")\n\n features_handle.close()\n print(\"\")\n\n\n", "sub_path": "src/lib/runner/getscore.py", "file_name": "getscore.py", "file_ext": "py", "file_size_in_byte": 4035, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "lib.runner.abstract.AbstractCommand", "line_number": 18, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "lib.exceptions.exceptions.RunningError", "line_number": 21, "usage_type": "call"}, {"api_name": "colorama.Fore", "line_number": 21, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 32, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 32, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 33, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 33, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 39, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 41, "usage_type": "call"}, {"api_name": "re.M", "line_number": 41, "usage_type": "attribute"}, {"api_name": "lib.detector.trash.TrashDetector", "line_number": 42, "usage_type": "call"}, {"api_name": "lib.detector.plain.PlainDetector", "line_number": 42, "usage_type": "call"}, {"api_name": "lib.detector.hash.HashDetector", "line_number": 42, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 58, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 60, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 60, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 61, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 61, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}]} +{"seq_id": "132634181", "text": "# coding: utf-8\nimport re\nimport web\nimport json\nimport ipaddress\n\n\nclass ValidationError(web.HTTPError):\n '''`400 Bad Request` error.'''\n\n headers = {'Content-Type': 'application/json'}\n\n def __init__(self, errors, headers=None):\n status = '400 Bad Request'\n message = json.dumps(errors)\n web.HTTPError.__init__(self, status, headers or self.headers,\n unicode(message))\n\n\nclass NotFoundError(web.HTTPError):\n '''`404 Not Found` error.'''\n\n headers = {'Content-Type': 'application/json'}\n\n def __init__(self, note='Not Found', headers=None):\n status = '404 Not Found'\n message = json.dumps([{'note': note}])\n web.HTTPError.__init__(self, status, headers or self.headers,\n unicode(message))\n\n\nclass InternalServerError(web.HTTPError):\n '''`500 Internal Server Error`.'''\n\n headers = {'Content-Type': 'application/json'}\n\n def __init__(self, note='Internal Server Error', headers=None):\n status = '500 Internal Server Error'\n message = json.dumps([{'note': note}])\n web.HTTPError.__init__(self, status, headers or self.headers,\n unicode(message))\n\n\nclass JSONForm(web.form.Form):\n '''Subclass web.py form to parse json input\n and raise validation errors in json format'''\n\n def serialize_errors(self):\n '''Serializes form's errors'''\n errors = []\n if self.note:\n errors.append({'note': self.note})\n for i in self.inputs:\n if i.note:\n errors.append({'name': i.name, 'note': i.note})\n return errors\n\n def validates(self, source=None, _validate=True, **kw):\n if not (source or kw):\n try:\n # Try to parse json request\n source = json.loads(web.data())\n except:\n # Assume empty request\n pass\n\n if super(JSONForm, self).validates(source, _validate, **kw):\n return True\n else:\n raise ValidationError(self.serialize_errors())\n\n\nclass Input(web.form.Input):\n '''Base input class'''\n\n\nclass BooleanInput(Input):\n '''Processes boolean input'''\n\n def __init__(self, name, *validators, **attrs):\n self.checked = attrs.pop('checked', False)\n super(BooleanInput, self).__init__(name, *validators, **attrs)\n\n def set_value(self, value):\n if value in ('0', 'false'):\n self.checked = False\n else:\n self.checked = bool(value)\n\n def get_value(self):\n return self.checked\n\n\nclass StringInput(Input):\n '''Processes string input'''\n\n\nclass IntegerInput(Input):\n\n def get_value(self):\n try:\n return int(self.value)\n except:\n return None\n\n\ndef valid_ip(value):\n '''Validates ip address'''\n return not value or ipaddress.ip_address(value)\n\n\ndef valid_choice(*choices):\n '''Validates if integer value is in choices'''\n return lambda value: int(value) in choices\n\n\nMAC_REGEXP = re.compile(r'^([0-9a-f]{2}[:-]){5}([0-9a-f]{2})$', re.I)\n\n\ndef valid_mac(value):\n '''Validates mac address'''\n return not value or bool(MAC_REGEXP.match(str(value)))\n", "sub_path": "restapp/modules/json_form.py", "file_name": "json_form.py", "file_ext": "py", "file_size_in_byte": 3237, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "web.HTTPError", "line_number": 8, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 15, "usage_type": "call"}, {"api_name": "web.HTTPError.__init__", "line_number": 16, "usage_type": "call"}, {"api_name": "web.HTTPError", "line_number": 16, "usage_type": "attribute"}, {"api_name": "web.HTTPError", "line_number": 20, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 27, "usage_type": "call"}, {"api_name": "web.HTTPError.__init__", "line_number": 28, "usage_type": "call"}, {"api_name": "web.HTTPError", "line_number": 28, "usage_type": "attribute"}, {"api_name": "web.HTTPError", "line_number": 32, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 39, "usage_type": "call"}, {"api_name": "web.HTTPError.__init__", "line_number": 40, "usage_type": "call"}, {"api_name": "web.HTTPError", "line_number": 40, "usage_type": "attribute"}, {"api_name": "web.form", "line_number": 44, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 62, "usage_type": "call"}, {"api_name": "web.data", "line_number": 62, "usage_type": "call"}, {"api_name": "web.form", "line_number": 73, "usage_type": "attribute"}, {"api_name": "ipaddress.ip_address", "line_number": 109, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 117, "usage_type": "call"}, {"api_name": "re.I", "line_number": 117, "usage_type": "attribute"}]} +{"seq_id": "262040585", "text": "from selenium import webdriver\nimport time\n\ndef get_download(links):\n\n download_dir = \"C:\\\\Temp\\\\stockMarketReportPDF\" # for linux/*nix, download_dir=\"/usr/Public\"\n\n options = webdriver.ChromeOptions()\n\n profile = {\"plugins.plugins_list\": [{\"enabled\": False, \"name\": \"Chrome PDF Viewer\"}], # Disable Chrome's PDF Viewer\n \"download.default_directory\": download_dir, \"download.extensions_to_open\": \"applications/pdf\"}\n options.add_experimental_option(\"prefs\", profile)\n driver = webdriver.Chrome('C:\\\\driver\\\\chromedriver.exe', chrome_options=options) # Optional argument, if not specified will search path.\n\n driver.get(links)\n\n#크롬의 경우 chrome driver의 위치를 지정해 준다.\ndriver = webdriver.Chrome('c:\\driver\\chromedriver.exe')\n\n## PhantomJS의 경우도 마찬가지\n#driver = webdriver.PhantomJS('C:\\driver\\phantomjs-2.1.1-windows\\phantomjs-2.1.1-windows\\bin\\phantomjs.exe')\ndriver.implicitly_wait(5)\n##URL에 접근한다.,\n\n#driver.get('http://hkconsensus.hankyung.com/apps.analysis/analysis.list')\ndriver.get('http://hkconsensus.hankyung.com/apps.analysis/analysis.list?&sdate=2018-09-15&edate=2018-10-15&order_type=&now_page=160')\n\nelements = driver.find_elements_by_xpath('//*[@id=\"contents\"]/div/table/tbody/tr/td/div/a')\nlinks = []\n\nfor i in range(len(elements)):\n links.append(elements[i].get_attribute('href'))\n\n\nfor link in links:\n print('navigating to: ' + link)\n get_download(link)\n\n time.sleep(1)\n\n\n\"\"\"\n driver.get(link)\n\n # do stuff within that page here...\n\n driver.back()\n\"\"\"\n\n", "sub_path": "beomi_crawling/selenium/selenium_HKConsensus.py", "file_name": "selenium_HKConsensus.py", "file_ext": "py", "file_size_in_byte": 1570, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "selenium.webdriver.ChromeOptions", "line_number": 8, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 8, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 13, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 13, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 18, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 18, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "613312774", "text": "from pyramid.config import Configurator\nimport pyramid.tweens\nfrom pyramid_nacl_session import EncryptedCookieSessionFactory\n\nfrom geru import cache\n\n\ndef main(global_config, **settings):\n \"\"\"\n This function returns a Pyramid WSGI application.\n \"\"\"\n\n # this call inits/enable/disable the cache\n cache.refresh()\n\n config = Configurator(settings=settings)\n\n # session configuration\n hex_secret = bytes.fromhex(settings['geru.session_secret'].strip())\n factory = EncryptedCookieSessionFactory(hex_secret) # other config ad lib.\n config.set_session_factory(factory)\n\n # tween to handle the session and track the urls visited\n config.add_tween(\n 'geru.tweens.user_tracker_tween',\n over=pyramid.tweens.MAIN\n )\n\n # adding here the packages used in all envs\n config.include('pyramid_jinja2')\n config.include('pyramid_restful')\n config.include('.models')\n config.include('.routes')\n\n config.scan()\n return config.make_wsgi_app()\n", "sub_path": "etc/geru_code/geru/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 995, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "geru.cache.refresh", "line_number": 14, "usage_type": "call"}, {"api_name": "geru.cache", "line_number": 14, "usage_type": "name"}, {"api_name": "pyramid.config.Configurator", "line_number": 16, "usage_type": "call"}, {"api_name": "pyramid_nacl_session.EncryptedCookieSessionFactory", "line_number": 20, "usage_type": "call"}, {"api_name": "pyramid.config.tweens", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pyramid.config", "line_number": 26, "usage_type": "name"}]} +{"seq_id": "399224860", "text": "from selenium import webdriver\n\n# Launch Firefox Instance\ndriver = webdriver.Firefox(executable_path=\"C:\\\\office\\\\code\\\\workspace-python\\\\automation-repository-python\\\\drivers\\\\windows\\\\geckodriver-v0.23.0-win64\\\\geckodriver.exe\")\n\n# Assigning URL to variable 'baseUrl'\nbaseUrl = \"http://book.theautomatedtester.co.uk/chapter2\"\n\n# Open the link\ndriver.get(baseUrl)\n\n# Maximize browser window\ndriver.maximize_window()\n\n# Get Element\nelement = driver.find_element_by_id('random')\n\n# Print text of button\nprint('Text of Button : ' + element.get_attribute('value'))\n\n# Quit Driver\ndriver.quit()", "sub_path": "get_attributes_of_elements.py", "file_name": "get_attributes_of_elements.py", "file_ext": "py", "file_size_in_byte": 590, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "selenium.webdriver.Firefox", "line_number": 4, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 4, "usage_type": "name"}]} +{"seq_id": "82970380", "text": "# -*- coding: utf-8 -*-\n\"\"\"One-off script to clear out a few registrations that failed during archiving.\"\"\"\nimport logging\nimport sys\n\nfrom framework.transactions.context import TokuTransaction\nfrom website.app import init_app\nfrom website.archiver import ARCHIVER_FAILURE, ARCHIVER_INITIATED\nfrom website.archiver.model import ArchiveJob\n\nfrom scripts import utils as script_utils\n\nlogger = logging.getLogger(__name__)\n\nFAILED_ARCHIVE_JOBS = [\n '56a8d29e9ad5a10179f77bd6',\n]\n\ndef clean(reg, dry):\n logger.info('Cleaning registration: {}'.format(reg))\n if not reg.registered_from:\n logger.info('Node {0} had registered_from == None'.format(reg._id))\n return\n if not reg.archive_job: # Be extra sure not to delete legacy registrations\n logger.info('Skipping legacy registration: {0}'.format(reg._id))\n return\n if not dry:\n reg.archive_job.status = ARCHIVER_FAILURE\n reg.archive_job.sent = True\n reg.archive_job.save()\n reg.root.sanction.forcibly_reject()\n reg.root.sanction.save()\n reg.root.delete_registration_tree(save=True)\n logger.info('Done.')\n\ndef main(dry):\n if dry:\n logger.info('[DRY MODE]')\n init_app(routes=False)\n for _id in FAILED_ARCHIVE_JOBS:\n archive_job = ArchiveJob.load(_id)\n assert archive_job.status == ARCHIVER_INITIATED\n root_node = archive_job.dst_node.root\n with TokuTransaction():\n clean(reg=root_node, dry=dry)\n\nif __name__ == \"__main__\":\n dry = 'dry' in sys.argv\n if not dry:\n script_utils.add_file_logger(logger, __file__)\n main(dry=dry)\n", "sub_path": "scripts/clean_failed_archives.py", "file_name": "clean_failed_archives.py", "file_ext": "py", "file_size_in_byte": 1626, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "website.archiver.ARCHIVER_FAILURE", "line_number": 28, "usage_type": "name"}, {"api_name": "website.app.init_app", "line_number": 39, "usage_type": "call"}, {"api_name": "website.archiver.model.ArchiveJob.load", "line_number": 41, "usage_type": "call"}, {"api_name": "website.archiver.model.ArchiveJob", "line_number": 41, "usage_type": "name"}, {"api_name": "website.archiver.ARCHIVER_INITIATED", "line_number": 42, "usage_type": "name"}, {"api_name": "framework.transactions.context.TokuTransaction", "line_number": 44, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 48, "usage_type": "attribute"}, {"api_name": "scripts.utils.add_file_logger", "line_number": 50, "usage_type": "call"}, {"api_name": "scripts.utils", "line_number": 50, "usage_type": "name"}]} +{"seq_id": "195001324", "text": "#--------------\n# USER: aguimorefran\n# DATE: 15/10/2019\n#--------------\n\nimport os\nimport datetime\nimport db\nimport cinema\n\n# Asks user for the input string\ndef createConStr(filepath):\n conStr = input(\"Connection string?\")\n choice = input(\"Save? (yes/no)\")\n if choice == \"yes\":\n # save file\n filename = input(\"Filename?\")\n f = open(filepath + \"/\" + filename, \"w+\")\n f.write(conStr)\n if len(os.stat(filepath + \"/\" + filename)) == 0:\n print(\"\\tERROR: could not create file\")\n return 0\n else:\n print(\"File created succesfully: %s\" % (filepath + \"/\" + filename))\n f.close()\n return conStr\n else:\n return createConStr(filepath)\n\n# Searchs for a mongo connection string and returns it. If there are none, asks for it\ndef askConStr():\n filepath = \"connections\"\n\n # search for connection strings in folder\n try:\n os.mkdir(filepath)\n print(\"Directory \", filepath, \" created\")\n except FileExistsError:\n None\n\n # show existing files\n if len(os.listdir(filepath)) == 0:\n print(\"No files in folder\")\n else:\n for i in os.listdir(filepath):\n print(\"\\t-\", i)\n\n choice = \"\"\n conStr = \"\"\n choice = input(\"Select file or create a new one? (select/new)\")\n if choice == \"select\":\n # select file from folder\n choice = input(\"Select file\")\n # if file not in folder\n if choice not in os.listdir(filepath):\n print(\"File not in folder\")\n return askConStr()\n else:\n # file selected\n f = open(filepath + \"/\" + choice, \"r\")\n conStr = f.read()\n f.close()\n choice = input(\"Use this connection string? (yes/no)\")\n if choice == \"yes\":\n return auth(conStr)\n else:\n return askConStr()\n\n elif choice == \"new\":\n # create new file in folder\n conStr = createConStr(filepath)\n choice = input(\"Use this connection string? (yes/no)\")\n if choice == \"yes\":\n return auth(conStr)\n else:\n return askConStr()\n else:\n print(\"Wrong input\")\n return askConStr()\n\n# Input con string and replaces with user given password\ndef auth(conStr):\n pwd = input(\"Password?\")\n return conStr.replace(\"\", pwd, 1)\n\n# Asks user for a custom date\ndef askDate():\n day = int(input(\"Day?\"))\n month = int(input(\"Month?\"))\n year = int(input(\"Year?\"))\n hour = int(input(\"Hour?\"))\n return datetime.datetime(year, month, day, hour)\n\n\n# Asks the film to the user\ndef askFilm():\n title = \"\"\n date = None\n rating = 0.0\n imdb = 0\n opinion = \"\"\n ok = \"\"\n while ok != \"yes\":\n title = input(\"Title?\")\n\n choice = input(\"Actual or custom date? (actual/custom)\")\n if choice == \"actual\":\n # use todays date\n date = datetime.datetime.now()\n elif choice == \"custom\":\n # ask for custom date\n date = askDate()\n else:\n continue\n \n rating = float(input(\"Rating?\"))\n imdb = int(input(\"IMDB?\"))\n opinion = input(\"Opinion?\")\n\n film1 = cinema.Film(title, date, rating, imdb, opinion)\n cinema.Film.printFilm(film1)\n\n ok = input(\"Upload? (yes/no)\")\n if ok == \"yes\":\n return film1\n \n\n# Program starts here\n# m1 is the server instance\nm1 = db.Mongo(askConStr())\n\nmenu = 0\nwhile menu != 4:\n print(\"\\n---------------------\")\n print(\"1. New film\")\n print(\"2. See top films\")\n print(\"4. Exit\")\n print(\"---------------------\")\n menu = int(input(\"Choice?\"))\n\n if menu == 1:\n # ask new film\n film1 = askFilm()\n m1.uploadFilm(film1)\n elif menu == 2:\n n = int(input(\"n top films?\"))\n films = m1.getTopFilms(n)\n for film in films:\n print(film)\n elif menu == 3:\n None\n elif menu == 4:\n print(\"Exiting program\")\n break\n else:\n print(\"Bad input\")\n continue", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4125, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.stat", "line_number": 20, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 36, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 42, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 45, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 55, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 92, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 109, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 109, "usage_type": "attribute"}, {"api_name": "cinema.Film", "line_number": 120, "usage_type": "call"}, {"api_name": "cinema.Film.printFilm", "line_number": 121, "usage_type": "call"}, {"api_name": "cinema.Film", "line_number": 121, "usage_type": "attribute"}, {"api_name": "db.Mongo", "line_number": 130, "usage_type": "call"}]} +{"seq_id": "624424469", "text": "import pygame\nimport math\nimport numpy as np\nfrom RobotLib.Math import *\nfrom cv2 import imread, imwrite\n# to install cv2 module: pip install opencv-python\n\nclass ObstacleMap:\n \"\"\"\n Maintains an obstacle map.\n \n The map contains binary values: 0 (free) or 1 (occupied).\n \n The map is stored as a matrix with shape (height,width).\n \n The map can be used to simulate rangefinder readings.\n \"\"\"\n def __init__(self,path,max_dist=80):\n \"\"\" Creates an obstacle map.\n Arguments:\n path: path to a grayscale image representing the map\n max_dist: maximum rangefinder reading (cm)\n \"\"\"\n self.max_dist = max_dist\n\n # read map from image\n self.grid = imread(path,0).astype('float32')/255.\n \n self.height = self.grid.shape[0]\n self.width = self.grid.shape[1]\n \n def draw(self,surface):\n \"\"\" Draws the obstacle map onto the surface. \"\"\"\n # transpose grid and convert to 0-255 range\n omap_array = ((1.-self.grid.transpose())*255.).astype('int')\n # replicate across RGB channels\n omap_array = np.tile(np.expand_dims(omap_array,axis=-1),[1,1,3])\n # draw grid on the surface\n pygame.surfarray.blit_array(surface,omap_array)\n \n def get_first_hit(self,T_sonar_map):\n \"\"\" Calculates distance that sonar would report given current pose.\n Arguments:\n T_sonar_map: sonar-to-map transformation matrix\n Returns:\n First-hit distance or zero if no hit.\n \"\"\"\n # iterate over possible range of distances\n for i in range(self.max_dist):\n # get point in sonar reference frame\n pt_sonar = vec(i,0)\n\n # transform to map reference frame\n pt_map = mul(T_sonar_map,pt_sonar)\n\n # get integer location in map\n r = int(pt_map[1])\n c = int(pt_map[0])\n\n # test for location outside map\n if r < 0 or r >= self.grid.shape[0]:\n continue\n if c < 0 or c >= self.grid.shape[1]:\n continue\n\n # test if cell is occupied\n if self.grid[r,c] > 0:\n # return rangefinder measurement plus a gaussian noise of standard deviation of 10 centered to 0\n return i + np.random.normal(0,2,1)[0]\n\n # return 0 for no hit\n return 0.\n\nif __name__ == '__main__':\n # run this script to make an example map\n\n # create a map\n grid = np.zeros((128,128))\n \n # border\n grid[:,0] = 1\n grid[:,127] = 1\n grid[0,:] = 1\n grid[127,:] = 1\n \n # obstacle\n grid[75:100,75:100] = 1\n \n imwrite('map.png',(grid*255.).astype('uint8'))\n\n", "sub_path": "HW3/ObstacleMap.py", "file_name": "ObstacleMap.py", "file_ext": "py", "file_size_in_byte": 2765, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "cv2.imread", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 37, "usage_type": "call"}, {"api_name": "pygame.surfarray.blit_array", "line_number": 39, "usage_type": "call"}, {"api_name": "pygame.surfarray", "line_number": 39, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 69, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 78, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 89, "usage_type": "call"}]} +{"seq_id": "47465776", "text": "import numpy as np \nfrom PIL import Image, ImageDraw\n\n\ndef process(image, k):\n\tdraw = ImageDraw.Draw(image)\n\theight = image.size[0]\n\twidth = image.size[1]\n\tpix = image.load()\n\tfor i in range(height):\n\t\tfor j in range(width):\n\t\t\tvals = []\n\t\t\tfor y in range(i-k/2,i+k/2+1):\n\t\t\t\tfor x in range(j-k/2,j+k/2+1):\n\t\t\t\t\tr, c = y, x\n\t\t\t\t\tif (y < 0): r = 0\n\t\t\t\t\tif (x < 0): c = 0\n\t\t\t\t\tif (y >= height): r = height - 1 \t\t\n\t\t\t\t\tif (x >= width): c = width - 1 \t\n\t\t\t\t\tvals.append(pix[r,c][0]*(256**2)+pix[r,c][1]*256+pix[r,c][2])\n\t\t\tvals = np.sort(vals)\t\n\t\t\tval = vals[len(vals)/2]\n\t\t\tr = val / (256**2)\n\t\t\tval -= r * (256**2)\n\t\t\tg = val / 256\n\t\t\tb = val % 256\n\t\t\tdraw.point((i, j), (r, g, b)) \n\nif __name__ == \"__main__\":\n\timport sys\n\timage = Image.open(sys.argv[1])\n\tprocess(image, 3)\n\timage.save(\"res.jpg\", \"JPEG\")\n", "sub_path": "median.py", "file_name": "median.py", "file_ext": "py", "file_size_in_byte": 804, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "PIL.ImageDraw.Draw", "line_number": 6, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 6, "usage_type": "name"}, {"api_name": "numpy.sort", "line_number": 21, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 31, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 31, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 31, "usage_type": "attribute"}]} +{"seq_id": "502782497", "text": "from keras import Sequential\nfrom keras.layers import Dense\nfrom keras.optimizers import Adam\nimport keras_metrics\nimport util\nimport keras\nfrom parameters import DATA_PATH, padding_len\nfrom keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping, CSVLogger, TensorBoard\nfrom imblearn.over_sampling import RandomOverSampler\nfrom imblearn.under_sampling import RandomUnderSampler\nfrom keras.utils import to_categorical\nimport os\nfrom dataset import DataGenerator\nfrom joblib import load, dump\nfrom keras.metrics import categorical_accuracy\nfrom keras.preprocessing.sequence import pad_sequences\nfrom embedding import load_embedding_matrix\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.layers import Embedding, Conv1D, GlobalMaxPooling1D, GlobalAveragePooling1D\nfrom keras.layers import Reshape, Flatten\nimport numpy as np\nimport pandas as pd\nfrom keras.layers import CuDNNLSTM, CuDNNGRU, Bidirectional, TimeDistributed, MaxPooling1D, BatchNormalization\nimport gc\nfrom imblearn import FunctionSampler\nfrom keras.layers import Concatenate, Input, Dropout, SpatialDropout1D\nfrom keras.models import Model\nfrom attention import AttentionWeightedAverage\nfrom util import CyclicLR\n\n\nNAME = \"bi_lstm_gru_spat_clr\"\n\nPARAMS = {\n 'sequence_len': padding_len,\n 'embedding_dim': 200,\n 'epochs': 5,\n 'batch_size': 256,\n 'loss': 'categorical_crossentropy',\n 'num_classes': len(util.get_categories()),\n 'class_weights': None,\n 'sampler': None\n}\n\nPATH = DATA_PATH+'models/'+NAME+'/'\n\nDEPENDENCIES = {\n 'categorical_recall': keras_metrics.categorical_recall(),\n 'balanced_recall': util.balanced_recall,\n 'AttentionWeightedAverage': AttentionWeightedAverage,\n 'f1': util.f1\n }\n\ndef load_model(path, extras={}):\n dependencies = {**DEPENDENCIES, **extras}\n return keras.models.load_model(path, custom_objects=dependencies)\n\ndef load_lastest(lang='pt', extras={}):\n if (len(os.listdir(PATH)) > 0):\n highest = (0, '')\n for file in os.listdir(PATH):\n if(file.startswith('weights') and file.endswith(lang+'.hdf5')):\n epoch = int(file.split('-')[1])\n if(highest[0] < epoch):\n highest = (epoch, file)\n if(highest[0] > 0):\n model = load_model(PATH+highest[1], extras=extras)\n epoch = highest[0]\n return model, epoch\n return None\n \ndef generate_model(params):\n inputs = Input(shape=(PARAMS['sequence_len'],), dtype='int32')\n input_layer = Embedding(input_dim=params['vocab_size'],\n output_dim=params['embedding_dim'],\n input_length=PARAMS['sequence_len'],\n weights=[params['embedding_matrix']],\n trainable=False)(inputs)\n input_layer = SpatialDropout1D(0.2)(input_layer)\n i1 = Bidirectional(CuDNNLSTM(params['embedding_dim']*2, return_sequences=True))(input_layer)\n i1 = Concatenate(axis=1)([AttentionWeightedAverage()(i1), GlobalAveragePooling1D()(i1), GlobalMaxPooling1D()(i1)])\n i2 = Bidirectional(CuDNNGRU(params['embedding_dim'], return_sequences=True))(input_layer)\n i2 = Concatenate(axis=1)([AttentionWeightedAverage()(i2), GlobalAveragePooling1D()(i2), GlobalMaxPooling1D()(i2)])\n concatenated_tensor = Concatenate(axis=1)([i1, i2])\n concatenated_tensor = Dense(params['num_classes']*2, activation = 'relu')(concatenated_tensor)\n concatenated_tensor = BatchNormalization()(concatenated_tensor)\n concatenated_tensor = Dropout(0.1)(concatenated_tensor)\n output = Dense(params['num_classes'], activation=\"softmax\")(concatenated_tensor)\n\n model = Model(inputs=inputs, outputs=output)\n\n opt=Adam()\n\n model.compile(optimizer=opt, loss=params['loss'],\n metrics=['accuracy',\n categorical_accuracy,\n keras_metrics.categorical_recall(),\n util.balanced_recall,\n util.f1\n ])\n\n model.summary()\n\n return model, params\n\ndef process_y(y):\n return to_categorical(y, num_classes=PARAMS['num_classes'])\n \ndef process_x(x):\n return pad_sequences(x, maxlen=PARAMS['sequence_len'])\n \ndef balance_dataset(X, y, cut_off=0.5, random_state=42):\n xyconcat = np.concatenate((X, y.reshape(-1,1)), axis=1)\n xyconcat.view('i8,i8').sort(order=['f1'], axis=0)\n unique_y, freq = np.unique(xyconcat[:, 1], return_counts=True)\n unique_y_dict = {y: i for i, y in enumerate(unique_y)}\n unique_y_sorted = unique_y[freq.argsort()[::-1]]\n dict_sorted = np.asarray([unique_y_dict[i] for i in unique_y_sorted])\n split_sorted = np.asarray(np.split(xyconcat, np.cumsum(freq)[:-1]))[dict_sorted]\n X1, y1 = np.hsplit(np.concatenate(split_sorted[:int(len(split_sorted)*cut_off)]), 2)\n X2, y2 = np.hsplit(np.concatenate(split_sorted[int(len(split_sorted)*cut_off):]), 2)\n X1, y1 = RandomUnderSampler(random_state=random_state).fit_resample(X1, y1)\n X2, y2 = RandomOverSampler(random_state=random_state).fit_resample(X2, y2)\n X_sampled = np.concatenate((X1, X2))\n y_sampled = np.concatenate((y1.reshape(-1), y2))\n del xyconcat, split_sorted\n del X1, y1, X2, y2\n gc.collect()\n return X_sampled, y_sampled\n\ndef train(lang='pt'):\n params = PARAMS.copy()\n initial_epoch = 0\n X, Y = util.get_X_Y(data_type='keras_tokenized_tri', lang=lang, file_type=\"dump\")\n X = np.asarray(X)\n params['embedding_matrix'] = load_embedding_matrix(name=\"fasttext_sg_tri_8\", tokenizer='keras_tokenized_tri',lang=lang, model_type=\"dump\")\n params[\"vocab_size\"] = params['embedding_matrix'].shape[0]\n params[\"embedding_dim\"] = params['embedding_matrix'].shape[1]\n \n if not os.path.exists(PATH):\n os.makedirs(PATH)\n if not os.path.exists(PATH+'log_dir'):\n os.makedirs(PATH+'log_dir')\n \n #params[\"loss\"] = util.focal_loss(gamma=5.,alpha=1588)\n lastest_model = load_lastest(lang=lang)\n if(lastest_model == None):\n model, params = generate_model(params)\n else:\n model = lastest_model[0]\n initial_epoch = lastest_model[1]\n \n print(model.metrics_names)\n \n params['sampler'] = FunctionSampler(func=balance_dataset,\n kw_args={'cut_off': 0.5,\n 'random_state': 42})\n \n data_generator = DataGenerator(X,Y, lang=lang, process_x=process_x, process_y=process_y, batch_size=PARAMS['batch_size'], sampler=params['sampler'])\n #data_generator.remove_reliable_0(pct=1.0)\n validation_data = data_generator.get_validation_data()\n print('data_generator.x: ', data_generator.__getitem__(0)[0][0:5])\n print('data_generator.y: ', data_generator.__getitem__(0)[1][0:5])\n\n #params[\"class_weights\"]= data_generator.get_classes_weights()\n \n reduce_lr = ReduceLROnPlateau(monitor='val_categorical_accuracy', factor=0.2, patience=3, verbose=1)\n early_stopping = EarlyStopping(monitor='val_categorical_accuracy', min_delta=0.02, patience=10, verbose=1)\n csv_logger = CSVLogger(PATH+'traning.log', append=True)\n tensorboard_callback = TensorBoard(log_dir=PATH+'log_dir', batch_size=params[\"batch_size\"])\n model_checkpoint = ModelCheckpoint(filepath=PATH+'weights-{epoch:03d}-{val_categorical_accuracy:.4f}-'+lang+'.hdf5',\n monitor='val_categorical_accuracy',\n verbose=1,\n mode='max')\n clr = CyclicLR(base_lr=1e-3, max_lr=2e-3,\n step_size=300., mode='exp_range',\n gamma=0.99994)\n \n params[\"callbacks\"] = [model_checkpoint, tensorboard_callback, csv_logger, clr]\n \n model.fit_generator(data_generator,\n epochs=params[\"epochs\"],\n verbose=1,\n callbacks=params[\"callbacks\"],\n validation_data=validation_data,\n #workers=7,\n #use_multiprocessing=True,\n class_weight=params[\"class_weights\"],\n initial_epoch=initial_epoch)\n \ndef evaluate(lang='pt'):\n X, Y = util.get_X_Y(data_type='keras_tokenized_tri', lang=lang, file_type=\"dump\")\n X = np.asarray(X)\n data_generator = DataGenerator(X,Y, lang=lang, process_x=process_x, batch_size=PARAMS['batch_size'])\n model, epoch = load_lastest(lang=lang)\n x_val, y_val = data_generator.get_validation_data()\n y_pred = model.predict(x_val)\n y_pred = y_pred.argmax(axis=-1)\n print('Model '+NAME+' val score on '+lang+': ', util.evaluate(y_val, y_pred))\n \ndef generate_result_file():\n dsets = []\n for lang in ['pt', 'es']:\n X = util.get_X_test(data_type='keras_tokenized_tri', lang=lang, file_type=\"dump\")\n model, epoch = load_lastest(lang=lang)\n y_pred = util.one_hot_decode(model.predict(process_x(X)))\n index = np.load('./data/test_index_'+lang+'.npy')\n df = pd.DataFrame({'id': index, 'category': y_pred})\n df.index = df['id']\n dsets.append(df)\n print('y_pred '+lang+' unique: ', len(np.unique(y_pred)))\n df = pd.concat(dsets)\n df = df.sort_index()\n df[['id', 'category']].to_csv('./data/results-'+NAME+'.csv', index=False)", "sub_path": "models/bi_lstm_gru_spat_clr.py", "file_name": "bi_lstm_gru_spat_clr.py", "file_ext": "py", "file_size_in_byte": 9459, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "parameters.padding_len", "line_number": 35, "usage_type": "name"}, {"api_name": "util.get_categories", "line_number": 40, "usage_type": "call"}, {"api_name": "parameters.DATA_PATH", "line_number": 45, "usage_type": "name"}, {"api_name": "keras_metrics.categorical_recall", "line_number": 48, "usage_type": "call"}, {"api_name": "util.balanced_recall", "line_number": 49, "usage_type": "attribute"}, {"api_name": "attention.AttentionWeightedAverage", "line_number": 50, "usage_type": "name"}, {"api_name": "util.f1", "line_number": 51, "usage_type": "attribute"}, {"api_name": "keras.models.load_model", "line_number": 56, "usage_type": "call"}, {"api_name": "keras.models", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 59, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 61, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 73, "usage_type": "call"}, {"api_name": "keras.layers.Embedding", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.layers.SpatialDropout1D", "line_number": 79, "usage_type": "call"}, {"api_name": "keras.layers.Bidirectional", "line_number": 80, "usage_type": "call"}, {"api_name": "keras.layers.CuDNNLSTM", "line_number": 80, "usage_type": "call"}, {"api_name": "keras.layers.Concatenate", "line_number": 81, "usage_type": "call"}, {"api_name": "attention.AttentionWeightedAverage", "line_number": 81, "usage_type": "call"}, {"api_name": "keras.layers.GlobalAveragePooling1D", "line_number": 81, "usage_type": "call"}, {"api_name": "keras.layers.GlobalMaxPooling1D", "line_number": 81, "usage_type": "call"}, {"api_name": "keras.layers.Bidirectional", "line_number": 82, "usage_type": "call"}, {"api_name": "keras.layers.CuDNNGRU", "line_number": 82, "usage_type": "call"}, {"api_name": "keras.layers.Concatenate", "line_number": 83, "usage_type": "call"}, {"api_name": "attention.AttentionWeightedAverage", "line_number": 83, "usage_type": "call"}, {"api_name": "keras.layers.GlobalAveragePooling1D", "line_number": 83, "usage_type": "call"}, {"api_name": "keras.layers.GlobalMaxPooling1D", "line_number": 83, "usage_type": "call"}, {"api_name": "keras.layers.Concatenate", "line_number": 84, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 85, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 86, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 87, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 88, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 90, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 92, "usage_type": "call"}, {"api_name": "keras.metrics.categorical_accuracy", "line_number": 96, "usage_type": "name"}, {"api_name": "keras_metrics.categorical_recall", "line_number": 97, "usage_type": "call"}, {"api_name": "util.balanced_recall", "line_number": 98, "usage_type": "attribute"}, {"api_name": "util.f1", "line_number": 99, "usage_type": "attribute"}, {"api_name": "keras.utils.to_categorical", "line_number": 107, "usage_type": "call"}, {"api_name": "keras.preprocessing.sequence.pad_sequences", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.split", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.hsplit", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.hsplit", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 121, "usage_type": "call"}, {"api_name": "imblearn.under_sampling.RandomUnderSampler", "line_number": 122, "usage_type": "call"}, {"api_name": "imblearn.over_sampling.RandomOverSampler", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 125, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 128, "usage_type": "call"}, {"api_name": "util.get_X_Y", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 135, "usage_type": "call"}, {"api_name": "embedding.load_embedding_matrix", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 140, "usage_type": "call"}, {"api_name": "os.path", "line_number": 140, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 141, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 142, "usage_type": "call"}, {"api_name": "os.path", "line_number": 142, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 143, "usage_type": "call"}, {"api_name": "imblearn.FunctionSampler", "line_number": 155, "usage_type": "call"}, {"api_name": "dataset.DataGenerator", "line_number": 159, "usage_type": "call"}, {"api_name": "keras.callbacks.ReduceLROnPlateau", "line_number": 167, "usage_type": "call"}, {"api_name": "keras.callbacks.EarlyStopping", "line_number": 168, "usage_type": "call"}, {"api_name": "keras.callbacks.CSVLogger", "line_number": 169, "usage_type": "call"}, {"api_name": "keras.callbacks.TensorBoard", "line_number": 170, "usage_type": "call"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 171, "usage_type": "call"}, {"api_name": "util.CyclicLR", "line_number": 175, "usage_type": "call"}, {"api_name": "util.get_X_Y", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 193, "usage_type": "call"}, {"api_name": "dataset.DataGenerator", "line_number": 194, "usage_type": "call"}, {"api_name": "util.evaluate", "line_number": 199, "usage_type": "call"}, {"api_name": "util.get_X_test", "line_number": 204, "usage_type": "call"}, {"api_name": "util.one_hot_decode", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 207, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 208, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 211, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 212, "usage_type": "call"}]} +{"seq_id": "14783945", "text": "import cv2\nimport os\nimport time\nimport argparse\nimport imutils\nfrom imutils.video import FPS\nfrom imutils.video import WebcamVideoStream\nfrom pyimagesearch.centroidtracker import CentroidTracker\nfrom pyimagesearch.trackableobject import TrackableObject\nfrom tpu_model import *\nfrom aux_functions import *\n\n# Suppress TF warnings\nos.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n\nmouse_pts = []\n\ndef get_mouse_points(event, x, y, flags, param):\n # Used to mark 4 points on the frame zero of the video that will be warped\n # Used to mark 2 points on the frame zero of the video that are 6 feet away\n global mouseX, mouseY, mouse_pts\n if event == cv2.EVENT_LBUTTONDOWN:\n mouseX, mouseY = x, y\n cv2.circle(image, (x, y), 10, (0, 255, 255), 10)\n if \"mouse_pts\" not in globals():\n mouse_pts = []\n mouse_pts.append((x, y))\n print(\"Point detected\")\n print(mouse_pts)\n\ndef append_objs_to_img(cv2_im, objs, labels, ROI, ct, trackableObjects, totalCount):\n height, width, channels = cv2_im.shape\n rects = []\n\n for obj in objs:\n x0, y0, x1, y1 = list(obj.bbox)\n x0, y0, x1, y1 = int(x0*width), int(y0*height), int(x1*width), int(y1*height)\n if x0 < 100 or x1 > 550:\n continue\n rects.append((x0, y0, x1, y1))\n percent = int(100 * obj.score)\n label = '{}-{}%'.format(labels.get(obj.id, obj.id), percent)\n\n cv2_im = cv2.rectangle(cv2_im, (x0, y0), (x1, y1), (0, 255, 0), 2)\n cv2_im = cv2.putText(cv2_im, label, (x0, y0+30),\n cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)\n\n objects = ct.update(rects)\n direction_str = \"...\"\n # loop over the tracked objects\n for (objectID, centroid) in objects.items():\n # check to see if a trackable object exists for the current\n # object ID\n to = trackableObjects.get(objectID, None)\n\n # if there is no existing trackable object, create one\n if to is None:\n to = TrackableObject(objectID, centroid)\n\n # otherwise, there is a trackable object so we can utilize it\n # to determine direction\n else:\n # check to see if the object has been counted or not\n if not to.counted:\n # if the previous centroids from one side\n # count as soon as the updated centroid reach other side\n for c in to.centroids:\n if c[0] < ROI and centroid[0] < ROI:\n direction_str = \"...\"\n elif c[0] < ROI and centroid[0] > ROI:\n totalCount += 1\n to.counted = True\n direction_str = \"In\"\n break\n elif c[0] > ROI and centroid[0] > ROI:\n direction_str = \"...\"\n elif c[0] > ROI and centroid[0] < ROI:\n totalCount += 1\n to.counted = True\n direction_str = \"Out\"\n break\n\n # update new centroid to trackable object\n to.centroids.append(centroid)\n\n # store the trackable object in our dictionary\n trackableObjects[objectID] = to\n\n # draw both the ID of the object and the centroid of the\n # object on the output frame\n text = \"ID {}\".format(objectID)\n cv2.putText(cv2_im, text, (centroid[0] - 10, centroid[1] - 10),\n cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)\n cv2.circle(cv2_im, (centroid[0], centroid[1]), 4, (0, 255, 0), -1)\n\n return cv2_im, totalCount, direction_str, trackableObjects\n\n# Command-line input setup\n#labels = load_labels('people_label.txt')\nstream_1 = 'rtsp://192.168.200.78:556/user=admin_password=tlJwpbo6_channel=1_stream=0.sdp?real_stream'\nstream_2 = 'rtsp://192.168.200.79:554/user=admin_password=tlJwpbo6_channel=1_stream=0.sdp?real_stream'\nmodel_1 = 'detection_1_edgetpu.tflite'\nmodel_2 = 'detection_2_edgetpu.tflite'\nmodel = 'detection_toco_edgetpu.tflite'\n\n\n# Define a DNN model\nDNN_distance = model_tpu(model_1)\nDNN_count = model_tpu(model_2)\n# Get video handle\nfvs1 = WebcamVideoStream(src=stream_1).start()\nfvs2 = WebcamVideoStream(src=stream_1).start()\nheight = 400\nwidth = 600\nct = CentroidTracker(maxDisappeared=2, maxDistance=45)\ntrackableObjects = {}\ntotalCount = 0\nROI = 350\n#height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n#width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n#fps = int(cap.get(cv2.CAP_PROP_FPS))\n\nscale_w = 1.2 / 2\nscale_h = 4 / 2\n\nSOLID_BACK_COLOR = (41, 41, 41)\n# Setup video writer\n# fourcc = cv2.VideoWriter_fourcc(*\"XVID\")\n# output_movie = cv2.VideoWriter(\n# \"Pedestrian_detect.avi\", fourcc, fps, (width, height))\n# bird_movie = cv2.VideoWriter(\n# \"Pedestrian_bird.avi\", fourcc, fps, (int(\n# width * scale_w), int(height * scale_h))\n# )\n\n# Initialize necessary variables\nframe_num = 0\ntotal_pedestrians_detected = 0\ntotal_six_feet_violations = 0\ntotal_pairs = 0\nabs_six_feet_violations = 0\npedestrian_per_sec = 0\nsh_index = 1\nsc_index = 1\n\ncv2.namedWindow(\"define Region of Interest\")\ncv2.setMouseCallback(\"define Region of Interest\", get_mouse_points)\nnum_mouse_points = 0\nfirst_frame_display = True\n#fps_count = FPS().start()\n\n# Process each frame, until end of video\nwhile True:\n t_dtc = time.time()\n direction_str = \"...\"\n frame1 = fvs1.read()\n frame2 = fvs2.read()\n\n if frame1 is None or frame2 is None:\n continue\n\n frame_num += 1\n H = 400\n W = 600\n\n if frame_num == 1:\n frame1 = imutils.resize(frame1, width=W, height=H)\n print('Please specify ROI for distance measurement.')\n # Ask user to mark parallel points and two points 6 feet apart. Order bl, br, tr, tl, p1, p2\n while True:\n image = frame1\n cv2.imshow(\"define Region of Interest\", image)\n cv2.waitKey(1)\n if len(mouse_pts) == 7:\n cv2.destroyWindow(\"define Region of Interest\")\n break\n first_frame_display = False\n four_points = mouse_pts\n\n # Get perspective\n M, Minv = get_camera_perspective(frame1, four_points[0:4])\n pts = src = np.float32(np.array([four_points[4:]]))\n warped_pt = cv2.perspectiveTransform(pts, M)[0]\n d_thresh = np.sqrt(\n (warped_pt[0][0] - warped_pt[1][0]) ** 2\n + (warped_pt[0][1] - warped_pt[1][1]) ** 2\n )\n bird_image = np.zeros(\n (int(H * scale_h), int(W * scale_w), 3), np.uint8\n )\n\n bird_image[:] = SOLID_BACK_COLOR\n pedestrian_detect = frame1\n\n print(\"Processing frame: \", frame_num)\n\n # draw polygon of ROI\n pts = np.array(\n [four_points[0], four_points[1], four_points[3], four_points[2]], np.int32\n )\n\n # Detect person and bounding boxes using DNN\n pedestrian_boxes, num_pedestrians, frame1 = DNN_distance.detect_distance(\n frame1)\n cv2.polylines(frame1, [pts], True, (0, 255, 255), thickness=4)\n\n if len(pedestrian_boxes) > 0:\n pedestrian_detect = plot_pedestrian_boxes_on_image(\n frame1, pedestrian_boxes)\n warped_pts, bird_image = plot_points_on_bird_eye_view(\n frame1, pedestrian_boxes, M, scale_w, scale_h\n )\n six_feet_violations, ten_feet_violations, pairs = plot_lines_between_nodes(\n warped_pts, bird_image, d_thresh\n )\n # plot_violation_rectangles(pedestrian_boxes, )\n total_pedestrians_detected += num_pedestrians\n total_pairs += pairs\n\n # total_six_feet_violations += six_feet_violations / fps\n # abs_six_feet_violations += six_feet_violations\n # pedestrian_per_sec, sh_index=calculate_stay_at_home_index(\n # total_pedestrians_detected, frame_num, fps\n # )\n\n # last_h=75\n # text=\"# distance violations: \" + str(int(total_six_feet_violations))\n # pedestrian_detect, last_h=put_text(\n # pedestrian_detect, text, text_offset_y=last_h)\n\n # text=\"Stay-at-home Index: \" + str(np.round(100 * sh_index, 1)) + \"%\"\n # pedestrian_detect, last_h=put_text(\n # pedestrian_detect, text, text_offset_y=last_h)\n\n # if total_pairs != 0:\n # sc_index=1 - abs_six_feet_violations / total_pairs\n\n # text=\"Social-distancing Index: \" + str(np.round(100 * sc_index, 1)) + \"%\"\n # pedestrian_detect, last_h=put_text(\n # pedestrian_detect, text, text_offset_y=last_h)\n # te_text=time.time()\n # print('Text: {}'.format(te_text - te_view))\n\n cv2_im, objs = DNN_count.detect_count(frame2)\n cv2_im = cv2.line(cv2_im, (ROI, 0), (ROI, H), (0, 255, 255), 2)\n cv2_im, totalCount, direction_str, trackableObjects = append_objs_to_img(cv2_im, objs, labels, ROI, ct, trackableObjects, totalCount)\n\n info = [\n (\"Direction\", direction_str),\n (\"Count\", totalCount),\n ]\n for (i, (k, v)) in enumerate(info):\n text = \"{}: {}\".format(k, v)\n cv2.putText(cv2_im, text, (10, H - ((i * 20) + 20)),\n cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)\n\n cv2.imshow(\"Camera_distance\", pedestrian_detect)\n cv2.imshow(\"Bird-eye_view\", bird_image)\n cv2.imshow('Camera_count', cv2_im)\n cv2.waitKey(1)\n # output_movie.write(pedestrian_detect)\n # bird_movie.write(bird_image)\n # fps_count.update()\n te_dtc = time.time()\n print('Detection in: {}'.format(t_dtc - te_dtc))\n\nfvs1.stop()\nfvs2.stop()\ncv2.destroyAllWindows()\n\n# fps_count.stop()\n#print(\"[INFO] elapsed time: {:.2f}\".format(fps_count.elapsed()))\n#print(\"[INFO] approx. FPS: {:.2f}\".format(fps_count.fps()))\n\n", "sub_path": "backup_git/DFM_counter/main_tpu.py", "file_name": "main_tpu.py", "file_ext": "py", "file_size_in_byte": 9753, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "os.environ", "line_number": 14, "usage_type": "attribute"}, {"api_name": "cv2.EVENT_LBUTTONDOWN", "line_number": 22, "usage_type": "attribute"}, {"api_name": "cv2.circle", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 44, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 46, "usage_type": "attribute"}, {"api_name": "pyimagesearch.trackableobject.TrackableObject", "line_number": 58, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 92, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 93, "usage_type": "attribute"}, {"api_name": "cv2.circle", "line_number": 94, "usage_type": "call"}, {"api_name": "imutils.video.WebcamVideoStream", "line_number": 111, "usage_type": "call"}, {"api_name": "imutils.video.WebcamVideoStream", "line_number": 112, "usage_type": "call"}, {"api_name": "pyimagesearch.centroidtracker.CentroidTracker", "line_number": 115, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 146, "usage_type": "call"}, {"api_name": "cv2.setMouseCallback", "line_number": 147, "usage_type": "call"}, {"api_name": "time.time", "line_number": 154, "usage_type": "call"}, {"api_name": "imutils.resize", "line_number": 167, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 172, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 173, "usage_type": "call"}, {"api_name": "cv2.destroyWindow", "line_number": 175, "usage_type": "call"}, {"api_name": "cv2.perspectiveTransform", "line_number": 183, "usage_type": "call"}, {"api_name": "cv2.polylines", "line_number": 205, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 245, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 254, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 255, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 257, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 258, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 259, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 260, "usage_type": "call"}, {"api_name": "time.time", "line_number": 264, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 269, "usage_type": "call"}]} +{"seq_id": "394415918", "text": "\"\"\"\ndesispec.scripts.quicklook\n===========================\nCommand line wrapper for running a QL pipeline \n\nS. Kama, G. Dhungana \nSMU\nSpring 2016\n\"\"\"\n\nfrom __future__ import absolute_import, division, print_function\n\nfrom desispec.quicklook import quicklook,qllogger,qlconfig\nimport desispec.image as image\nimport desispec.frame as frame\nimport desispec.io.frame as frIO\nimport desispec.io.image as imIO\n\nimport os,sys\nimport yaml\n\nimport argparse\n\ndef parse():\n \"\"\"\n Should have either a pre existing config file, or need to generate one using config module\n \"\"\"\n parser=argparse.ArgumentParser(description=\"Run QL on DESI data\")\n parser.add_argument(\"-i\", \"--config_file\", type=str, required=False,help=\"yaml file containing config dictionary\",dest=\"config\")\n parser.add_argument(\"-g\", \"--gen_testconfig\", type=str, required=False, help=\"generate test configuration\",dest=\"dotest\")\n parser.add_argument(\"-n\",\"--night\", type=str, required=False, help=\"night for the data\")\n parser.add_argument(\"-c\", \"--camera\", type=str, required=False, help= \"camera for the raw data\")\n parser.add_argument(\"-e\",\"--expid\", type=int, required=False, help=\"exposure id\")\n parser.add_argument(\"-f\",\"--flavor\", type=str, required=False, help=\"flavor of exposure\",default=\"dark\")\n parser.add_argument(\"--psfboot\",type=str,required=False,help=\"psf boot file\")\n parser.add_argument(\"--fiberflat\",type=str, required=False, help=\"fiberflat file\",default=None)\n parser.add_argument(\"--rawdata_dir\", type=str, required=False, help=\"rawdata directory. overrides $DESI_SPECTRO_DATA in config\")\n parser.add_argument(\"--specprod_dir\",type=str, required=False, help=\"specprod directory, overrides $DESI_SPECTRO_REDUX/$SPECPROD in config\")\n parser.add_argument(\"--save\",type=str, required=False,help=\"save this config to a file\")\n parser.add_argument(\"--qlf\",type=str,required=False,help=\"setup for QLF run\", default=False)\n \n args=parser.parse_args()\n return args\n\ndef ql_main(args=None):\n\n qlog=qllogger.QLLogger(\"QuickLook\",20)\n log=qlog.getlog()\n\n if args is None:\n args = parse()\n\n if args.dotest is not None:\n quicklook.testconfig(args.dotest)\n\n if args.config is not None:\n if os.path.exists(args.config):\n if \"yaml\" in args.config:\n configdict=yaml.load(open(args.config,'rb'))\n else:\n log.critical(\"Can't open config file %s\"%(args.config))\n sys.exit(\"Can't open config file\")\n else:\n log.warning(\"No config file given. Trying to create config from other options\")\n PAs=qlconfig.Palist(args.flavor)\n\n config=qlconfig.Make_Config(args.night,args.flavor,args.expid,args.camera, PAs,psfboot=args.psfboot,rawdata_dir=args.rawdata_dir, specprod_dir=args.specprod_dir,fiberflat=args.fiberflat, qlf=args.qlf)\n configdict=qlconfig.build_config(config)\n\n #- save this config to a file\n if args.save:\n if \"yaml\" in args.save:\n yaml.dump(configdict,open(args.save,\"wb\"))\n log.info(\"Output saved for this configuration to %s \"%args.save)\n else:\n log.info(\"Can save config to only yaml output. Put a yaml in the argument\")\n \n pipeline, convdict = quicklook.setup_pipeline(configdict)\n res=quicklook.runpipeline(pipeline,convdict,configdict)\n inpname=configdict[\"RawImage\"]\n camera=configdict[\"Camera\"]\n chan,spectrograph,expid=quicklook.get_chan_spec_exp(inpname,camera=camera) #- may be other ways to get it as well\n\n if isinstance(res,image.Image):\n if configdict[\"OutputFile\"]: finalname=configdict[\"OutputFile\"]\n else: finalname=\"image-%s%d-%08d.fits\"%(chan,spectrograph,expid)\n imIO.write_image(finalname,res,meta=None) \n elif isinstance(res,frame.Frame):\n if configdict[\"OutputFile\"]: finalname=configdict[\"OutputFile\"]\n else: finalname=\"frame-%s%d-%08d.fits\"%(chan,spectrograph,expid)\n frIO.write_frame(finalname,res,header=None)\n else:\n log.error(\"Result of pipeline is in unkown type %s. Don't know how to write\"%(type(res)))\n sys.exit(\"Unknown pipeline result type %s.\"%(type(res)))\n log.info(\"Pipeline completed. Final result is in %s\"%finalname)\nif __name__=='__main__':\n ql_main() \n", "sub_path": "py/desispec/scripts/quicklook.py", "file_name": "quicklook.py", "file_ext": "py", "file_size_in_byte": 4327, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 28, "usage_type": "call"}, {"api_name": "desispec.quicklook.qllogger.QLLogger", "line_number": 47, "usage_type": "call"}, {"api_name": "desispec.quicklook.qllogger", "line_number": 47, "usage_type": "name"}, {"api_name": "desispec.quicklook.quicklook.testconfig", "line_number": 54, "usage_type": "call"}, {"api_name": "desispec.quicklook.quicklook", "line_number": 54, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "yaml.load", "line_number": 59, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 62, "usage_type": "call"}, {"api_name": "desispec.quicklook.qlconfig.Palist", "line_number": 65, "usage_type": "call"}, {"api_name": "desispec.quicklook.qlconfig", "line_number": 65, "usage_type": "name"}, {"api_name": "desispec.quicklook.qlconfig.Make_Config", "line_number": 67, "usage_type": "call"}, {"api_name": "desispec.quicklook.qlconfig", "line_number": 67, "usage_type": "name"}, {"api_name": "desispec.quicklook.qlconfig.build_config", "line_number": 68, "usage_type": "call"}, {"api_name": "desispec.quicklook.qlconfig", "line_number": 68, "usage_type": "name"}, {"api_name": "yaml.dump", "line_number": 73, "usage_type": "call"}, {"api_name": "desispec.quicklook.quicklook.setup_pipeline", "line_number": 78, "usage_type": "call"}, {"api_name": "desispec.quicklook.quicklook", "line_number": 78, "usage_type": "name"}, {"api_name": "desispec.quicklook.quicklook.runpipeline", "line_number": 79, "usage_type": "call"}, {"api_name": "desispec.quicklook.quicklook", "line_number": 79, "usage_type": "name"}, {"api_name": "desispec.quicklook.quicklook.get_chan_spec_exp", "line_number": 82, "usage_type": "call"}, {"api_name": "desispec.quicklook.quicklook", "line_number": 82, "usage_type": "name"}, {"api_name": "desispec.image.Image", "line_number": 84, "usage_type": "attribute"}, {"api_name": "desispec.image", "line_number": 84, "usage_type": "name"}, {"api_name": "desispec.io.image.write_image", "line_number": 87, "usage_type": "call"}, {"api_name": "desispec.io.image", "line_number": 87, "usage_type": "name"}, {"api_name": "desispec.frame.Frame", "line_number": 88, "usage_type": "attribute"}, {"api_name": "desispec.frame", "line_number": 88, "usage_type": "name"}, {"api_name": "desispec.io.frame.write_frame", "line_number": 91, "usage_type": "call"}, {"api_name": "desispec.io.frame", "line_number": 91, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 94, "usage_type": "call"}]} +{"seq_id": "207805402", "text": "# uncompyle6 version 3.7.4\n# Python bytecode 3.6 (3379)\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.macosx-10.6-intel/egg/v_m_t/VolumeInfoBase.py\n# Compiled at: 2020-03-19 15:18:37\n# Size of source mod 2**32: 3815 bytes\nimport abc, collections, logging\nfrom boto.s3.bucket import Bucket\nfrom boto3 import client\nfrom botocore.paginate import Paginator\nfrom .getS3FolderPrefix import get_s3_folder_prefix\nVolInfo = collections.namedtuple('VolInfo', ['imageList', 'imageGroupID'])\nVMT_BUDABOM: str = 'fileList.json'\nVMT_BUDABOM_KEY = 'filename'\nVMT_DIM: str = 'dimensions.json'\nlogger: logging = None\n\nclass VolumeInfoBase(metaclass=abc.ABCMeta):\n __doc__ = '\\n Gets volume info for a work.\\n Passes request off to subclasses\\n '\n boto_client: client = None\n s3_image_bucket: Bucket = None\n boto_paginator: Paginator = None\n\n def __init__(self, boto_client: client, bucket: Bucket):\n \"\"\"\n :param boto_client: context for operations\n :type boto_client: boto3.client\n : param bucket: target container\n :type bucket: boto.s3.bucket.Bucket\n \"\"\"\n self.boto_client = boto_client\n self.boto_paginator = self.boto_client.get_paginator('list_objects_v2')\n self.s3_image_bucket = bucket\n self.logger = logging.getLogger(__name__)\n\n @abc.abstractmethod\n def fetch(self, urlRequest) -> []:\n \"\"\"\n Subclasses implement\n :param urlRequest:\n :return: VolInfo[] with one entry for each image in the image group\n \"\"\"\n pass\n\n def read_bom_from_s3(self, bom_path: str) -> list:\n \"\"\"\n Reads a json file and returns the values with the \"filename\" key as a list of strings\n :param bom_path: full s3 path to BOM\n :return:\n \"\"\"\n import boto3, json\n s3 = boto3.client('s3')\n obj = s3.get_object(Bucket=(self.s3_image_bucket.name), Key=bom_path)\n json_body = json.loads(obj['Body'].read().decode('utf - 8'))\n self.logger.debug('read bom from s3 object size %d json body size %d', len(obj), len(json_body))\n return [x[VMT_BUDABOM_KEY] for x in json_body]\n\n def get_image_names_from_S3(self, work_rid: str, image_group: str) -> []:\n \"\"\"\n get names of the image files (actually, all the files in an image group, regardless\n :param work_rid: work name ex: W1FPl2251\n :param image_group: sub folder (e.g. I1CZ0085)\n :return: str[] should contain ['I1CZ0085001.jpg','I1CZ0085002.jpg'...']\n \"\"\"\n image_list = []\n full_image_group_path = get_s3_folder_prefix(work_rid, image_group)\n bom = self.read_bom_from_s3(full_image_group_path + VMT_BUDABOM)\n if len(bom) > 0:\n self.logger.debug(f\"fetched BOM from BUDA BOM: {len(bom)} entries\")\n return bom\n else:\n page_iterator = self.boto_paginator.paginate(Bucket=(self.s3_image_bucket.name), Prefix=full_image_group_path)\n for page in page_iterator:\n if 'Contents' in page:\n image_list.extend([dat['Key'].replace(full_image_group_path, '') for dat in page['Contents'] if '.json' not in dat['Key']])\n\n self.logger.debug(f\"fetched BOM from S3 list_objects: {len(image_list)} entries.\")\n return image_list", "sub_path": "pycfiles/volume_manifest_tool-1.0.1-py3.6/VolumeInfoBase.cpython-36.py", "file_name": "VolumeInfoBase.cpython-36.py", "file_ext": "py", "file_size_in_byte": 3384, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "collections.namedtuple", "line_number": 13, "usage_type": "call"}, {"api_name": "abc.ABCMeta", "line_number": 19, "usage_type": "attribute"}, {"api_name": "boto3.client", "line_number": 21, "usage_type": "name"}, {"api_name": "boto.s3.bucket.Bucket", "line_number": 22, "usage_type": "name"}, {"api_name": "botocore.paginate.Paginator", "line_number": 23, "usage_type": "name"}, {"api_name": "boto3.client", "line_number": 25, "usage_type": "name"}, {"api_name": "boto.s3.bucket.Bucket", "line_number": 25, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 35, "usage_type": "call"}, {"api_name": "abc.abstractmethod", "line_number": 37, "usage_type": "attribute"}, {"api_name": "boto3.client", "line_number": 53, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 55, "usage_type": "call"}, {"api_name": "getS3FolderPrefix.get_s3_folder_prefix", "line_number": 67, "usage_type": "call"}]} +{"seq_id": "414407552", "text": "#!/user/bin/env python\n# -*- coding: utf-8 -*-\n\n\n# Read a File -> if not exits throw FileNotFoundError > No such file or directory\nimport json\nimport os\nimport pickle\nfrom io import StringIO, BytesIO\n\ntry:\n f = open('ReadFiles/test.txt', 'r')\n print('by read ->', f.read()) # Hello Python\nexcept IOError as e:\n print('IOError :', e)\nfinally:\n # 读取完毕需要关闭 因为文件对象会占用操作系统的资源 并且操作系统同一时间能打开的文件数量也是有限的\n if f:\n f.close()\n\n# ------------------> 等同 <------------------\nwith open('ReadFiles/test.txt', 'r') as f:\n pass\n print('by with ->', f.read())\n\n# 调用 read() 会一次性读取文件的全部内容 , 保险起见 , 可以反复调用read(size)方法 , 每次最多读取size个字节的内容\n# 调用 readline() 可以每次读取一行内容，调用 readlines() 一次读取所有内容并按行返回 list\n# 如果文件很小 , read() 一次性读取最方便\n# 如果不能确定文件大小 , 反复调用 read(size) 比较保险\n# 如果是配置文件，调用 readlines() 最方便\ntry:\n f = open('ReadFiles/test.txt', 'r')\n for line in f.readlines():\n print('by readlines ->', line.strip())\nexcept IOError as e:\n print('IOError:', e)\n\nfinally:\n if f:\n f.close()\n\n# 要读取二进制文件，比如图片、视频等等，用'rb'模式打开文件\ntry:\n f = open('P_Appendix_PythonBuild-inFunction.png', 'rb')\n print('read png rb ->', f.read())\n\nexcept IOError as e:\n print('IOError:', e)\n\nfinally:\n if f:\n f.close()\n\n# 要读取非 utf-8 编码的文本文件，需要给 open() 函数传入 encoding 参数\n# 遇到有些编码不规范的文件 , 会遇到 UnicodeDecodeError , 因为在文本文件中可能夹杂了一些非法编码的字符 -> errors参数\ntry:\n f = open('ReadFiles/Test.py', 'r', encoding='gbk', errors='ignore')\n print(f.read())\n\nexcept IOError as e:\n print('IOError:', e)\nexcept UnicodeDecodeError as e:\n print('UnicodeDecodeError:', e)\n\nfinally:\n if f:\n f.close()\n\n# Write a File\n# 写入特定编码的 需要传入 encoding 参数\ntry:\n f = open('ReadFiles/test.txt', 'w', encoding='gbk')\n f.write('Hello World\\nHello Python')\nexcept IOError as e:\n print('IOError:', e)\nfinally:\n if f:\n # 只有调用 close() 方法时，操作系统才保证把没有写入的数据全部写入磁盘\n # 忘记调用 close() 的后果是数据可能只写了一部分到磁盘，剩下的丢失了\n f.close()\n\n# ------------------> 等同 <------------------\nwith open('ReadFiles/test.txt', 'w', encoding='gbk') as f:\n f.write('Hello World\\nHello Python')\n\n#########################################################################################\n\n# 像 open() 函数返回的这种有个 read() 方法的对象，在 Python 中统称为 file-like Object\n# file-like Object 不要求从特定类继承，只要写个 read() 方法就行\n\n# StringIO => 在内存中读写 str\nf = StringIO()\nf.write('Hello Python')\n# getvalue() 方法用于获得写入后的str\nprint(f.getvalue())\n\n# 读取StringIO\nf = StringIO('ReadFiles/test.txt')\nwhile True:\n s = f.readline()\n if s == '':\n break\n print(s.strip()) # ReadFiles/test.txt\n\n# ByteIO\nf = BytesIO()\n# 写入的不是 str , 而是经过 utf-8 编码的 bytes\nf.write('中文'.encode('utf-8'))\nprint(f.getvalue()) # b'\\xe4\\xb8\\xad\\xe6\\x96\\x87'\n\n#########################################################################################\n\n# 操作系统类型\nos.name\n\n# 获取详细的系统信息 window 不提供 uname()\n# os.uname()\n\n# 查看操作系统中定义的环境变量\nos.environ\n\n# 获取某个环境变量的值\nos.environ.get('key')\n\n# 查看当前目录的绝对路径:\nos.path.abspath('.')\n\n# 在某个目录下创建一个新目录，首先把新目录的完整路径表示出来:\n# os.path.join('E:/PyCharmProject/HelloPython/ReadFiles/test.txt', 'test_dir')\n\n# 创建一个目录\nos.mkdir('Test')\n\n# 删掉一个目录\nos.rmdir('Test')\n\n# 两个路径合成一个\n# os.path.join('...', '...')\n\n# 拆分路径\nos.path.split('ReadFiles/test.txt')\n\n# 得到文件扩展名\nos.path.splitext('ReadFiles/test.txt')\n\n# 对文件重命名\n# os.rename('ReadFiles/test.txt','ReadFiles/test.xml')\n\n# 删掉文件\n# os.remove('ReadFiles/test.txt')\n\n# 过滤文件\nprint([x for x in os.listdir('.') if os.path.isdir(x)]) # ['.git', '.idea', 'ReadFiles', '__pycache__']\n\nprint([x for x in os.listdir('.') if os.path.isfile(x) and os.path.splitext(x)[1] == '.py'])\n\n#########################################################################################\n\n# picking\nd = dict(name='Bob', age=20, score=99)\npickle.dumps(d)\nprint('picking ->', pickle.dumps(d))\nf = open('ReadFiles/dump.txt', 'wb')\npickle.dump(d, f)\nf.close()\n\n# unpicking\nf = open('ReadFiles/dump.txt', 'rb')\n# 这个变量和原来的变量是完全不相干的对象 , 它们只是内容相同而已\nd = pickle.load(f)\nf.close()\nprint('unpicking ->', d)\n\n# python transforms to json\n#\n# json.dumps(obj, *, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True,\n# cls=None, indent=None, separators=None, default=None, sort_keys=False, **kw)\n#\n# json.dump(obj, fp: {write}, *, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True,\n# cls=None, indent=None, separators=None, default=None, sort_keys=False, **kw)\n\nd = dict(name='Bob', age=20, score=99)\npy_js = json.dumps(d)\nprint('python to json ->', py_js)\n\n# json transforms to python\n#\n# json.loads(s:{count , rfind , __len__}, encoding=None, cls=None, object_hook=None,\n# parse_float=None, parse_int=None, parse_constant=None, object_pairs_hook=None, **kw)\n#\n# json.load(fp:{read}, cls=None, object_hook=None, parse_float=None,\n# parse_int=None, parse_constant=None, object_pairs_hook=None, **kw)\n\njson_str = '{\"name\": \"Bob\", \"age\": 20, \"score\": 99}'\njs_py = json.loads(json_str)\nprint('json to python ->', js_py)\n\n\n# class and json transform\nclass Student:\n def __init__(self, name, age, score):\n self.name = name\n self.age = age\n self.score = score\n\n\ndef student2dict(std):\n return {\n 'name': std.name,\n 'age': std.age,\n 'score': std.score\n }\n\n\ndef dict2student(d):\n return Student(d['name'], d['age'], d['score'])\n\n\ns = Student('Bob', 20, 99)\n# print('class to json ->', json.dumps(s, default=student2dict))\nprint('class to json ->', json.dumps(s, default=lambda obj: obj.__dict__))\n# print('json to class ->', json.loads(json_str, object_hook=dict2student))\nprint('json to class ->', json.loads(json_str, object_hook=lambda d: Student(d['name'], d['age'], d['score'])))\n", "sub_path": "P_10_IO.py", "file_name": "P_10_IO.py", "file_ext": "py", "file_size_in_byte": 6748, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "io.StringIO", "line_number": 92, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 98, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 106, "usage_type": "call"}, {"api_name": "os.name", "line_number": 114, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 120, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 123, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 123, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 126, "usage_type": "call"}, {"api_name": "os.path", "line_number": 126, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 132, "usage_type": "call"}, {"api_name": "os.rmdir", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 141, "usage_type": "call"}, {"api_name": "os.path", "line_number": 141, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path", "line_number": 144, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path", "line_number": 153, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 155, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 155, "usage_type": "call"}, {"api_name": "os.path", "line_number": 155, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 155, "usage_type": "call"}, {"api_name": "pickle.dumps", "line_number": 161, "usage_type": "call"}, {"api_name": "pickle.dumps", "line_number": 162, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 164, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 170, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 183, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 195, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 221, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 223, "usage_type": "call"}]} +{"seq_id": "281763004", "text": "#!/usr/bin/python\n\n\nimport datetime\nimport platform\nimport plistlib\nimport re\nimport subprocess\nimport sys\nimport xml.parsers.expat\nfrom distutils.version import StrictVersion\n\nsys.path.insert(0, '/usr/local/sal')\nimport utils\n\n\n__version__ = '1.0.1'\n\n\ndef main():\n sus_submission = {}\n sus_submission['facts'] = get_sus_facts()\n\n # Process managed items and update histories.\n sus_submission['managed_items'] = get_sus_install_report()\n sus_submission['update_history'] = []\n\n pending = get_pending()\n sus_submission['managed_items'].update(pending)\n\n utils.set_checkin_results('Apple Software Update', sus_submission)\n\n\ndef get_sus_install_report():\n \"\"\"Return installed apple updates from softwareupdate\"\"\"\n try:\n history = plistlib.readPlist('/Library/Receipts/InstallHistory.plist')\n except (IOError, xml.parsers.expat.ExpatError):\n history = []\n return {\n i['displayName']: {\n 'date_managed': i['date'],\n 'status': 'PRESENT',\n 'data': {\n 'type': 'Apple SUS Install',\n 'version': i['displayVersion'].strip()\n }\n } for i in history if i['processName'] == 'softwareupdated'}\n\n\ndef get_sus_facts():\n result = {'checkin_module_version': __version__}\n history_limit = datetime.datetime.utcnow() - datetime.timedelta(days=1)\n cmd = ['softwareupdate', '--dump-state']\n try:\n subprocess.check_call(cmd)\n except subprocess.CalledProcessError:\n return result\n\n with open('/var/log/install.log') as handle:\n install_log = handle.readlines()\n\n for line in reversed(install_log):\n # TODO: Stop if we go before the subprocess call datetime-wise\n if 'Catalog: http' in line and 'catalog' not in result:\n result['catalog'] = line.split()[-1]\n elif 'SUScan: Elapsed scan time = ' in line and 'last_check' not in result:\n # Example date 2019-02-08 10:49:56-05\n # Ahhhh, python 2 stdlib... Doesn't support the %z UTC\n # offset correctly.\n\n # So split off UTC offset.\n raw_date = ' '.join(line.split()[:2])\n # and make a naive datetime from it.\n naive = datetime.datetime.strptime(raw_date[:-3], '%Y-%m-%d %H:%M:%S')\n # Convert the offset in hours to an int, including the sign.\n offset = int(raw_date[-3:])\n # Invert the offset by subtracting from the naive datetime.\n last_check_datetime = naive - datetime.timedelta(hours=offset)\n # Finally, convert to ISO format and tack a Z on to show\n # we're using UTC time now.\n result['last_check'] = last_check_datetime.isoformat() + 'Z'\n\n log_time = _get_log_time(line)\n if log_time and log_time < history_limit:\n # Let's not look earlier than when we started\n # softwareupdate.\n break\n\n elif 'catalog' in result and 'last_check' in result:\n # Once we have both facts, bail; no need to process the\n # entire file.\n break\n\n return result\n\n\ndef _get_log_time(line):\n try:\n result = datetime.datetime.strptime(line[:19], '%Y-%m-%d %H:%M:%S')\n except ValueError:\n return None\n utc_result = result - datetime.timedelta(hours=int(line[19:22]))\n return utc_result\n\n\ndef get_pending():\n pending_items = {}\n cmd = ['softwareupdate', '-l', '--no-scan']\n try:\n # softwareupdate outputs \"No new software available\" to stderr,\n # so we pipe it off.\n output = subprocess.check_output(cmd, stderr=subprocess.PIPE)\n except subprocess.CalledProcessError:\n return pending_items\n\n # The following regex code is from Shea Craig's work on the Salt\n # mac_softwareupdate module. Reference that for future updates.\n if StrictVersion(platform.mac_ver()[0]) >= StrictVersion('10.15'):\n # Example output:\n # Software Update Tool\n #\n # Finding available software\n # Software Update found the following new or updated software:\n # * Label: Command Line Tools beta 5 for Xcode-11.0\n # Title: Command Line Tools beta 5 for Xcode, Version: 11.0, Size: 224804K, Recommended: YES,\n # * Label: macOS Catalina Developer Beta-6\n # Title: macOS Catalina Public Beta, Version: 5, Size: 3084292K, Recommended: YES, Action: restart,\n # * Label: BridgeOSUpdateCustomer\n # Title: BridgeOSUpdateCustomer, Version: 10.15.0.1.1.1560926689, Size: 390674K, Recommended: YES, Action: shut down,\n # - Label: iCal-1.0.2\n # Title: iCal, Version: 1.0.2, Size: 6520K,\n rexp = re.compile(\n r'(?m)' # Turn on multiline matching\n r'^\\s*[*-] Label: ' # Name lines start with * or - and \"Label: \"\n r'(?P[^ ].*)[\\r\\n]' # Capture the rest of that line; this is the update name.\n r'.*Version: (?P[^,]*), ' # Grab the version number.\n r'Size: (?P[^,]*),\\s*' # Grab the size; unused at this time.\n r'(?PRecommended: YES,)?\\s*' # Optionally grab the recommended flag.\n r'(?PAction: (?:restart|shut down),)?' # Optionally grab an action.\n )\n else:\n # Example output:\n # Software Update Tool\n #\n # Finding available software\n # Software Update found the following new or updated software:\n # * Command Line Tools (macOS Mojave version 10.14) for Xcode-10.3\n # Command Line Tools (macOS Mojave version 10.14) for Xcode (10.3), 199140K [recommended]\n # * macOS 10.14.1 Update\n # macOS 10.14.1 Update (10.14.1), 199140K [recommended] [restart]\n # * BridgeOSUpdateCustomer\n # BridgeOSUpdateCustomer (10.14.4.1.1.1555388607), 328394K, [recommended] [shut down]\n # - iCal-1.0.2\n # iCal, (1.0.2), 6520K\n rexp = re.compile(\n r'(?m)' # Turn on multiline matching\n r'^\\s+[*-] ' # Name lines start with 3 spaces and either a * or a -.\n r'(?P.*)[\\r\\n]' # The rest of that line is the name.\n r'.*\\((?P[^ \\)]*)' # Capture the last parenthesized value on the next line.\n r'[^\\r\\n\\[]*(?P\\[recommended\\])?\\s?' # Capture [recommended] if there.\n r'(?P\\[(?:restart|shut down)\\])?' # Capture an action if present.\n )\n\n now = datetime.datetime.utcnow().isoformat() + 'Z'\n return {\n m.group('name'): {\n 'date_managed': now,\n 'status': 'PENDING',\n 'data': {\n 'version': m.group('version'),\n 'recommended': 'TRUE' if 'recommended' in m.group('recommended') else 'FALSE',\n 'action': _bracket_cleanup(m, 'action')\n }\n } for m in rexp.finditer(output)\n }\n\n\ndef _bracket_cleanup(match, key):\n \"\"\"Strip out [ and ] and uppercase SUS output\"\"\"\n return re.sub(r'[\\[\\]]', '', match.group(key) or '').upper()\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "payload/usr/local/sal/checkin_modules/apple_sus_checkin.py", "file_name": "apple_sus_checkin.py", "file_ext": "py", "file_size_in_byte": 7132, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sys.path.insert", "line_number": 13, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "utils.set_checkin_results", "line_number": 31, "usage_type": "call"}, {"api_name": "plistlib.readPlist", "line_number": 37, "usage_type": "call"}, {"api_name": "xml.parsers.expat.parsers", "line_number": 38, "usage_type": "attribute"}, {"api_name": "xml.parsers.expat", "line_number": 38, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 53, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 53, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 53, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 56, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 57, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 75, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 75, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 79, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 100, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 100, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 103, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 113, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 113, "usage_type": "attribute"}, {"api_name": "subprocess.CalledProcessError", "line_number": 114, "usage_type": "attribute"}, {"api_name": "distutils.version.StrictVersion", "line_number": 119, "usage_type": "call"}, {"api_name": "platform.mac_ver", "line_number": 119, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 133, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 156, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 165, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 165, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 181, "usage_type": "call"}]} +{"seq_id": "931174", "text": "import os\n\nfrom colorama import Fore\nfrom rtxlib import info, error, current_milli_time\nfrom rtxlib.execution import clusteringExperimentFunction\nfrom rtxlib.clustering_tools import run_model, partial_clustering, write_description, write_raw_data, write_samples\n\nfrom sklearn.cluster import Birch\nimport numpy as np\nimport copy\n#import wandb\n\n\ndef start_clustering_strategy(wf):\n \"\"\" executes all experiments from the definition file \"\"\"\n\n info(\"> ExecStrategy | BIRCH Clustering\", Fore.CYAN)\n wf.totalExperiments = wf.execution_strategy[\"sample_size\"]\n \n start_time = current_milli_time()\n\n folder = wf.execution_strategy['save_in']\n os.makedirs(os.path.dirname(folder), exist_ok=True)\n\n sample_size = wf.execution_strategy[\"sample_size\"]\n partial_clustering_size = wf.execution_strategy['partial_clustering_sample_size']\n \n feature_array = [\n 'index',\n 'totalCarNumber',\n 'numberOfTrips',\n 'median_overhead',\n 'q1_overhead',\n 'q3_overhead',\n 'p9_overhead',\n ]\n \n features_for_raw_data = [\n # 'tick',\n 'startCarNumber',\n 'totalCarNumber',\n 'overhead',\n 'duration',\n ]\n\n #wandb.init(project='CrowdNav_BIRCH_clustering', name='Repetition4')\n birchModel = Birch(n_clusters=None, threshold=0.1)\n\n number_of_submodels_trained = 1\n\n data = []\n data_for_partial_clustering = []\n sample_number = 0\n\n write_raw_data(None, folder,features_for_raw_data, header=True)\n write_samples(None, folder, feature_array, header = True)\n\n while sample_number < sample_size:\n result, new_sample= clusteringExperimentFunction(sample_number, folder, wf, {\n \"ignore_first_n_results\": wf.execution_strategy['ignore_first_n_ticks'],\n \"window_size\": wf.execution_strategy['ticks_per_sample'],\n }) \n\n if new_sample is not None:\n sample_number += 1\n data_for_partial_clustering.append(new_sample)\n write_samples(new_sample, folder, feature_array, False)\n \n # Configure the maximum number of samples stored in memory\n # if len(data) == 50:\n # data.pop(0)\n # data.append(new_sample)\n # else:\n # data.append(new_sample)\n\n data.append(new_sample)\n\n # run partial clustering when the specified number of samples was created \n if len(data_for_partial_clustering) == partial_clustering_size:\n cpy_data = copy.deepcopy(data)\n partial_clustering(birchModel, cpy_data, data_for_partial_clustering, feature_array, folder, number_of_submodels_trained)\n data_for_partial_clustering = []\n number_of_submodels_trained += 1\n\n # at the end of the gathering process, if there is still data left for parial clustering, cluster it.\n if sample_number % partial_clustering_size != 0:\n cpy_data = copy.deepcopy(data)\n partial_clustering(birchModel, cpy_data, data_for_partial_clustering, feature_array, folder, number_of_submodels_trained)\n data_for_partial_clustering = []\n number_of_submodels_trained += 1\n \n # run the global clustering\n run_model(birchModel, data, 'final_', folder)\n \n duration = current_milli_time() - start_time\n\n write_description(duration, feature_array, folder, wf)\n\n", "sub_path": "rtxlib/executionstrategy/ClusteringStrategy.py", "file_name": "ClusteringStrategy.py", "file_ext": "py", "file_size_in_byte": 3434, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "rtxlib.info", "line_number": 17, "usage_type": "call"}, {"api_name": "colorama.Fore.CYAN", "line_number": 17, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 17, "usage_type": "name"}, {"api_name": "rtxlib.current_milli_time", "line_number": 20, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "sklearn.cluster.Birch", "line_number": 47, "usage_type": "call"}, {"api_name": "rtxlib.clustering_tools.write_raw_data", "line_number": 55, "usage_type": "call"}, {"api_name": "rtxlib.clustering_tools.write_samples", "line_number": 56, "usage_type": "call"}, {"api_name": "rtxlib.execution.clusteringExperimentFunction", "line_number": 59, "usage_type": "call"}, {"api_name": "rtxlib.clustering_tools.write_samples", "line_number": 67, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 80, "usage_type": "call"}, {"api_name": "rtxlib.clustering_tools.partial_clustering", "line_number": 81, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 87, "usage_type": "call"}, {"api_name": "rtxlib.clustering_tools.partial_clustering", "line_number": 88, "usage_type": "call"}, {"api_name": "rtxlib.clustering_tools.run_model", "line_number": 93, "usage_type": "call"}, {"api_name": "rtxlib.current_milli_time", "line_number": 95, "usage_type": "call"}, {"api_name": "rtxlib.clustering_tools.write_description", "line_number": 97, "usage_type": "call"}]} +{"seq_id": "410280932", "text": "import argparse\r\nimport re\r\nfrom os import system\r\nfrom sys import exc_info\r\n\r\ndef nth_repl(s, sub, repl, nth):\r\n find = s.find(sub)\r\n i = find != -1\r\n while find != -1 and i != nth:\r\n find = s.find(sub, find + 1)\r\n i += 1\r\n if i == nth:\r\n return s[:find]+repl+s[find + len(sub):]\r\n return s\r\n\r\n\r\nparser = argparse.ArgumentParser()\r\nparser.add_argument('input_file', help='the source file to parse')\r\nargs = parser.parse_args()\r\n\r\nnew_url_pattern = 'http.*://www.moneysavingexpert.com'\r\nold_url_pattern = 'https://author.prod1.gb.moneysavingexpert.com/editor.html/content/mse/msecom/en-gb/home'\r\nnew_urls = []\r\nold_urls = []\r\nflag = False\r\n\r\ntry:\r\n with open(args.input_file, 'r') as in_file:\r\n for line in in_file:\r\n line = line.replace('\\n', '')\r\n line = nth_repl(line, '.html', '', 2)\r\n if '#' in line:\r\n flag = True\r\n elif flag:\r\n old_urls.append(re.sub(old_url_pattern, '', line))\r\n else:\r\n new_urls.append(re.sub(new_url_pattern, '', line))\r\n\r\nexcept FileNotFoundError:\r\n print('file {} doesn\\'t exist'.format(args.input_file))\r\nexcept:\r\n print(\"Unexpected error:\", exc_info()[0])\r\n raise\r\n\r\nif len(old_urls) != len(new_urls):\r\n print('Error, number of old and new urls isn\\'t equal')\r\n exit()\r\n\r\nfor i, j in enumerate(new_urls):\r\n if new_urls[i] != old_urls[i]:\r\n print('\\n', i+2, '\\n', new_urls[i], '\\n', old_urls[i], '\\n')\r\n\r\n#system('cat {}'.format(args.output_file))\r\n", "sub_path": "compare.py", "file_name": "compare.py", "file_ext": "py", "file_size_in_byte": 1550, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 17, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 35, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 37, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "15599326", "text": "# coding=utf-8\n# --------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for\n# license information.\n#\n# Code generated by Microsoft (R) AutoRest Code Generator.\n# Changes may cause incorrect behavior and will be lost if the code is\n# regenerated.\n# --------------------------------------------------------------------------\n\nfrom msrest.serialization import Model\n\n\nclass JSONUtterance(Model):\n \"\"\"Exported Model - Utterance that was used to train the model.\n\n :param text: The utterance.\n :type text: str\n :param intent: The matched intent.\n :type intent: str\n :param entities: The matched entities.\n :type entities:\n list[~azure.cognitiveservices.language.luis.authoring.models.JSONEntity]\n \"\"\"\n\n _attribute_map = {\n 'text': {'key': 'text', 'type': 'str'},\n 'intent': {'key': 'intent', 'type': 'str'},\n 'entities': {'key': 'entities', 'type': '[JSONEntity]'},\n }\n\n def __init__(self, *, text: str=None, intent: str=None, entities=None, **kwargs) -> None:\n super(JSONUtterance, self).__init__(**kwargs)\n self.text = text\n self.intent = intent\n self.entities = entities\n", "sub_path": "azure-cognitiveservices-language-luis/azure/cognitiveservices/language/luis/authoring/models/json_utterance_py3.py", "file_name": "json_utterance_py3.py", "file_ext": "py", "file_size_in_byte": 1305, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "msrest.serialization.Model", "line_number": 15, "usage_type": "name"}]} +{"seq_id": "492305522", "text": "from django.conf.urls import url\n\nfrom .views import (\n VideoDetail,\n VideoUpdate,\n VideoList,\n VideoCreate,\n RawWeddingVideoList,\n RawMitzvahVideoList,\n RawCorporateVideoList,\n VideoAdminDetail,\n get_raw_mitzvah_videos,\n get_raw_wedding_videos,\n get_raw_corporate_videos\n)\n\n\napp_name = 'videos'\nurlpatterns = [\n\n url(r'^list/$', VideoList.as_view(), name='video-list'),\n url(r'^create/$', VideoCreate.as_view(), name='video-create'),\n url(r'^admin/(?P[0-9]+)/$', VideoAdminDetail.as_view(), name='video-admin-detail'),\n url(r'^(?P[-\\w]+)/$', VideoDetail.as_view(), name='video-detail'),\n url(r'^(?P[0-9]+)/update/$', VideoUpdate.as_view(), name='video-update'),\n url(r'^api/raw/wedding/$', get_raw_wedding_videos),\n url(r'^raw/wedding/$', RawWeddingVideoList.as_view(), name='raw-wedding-video-list'),\n url(r'^api/raw/mitzvah/$', get_raw_mitzvah_videos),\n url(r'^raw/mitzvah/$', RawMitzvahVideoList.as_view(), name='raw-mitzvah-video-list'),\n url(r'^api/raw/corporate/$', get_raw_corporate_videos),\n url(r'^raw/corporate/$', RawCorporateVideoList.as_view(), name='raw-corporate-video-list'),\n\n]\n", "sub_path": "videos/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1176, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "django.conf.urls.url", "line_number": 21, "usage_type": "call"}, {"api_name": "views.VideoList.as_view", "line_number": 21, "usage_type": "call"}, {"api_name": "views.VideoList", "line_number": 21, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 22, "usage_type": "call"}, {"api_name": "views.VideoCreate.as_view", "line_number": 22, "usage_type": "call"}, {"api_name": "views.VideoCreate", "line_number": 22, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 23, "usage_type": "call"}, {"api_name": "views.VideoAdminDetail.as_view", "line_number": 23, "usage_type": "call"}, {"api_name": "views.VideoAdminDetail", "line_number": 23, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 24, "usage_type": "call"}, {"api_name": "views.VideoDetail.as_view", "line_number": 24, "usage_type": "call"}, {"api_name": "views.VideoDetail", "line_number": 24, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 25, "usage_type": "call"}, {"api_name": "views.VideoUpdate.as_view", "line_number": 25, "usage_type": "call"}, {"api_name": "views.VideoUpdate", "line_number": 25, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 26, "usage_type": "call"}, {"api_name": "views.get_raw_wedding_videos", "line_number": 26, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 27, "usage_type": "call"}, {"api_name": "views.RawWeddingVideoList.as_view", "line_number": 27, "usage_type": "call"}, {"api_name": "views.RawWeddingVideoList", "line_number": 27, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 28, "usage_type": "call"}, {"api_name": "views.get_raw_mitzvah_videos", "line_number": 28, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 29, "usage_type": "call"}, {"api_name": "views.RawMitzvahVideoList.as_view", "line_number": 29, "usage_type": "call"}, {"api_name": "views.RawMitzvahVideoList", "line_number": 29, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 30, "usage_type": "call"}, {"api_name": "views.get_raw_corporate_videos", "line_number": 30, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 31, "usage_type": "call"}, {"api_name": "views.RawCorporateVideoList.as_view", "line_number": 31, "usage_type": "call"}, {"api_name": "views.RawCorporateVideoList", "line_number": 31, "usage_type": "name"}]} +{"seq_id": "550879985", "text": "from django.urls import reverse\n\n\nclass InlineEditLinkMixin(object):\n readonly_fields = ['edit_details']\n edit_label = \"Edit\"\n\n def edit_details(self, obj):\n if obj.id:\n opts = self.model._meta\n return \"%s\" % (reverse(\n 'admin:%s_%s_change' % (opts.app_label, opts.object_name\n .lower()),\n args=[obj.id]\n ), self.edit_label)\n else:\n return \"(save to edit details)\"\n edit_details.allow_tags = True\n", "sub_path": "app/core/mixims/inlineeditlink.py", "file_name": "inlineeditlink.py", "file_ext": "py", "file_size_in_byte": 569, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "django.urls.reverse", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "214711730", "text": "from flask import Flask, jsonify, request\n\napp = Flask(__name__)\n\nframeworks = [\n {\n \"id\":1,\n \"name\":\"flask\"\n },\n {\n \"id\":2,\n \"name\":\"ExpressJS\"\n },\n {\n \"id\":3,\n \"name\":\"Laravel\"\n }\n ]\n\n@app.route(\"/\")\ndef index():\n return \"Hola mundo\"\n\n@app.route(\"/api/frameworks/\",methods=[\"GET\"])\ndef get_framework():\n return jsonify(frameworks)\n\n@app.route(\"/api/frameworks/\")\ndef get_framework_by_name(name):\n framework = []\n for f in frameworks:\n if f[\"name\"] == name:\n framework.append(f)\n return jsonify(framework[0])\n\"\"\"\n@app.route(\"/api/frameworks/id/\")\ndef get_framework_by_id(id):\n framework = []\n for f in frameworks:\n if f[\"id\"] == id:\n framework.append(f)\n return jsonify(framework[0])\n\"\"\"\n@app.route(\"/api/frameworks/\", methods=[\"POST\"])\ndef add_framework():\n #framework = request.json\n framework={\n \"id\":request.json[\"id\"],\n \"name\":request.json[\"name\"]\n }\n frameworks.append(framework)\n return jsonify(framework)\n\n@app.route(\"/api/frameworks/\",methods=[\"PUT\"])\ndef edit_frameworks(id):\n framework = [framework for framework in frameworks if framework[\"id\"] == id]\n if (len(framework)==1):\n framework = framework[0]\n framework[\"id\"]= request.json[\"id\"]\n framework[\"name\"]=request.json[\"name\"]\n return jsonify(framework)\n return \"ERROR, EL ELEMENTO SE REPITE\"\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n\n", "sub_path": "methods/put/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1546, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "flask.Flask", "line_number": 3, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 48, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 48, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 49, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 49, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 52, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 59, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 59, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 60, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 60, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "276934975", "text": "#given directed, weighted graph N nodes\n#Q questions of shortest dist between 2 nodes\n#can be multiple edges between same nodes\n\nfrom copy import deepcopy\nfrom collections import defaultdict\nclass Graph():\n\n def __init__(self,N):\n self.size = N\n self.edges = {}\n self.g = defaultdict(set)\n\n def add(self,n1,n2, w):\n #directed graph\n self.g[n1].add(n2)\n if (n1,n2) in self.edges:\n w1 = self.edges[(n1,n2)]\n if w < w1:\n self.edges[(n1,n2)] = w\n else:\n self.edges[(n1,n2)] = w\n #self.edges[(n1,n2)].append(w)\n\n def bellmanford(self,s):\n distances = [10000000]*(self.size+1)\n parents = [-1]*(self.size)\n\n distances[s] = 0\n #parents[s] = 0\n #O(V*E)\n for i in range(self.size-1):\n for tuple, w in self.edges.items():\n u,v = tuple\n #relax\n if distances[v] > distances[u] + w:\n distances[v] = distances[u] + w\n #parents[v] = u\n #detect for cycles\n for edge, w in self.edges.items():\n u,v = edge\n if distances[v] > distances[u] +w: #u got decreased through some means\n return [-1]\n\n return distances#, parents\n\n\n def floyd(self, a,b):\n distances = self.bellmanford(a)\n return distances[b]\n\nclass Graph2():\n\n def __init__(self,N):\n self.size = N\n self.am = [[10000000]*(N+1) for x in range(N+1)]\n\n def add(self,n1, n2,w):\n self.am[n1][n2] = w\n\n def floyd_warshall(self):\n n = self.size\n D = deepcopy(self.am)\n for k in range(1,n+1):\n for i in range(1,n+1):\n for j in range(1,n+1):\n print(i,j,k)\n #print(D)\n print(\"ds\", D[i][j], D[i][k], D[k][j])\n x = min(D[i][j], D[i][k] + D[k][j])\n D[i][j] = x\n print(D)\n return D\n\n\n\n\n\n\ndef make_graph():\n input_string = \"4 5\\n1 2 5\\n 1 4 24\\n2 4 6\\n3 4 4\\n3 2 7\"\n input_list = input_string.split(\"\\n\")\n N,E = input_list.pop(0).split()\n N,E = [int(N), int(E)]\n g = Graph2(N)\n for a in range(E):\n x, y, r = input_list.pop(0).strip().split()\n x,y,r = [int(x), int(y), int(r)]\n g.add(x,y,r)\n return g\n\ndef get_queries():\n input_string = \"3\\n1 2\\n3 1\\n1 4\"\n input_list = input_string.split(\"\\n\")\n q = input_list.pop(0)\n queries = []\n q = int(q)\n for a in range(q):\n a,b = input_list.pop(0).strip().split()\n a,b = [int(a), int(b)]\n queries.append((a,b))\n return queries\n\n#import numpy as np\ndef test_city():\n g = make_graph()\n #print(g.edges)\n #print(g.g)\n queries = get_queries()\n dists = []\n print(())\n\n D = g.floyd_warshall()\n for a,b in queries:\n #dist = g.floyd(a,b)\n dist = D[a][b]\n dists.append(dist)\n\n print(dists)\n print(dists == [5,-1,11])\n\ntest_city()", "sub_path": "hackerrank/graph_theory/CityOfBlindingLights.py", "file_name": "CityOfBlindingLights.py", "file_ext": "py", "file_size_in_byte": 3039, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "collections.defaultdict", "line_number": 12, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "343232069", "text": "# script to turn w outputs into netcdf\nimport os\nimport numpy as np\nimport datetime\nfrom netCDF4 import Dataset\n\nnum_lat = 158\nnum_lon = 122\nlevels = 1\ndates = 1356\n#change to the path to where w file is in (omega-->w)\nw_path = '/Users/brownscholar/Desktop/AMNH2021Internship/ocean-motion-2021/boop/omega/w'#path to w files (created by fortran code)\n\n\n#change the path to the full data list\ndate_list = open('/Users/brownscholar/Desktop/AMNH2021Internship/ocean-motion-2021/dates/3-23/date_list-1.txt','r')\n\nw_array = np.zeros((dates,num_lat,num_lon,levels))#creates blank array to fill\n\nfor d in range(0,dates):#loops through number of dates\n#take out the s_ and if your w file ends in gr its fine, but mine ended in .txt\n\tfilename = (date_list.readline()).strip('\\n')+\"_ww.txt\"#creates file name from date_list\n\tprint(filename)\n\tif os.path.isfile(w_path+filename):\n\t\tw_file = open(w_path+filename,\"r\")#opens w file\n\t\tw_file.readline()#skips the header in w file\n\t\tw_file.readline()#skips the header in w file\n\t\tfor i in range(0,levels):#loops through depth\n\t\t\tfor j in range(0,num_lat):#loops through latitude\n\t\t\t\tfor k in range(0,num_lon):#loops through longitude\n\t\t\t\t\tw = w_file.readline()\n\t\t\t\t\tw_array[d,j,k,i] = float(w)# takes value from w file and puts it into numpy array\n\t\t\t\t\t#print(w)\n\n\nlatitude_val = np.arange(12.625+.5,51.875-0.25,0.25)#creates numpy array with all of the latitude values\nlongitude_val = np.arange(311.875+.5,342.125-0.25,0.25)\ntime_val = np.arange(377064,604704+168,168)\nprint(time_val.size)\n#change to desktop\n#change path to some where you want the nc file to be\ngrp = Dataset('/Users/brownscholar/Desktop/atlantic_data_1993-2018.nc','w', format='NETCDF4')# opens netcdf file\n#creates dimensions in netcdf file\ngrp.createDimension('lon', num_lon-4)\ngrp.createDimension('lat', num_lat-4)\ngrp.createDimension('depth', levels)\ngrp.createDimension('time', dates)\n#creates variables in numpy array\nlongitude = grp.createVariable('longitude', 'f4', 'lon')#f4 means that the values will be floats\nlatitude = grp.createVariable('latitude', 'f4', 'lat')#f4 means that the values will be floats\ndepth = grp.createVariable('depth', 'f4', 'depth')\ntime = grp.createVariable('time','f4', 'time')\nw = grp.createVariable('w', 'f4', ('time', 'lat', 'lon', 'depth'))\n#fills the variables in the netcdf file with the values from the numpy arrays\nlongitude[:] = longitude_val\nlatitude[:] = latitude_val\ntime[:] = time_val\ndepth[:]= [1]\n\nw[:] = w_array[:,2:156,2:120,:]\n\ntime.units = 'hours since 1950-01-01'#adds units to netcdf variables\nlatitude.units = 'degrees_north'#adds units to netcdf variables\ndepth.units = 'm'#adds units to netcdf variables\ndepth.positive ='down'\ndepth.axis = 'Z'\n\nw.units = 'm/day'#adds units to netcdf variables\n\ngrp.close()\n", "sub_path": "dates/4-6/w_to_netcdf.py", "file_name": "w_to_netcdf.py", "file_ext": "py", "file_size_in_byte": 2770, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.zeros", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 38, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "187943340", "text": "from django.conf.urls import url,include\nfrom django.contrib import admin\nfrom django.conf.urls.static import static\nfrom django.conf import settings\nfrom . import views\n\nurlpatterns = [\n url(r'^admin/', admin.site.urls),\n url(r'^$',views.principal,name='principal'),\n url(r'^obtenermail/',views.send_email,name='send_email'),\n url(r'^login/',include('login.urls',namespace='login')),\n url(r'^cita/',include('agenda.urls',namespace='agenda')),\n url(r'^asesor/',include('asesor.urls',namespace='asesor')),\n url(r'^ventas/',include('ventas.urls',namespace='ventas')),\n url(r'^sesion1/',include('sesion1.urls',namespace='sesion1')),\n url(r'^sesion2/',include('sesion2.urls',namespace='sesion2')),\n url(r'^cliente/',include('cliente.urls',namespace='cliente')),\n url(r'^general/',include('creditos.urls',namespace='conekta')),\n url(r'^mensajes/',include('mensajes.urls',namespace='mensajes')),\n url(r'^productos/',include('productos.urls',namespace='producto')),\n url(r'^reportes/',include('reportes.urls', namespace='generacion')),\n]\n\nif settings.DEBUG:\n urlpatterns += static(settings.STATIC_URL, document_root=settings.STATIC_ROOT)\n urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n", "sub_path": "vive/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1257, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 8, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 16, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 17, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 18, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 18, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 19, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 19, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 20, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 20, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 21, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 21, "usage_type": "call"}, {"api_name": "django.conf.settings.DEBUG", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 24, "usage_type": "name"}, {"api_name": "django.conf.urls.static.static", "line_number": 25, "usage_type": "call"}, {"api_name": "django.conf.settings.STATIC_URL", "line_number": 25, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 25, "usage_type": "name"}, {"api_name": "django.conf.settings.STATIC_ROOT", "line_number": 25, "usage_type": "attribute"}, {"api_name": "django.conf.urls.static.static", "line_number": 26, "usage_type": "call"}, {"api_name": "django.conf.settings.MEDIA_URL", "line_number": 26, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 26, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 26, "usage_type": "attribute"}]} +{"seq_id": "184489001", "text": "#!/usr/bin/env python\n\nfrom bs4 import BeautifulSoup\nimport urllib.request\nfrom flask import Flask\nfrom flask import request\n\napp = Flask(__name__)\n\n@app.route('/')\ndef get():\n stop = request.args.get('stop')\n route = request.args.get('route')\n\n url = 'http://199.191.49.179/where/iphone/stop.action?id=1_' + \\\n stop + \\\n '&route=1_' + \\\n route\n\n raw_html = urllib.request.urlopen(url).read()\n html = BeautifulSoup(raw_html)\n\n html.find('div', id='topBar' ).extract()\n html.find('div', class_='arrivalsFilterPanel' ).extract()\n html.find('div', id='nearby_stops' ).extract()\n html.find('div', class_='agenciesSection' ).extract()\n for ul in html.find_all('ul', class_='buttons' ):\n ul.extract()\n\n for tr in html.find_all('tr', class_='arrivalsRow'):\n td = tr.find('td', class_='arrivalsStatusEntry')\n\n try:\n if int(td.string) < 0:\n print('--> Removing negative time: ' + td.string)\n tr.extract()\n except:\n print('--> Ignoring non-int: ' + td.string)\n None\n\n return str(html)\n\n@app.route('/')\ndef catchall(path):\n url = 'http://199.191.49.179/' + request.url\n return urllib.request.urlopen(url).read()\n\nif __name__ == '__main__':\n app.run(host=\"0.0.0.0\", port=5000)\n", "sub_path": "oba_proxy/index.py", "file_name": "index.py", "file_ext": "py", "file_size_in_byte": 1328, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 12, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 12, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 13, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 13, "usage_type": "name"}, {"api_name": "urllib.request.request.urlopen", "line_number": 20, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 20, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 20, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.request.url", "line_number": 45, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 45, "usage_type": "name"}, {"api_name": "urllib.request.request.urlopen", "line_number": 46, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 46, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 46, "usage_type": "name"}]} +{"seq_id": "387169782", "text": "#!/usr/bin/env python3\n\nfrom PIL import Image\nimport sys\nimport usb1\nfrom adepttool.device import get_devices\n\nimg = Image.open(sys.argv[1])\nW = 320\nH = 200\n\nwith usb1.USBContext() as ctx:\n devs = get_devices(ctx)\n if not devs:\n print('No devices found.')\n sys.exit(1)\n dev = devs[0]\n dev.start()\n port = dev.depp_ports[0]\n port.enable()\n\n written_bytes = []\n\n for y in range(H):\n port.put_reg(2, [y])\n for x in range(0, W, 8):\n port.put_reg(0, [x % 256])\n port.put_reg(1, [x // 256])\n bits = 0\n for dx in range(8):\n bits += (1 if not img.getpixel((x + dx, y)) else 0) * 2 ** dx\n port.put_reg(0x0e, [bits])\n written_bytes.append(bits)\n\n read_bytes = []\n\n tab_idx = 0\n for y in range(H):\n port.put_reg(2, [y])\n for x in range(0, W, 8):\n port.put_reg(0, [x % 256])\n port.put_reg(1, [x // 256])\n read_bytes.append(port.get_reg(0x0e, 1)[0])\n\n if read_bytes != written_bytes:\n for num, (w, r) in enumerate(zip(written_bytes, read_bytes)):\n if w != r:\n print (\"{}: expected: {}, got: {}\".format(num, w, r))\n\n port.disable()\n\n", "sub_path": "adepttool/display_image.py", "file_name": "display_image.py", "file_ext": "py", "file_size_in_byte": 1247, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "PIL.Image.open", "line_number": 8, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 8, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 8, "usage_type": "attribute"}, {"api_name": "usb1.USBContext", "line_number": 12, "usage_type": "call"}, {"api_name": "adepttool.device.get_devices", "line_number": 13, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "319050463", "text": "from django.shortcuts import render\nfrom rest_framework import generics, permissions\nfrom server.serializers import *\nfrom django.contrib.auth import get_user_model\nfrom rest_framework.response import Response\nfrom rest_framework import status\nfrom .models import Profile, Messages\nimport logging\n\nlogger = logging.getLogger(__name__)\nUser = get_user_model()\n\nclass RegisterView(generics.GenericAPIView):\n global logger\n serializer_class = UserProfileSerializer\n def post(self, request):\n serializer = UserProfileSerializer(data=request.data)\n logger.info(\"serializer: {}\".format(serializer))\n logger.info(\"serializer.is_valid(): {}\".format(serializer.is_valid()))\n if serializer.is_valid():\n serializer.save()\n return Response({'_message': 'OK'}, status=status.HTTP_201_CREATED)\n \n custom_error_messages = []\n \n if 'username' in serializer.errors:\n custom_error_messages.append({'_message':\"Username is already in use.\"})\n if 'email' in serializer.errors:\n custom_error_messages.append({'_message':\"Email is already in use.\"})\n if 'profile' in serializer.errors:\n custom_error_messages.append({'_message':\"Passport is already in use.\"})\n \n return Response(custom_error_messages, status=status.HTTP_400_BAD_REQUEST)\n\nclass MessageView(generics.GenericAPIView):\n global logger\n serializer_class = ChatSerializer\n def post(self, request):\n serializer = ChatSerializer(data=request.data, context={'request':request})\n logger.info(\"serializer: {}\".format(serializer))\n if serializer.is_valid():\n serializer.save()\n return Response({'_message': 'OK'}, status=status.HTTP_201_CREATED)\n\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\nclass MessageListView(generics.ListAPIView):\n permission_classes = [permissions.IsAuthenticated]\n serializer_class = MessageDetailSerializer\n lookup_field = \"chat_id\"\n queryset = Messages.objects.all()\n\n def get_queryset(self):\n return Messages.objects.filter(chat_id=self.kwargs['chat_id'])\n\nclass UserCreateView(generics.CreateAPIView):\n permission_classes = [permissions.IsAdminUser]\n serializer_class = UserDetailSerializer\n\nclass UserDetailView(generics.RetrieveUpdateDestroyAPIView):\n permission_classes = [permissions.IsAdminUser]\n serializer_class = UserDetailSerializer\n queryset = User.objects.all()\n\nclass UserListView(generics.ListAPIView):\n permission_classes = [permissions.IsAdminUser]\n serializer_class = UserListSerializer\n queryset = User.objects.all()\n \nclass CreateChatView(generics.GenericAPIView):\n permission_classes = [permissions.IsAuthenticated]\n serializer_class = UserDetailSerializer\n\nclass VirtualAccountView(generics.RetrieveAPIView):\n permission_classes = [permissions.IsAuthenticated]\n serializer_class = VirtualAccountDetailSerializer\n queryset = VirtualAccount.objects.all()\n\nclass VirtualAccountListView(generics.ListAPIView):\n permission_classes = [permissions.IsAuthenticated]\n serializer_class = VirtualAccountDetailSerializer\n queryset = VirtualAccount.objects.all()\n\nclass CreateVirtualAccountView(generics.CreateAPIView):\n permission_classes = [permissions.IsAuthenticated]\n serializer_class = CreateVirtualAccountSerializer\n\n def post(self, request):\n serializer = CreateVirtualAccountSerializer(data=request.data)\n logger.info(\"serializer: {}\".format(serializer))\n if serializer.is_valid():\n serializer.save()\n return Response({'_message': 'OK'}, status=status.HTTP_201_CREATED)\n \n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\nclass RetrieveUpdateVirtualAccountView(generics.RetrieveUpdateAPIView):\n permission_classes = [permissions.IsAuthenticated]\n serializer_class = VirtualAccountDetailSerializer\n queryset = VirtualAccount.objects.all()\n\n\n\n\n", "sub_path": "2020/src/server/django_server/api_server/server/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4010, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 11, "usage_type": "call"}, {"api_name": "rest_framework.generics.GenericAPIView", "line_number": 13, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 13, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 22, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 22, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 22, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 33, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 33, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 33, "usage_type": "name"}, {"api_name": "rest_framework.generics.GenericAPIView", "line_number": 35, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 35, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 43, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 43, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 43, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 45, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 45, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 45, "usage_type": "name"}, {"api_name": "rest_framework.generics.ListAPIView", "line_number": 47, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 47, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 48, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 48, "usage_type": "name"}, {"api_name": "models.Messages.objects.all", "line_number": 51, "usage_type": "call"}, {"api_name": "models.Messages.objects", "line_number": 51, "usage_type": "attribute"}, {"api_name": "models.Messages", "line_number": 51, "usage_type": "name"}, {"api_name": "models.Messages.objects.filter", "line_number": 54, "usage_type": "call"}, {"api_name": "models.Messages.objects", "line_number": 54, "usage_type": "attribute"}, {"api_name": "models.Messages", "line_number": 54, "usage_type": "name"}, {"api_name": "rest_framework.generics.CreateAPIView", "line_number": 56, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 56, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAdminUser", "line_number": 57, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 57, "usage_type": "name"}, {"api_name": "rest_framework.generics.RetrieveUpdateDestroyAPIView", "line_number": 60, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 60, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAdminUser", "line_number": 61, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 61, "usage_type": "name"}, {"api_name": "rest_framework.generics.ListAPIView", "line_number": 65, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 65, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAdminUser", "line_number": 66, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 66, "usage_type": "name"}, {"api_name": "rest_framework.generics.GenericAPIView", "line_number": 70, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 70, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 71, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 71, "usage_type": "name"}, {"api_name": "rest_framework.generics.RetrieveAPIView", "line_number": 74, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 74, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 75, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 75, "usage_type": "name"}, {"api_name": "rest_framework.generics.ListAPIView", "line_number": 79, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 79, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 80, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 80, "usage_type": "name"}, {"api_name": "rest_framework.generics.CreateAPIView", "line_number": 84, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 84, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 85, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 85, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 93, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 93, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 93, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 95, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 95, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 95, "usage_type": "name"}, {"api_name": "rest_framework.generics.RetrieveUpdateAPIView", "line_number": 97, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 97, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 98, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 98, "usage_type": "name"}]} +{"seq_id": "391997219", "text": "import os\nimport argparse\n\nimport torch\nimport cv2\nimport time\n\nfrom test import GOTURN\n\nargs = None\nparser = argparse.ArgumentParser(description='GOTURN Testing')\nparser.add_argument('-w', '--model-weights',\n type=str, help='path to pretrained model')\nparser.add_argument('-d', '--data-directory',\n default='../data/OTB/Man/img', type=str,\n help='path to video frames')\n\ndef main(args):\n cuda = torch.cuda.is_available()\n device = torch.device('cuda:0' if cuda else 'cpu')\n tester = GOTURN(args.data_directory,\n args.model_weights,\n device, True)\n # save initial frame with bounding box\n tester.model.eval()\n \n initBox = None\n try:\n init_rect = cv2.selectROI('Demo', tester.img[0][0], False, False)\n x, y, w, h = init_rect\n initBox = [x, y, w+x, h+y]\n print(initBox)\n except:\n exit()\n tester.set_init_box(initBox)\n\n count =0\n start=time.time()\n # loop through sequence images\n for i in range(tester.len):\n # get torch input tensor\n sample = tester[i]\n\n # predict box\n bbox = tester.get_rect(sample)\n #gt_bb = tester.gt[i]\n tester.prev_rect = bbox\n\n # save current image with predicted rectangle and gt box\n im = tester.img[i][1]\n sMatImageDraw = im.copy()\n cv2.rectangle(sMatImageDraw, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])), (255, 0, 0), 2)\n \n count+=1\n timeUsed = time.time()-start\n\n font = cv2.FONT_HERSHEY_SIMPLEX\n cv2.putText(sMatImageDraw,'{:0.2f}fps[#{}]'.format(count/timeUsed, i),(0,50), font, 0.5,(255,255,255),1,cv2.LINE_AA)\n\n cv2.imshow('Demo', sMatImageDraw)\n key = cv2.waitKey(10)\n if key > 0:\n break\n # save(im, bb, gt_bb, i+2)\n\n # print stats\n # print('frame: %d, IoU = %f' % (\n # i+2, axis_aligned_iou(gt_bb, bb)))\n\n\nif __name__ == \"__main__\":\n args = parser.parse_args()\n main(args)\n", "sub_path": "src/livedemo.py", "file_name": "livedemo.py", "file_ext": "py", "file_size_in_byte": 2061, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 19, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 20, "usage_type": "call"}, {"api_name": "test.GOTURN", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.selectROI", "line_number": 29, "usage_type": "call"}, {"api_name": "time.time", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 52, "usage_type": "call"}, {"api_name": "time.time", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 57, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 58, "usage_type": "call"}, {"api_name": "cv2.LINE_AA", "line_number": 58, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 60, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "54765631", "text": "import torch\nimport torch.nn as nn\nimport numpy as np\n\nfrom math import pi\nfrom torch.nn import Conv2d, ConvTranspose2d, MaxPool2d\nfrom torch.nn.init import _calculate_fan_in_and_fan_out\nimport sys\nsys.path.append(\"..\")\nfrom configs.config_wnet import config\n\n'''\nPytorch implementation of: https://github.com/MRSRL/complex-networks-release/blob/master/complex_utils.py\nImplementation related to the paper \"Complex-Valued Convolutional Neural Networks for MRI Reconstruction\" \nby Elizabeth K. Cole et. al: https://arxiv.org/abs/2004.01738\n'''\n\n\nclass ComplexConv2D(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=0, dilation=1, groups=1, bias=True):\n super(ComplexConv2D, self).__init__()\n\n in_channels = in_channels // 2\n out_channels = out_channels // 2\n\n self.conv_r = Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias)\n self.conv_i = Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias)\n\n if config['complex_weight_init']:\n self.conv_r.weight = torch.nn.Parameter(complex_init_individual(self.conv_r))\n self.conv_i.weight = torch.nn.Parameter(complex_init_individual(self.conv_i))\n\n def forward(self, x_complex): # (batch_size, channels, image_height, image_width)\n real_out = self.conv_r(x_complex[:, ::2]) - self.conv_i(x_complex[:, 1::2])\n imag_out = self.conv_r(x_complex[:, 1::2]) + self.conv_i(x_complex[:, ::2])\n\n b, c, h, w = real_out.shape\n if torch.cuda.is_available():\n output = torch.empty(b, c*2, h, w).cuda()\n else:\n output = torch.empty(b, c * 2, h, w)\n output[:, ::2] = real_out\n output[:, 1::2] = imag_out\n return output\n\n\nclass ComplexConvTranspose2D(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=0, dilation=1, groups=1, bias=True):\n super(ComplexConvTranspose2D, self).__init__()\n\n in_channels = in_channels // 2\n out_channels = out_channels // 2\n\n self.conv_r = ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias)\n self.conv_i = ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias)\n\n def forward(self, x_complex): # (batch_size, channels, image_height, image_width)\n real_out = self.conv_r(x_complex[:, ::2]) - self.conv_i(x_complex[:, 1::2])\n imag_out = self.conv_r(x_complex[:, 1::2]) + self.conv_i(x_complex[:, ::2])\n\n b, c, h, w = real_out.shape\n if torch.cuda.is_available():\n output = torch.empty(b, c*2, h, w).cuda()\n else:\n output = torch.empty(b, c * 2, h, w)\n output[:, ::2] = real_out\n output[:, 1::2] = imag_out\n return output\n\n\nclass ComplexMaxPool2D(nn.Module):\n def __init__(self, kernel_size, padding=0, dilation=1, return_indices=True, ceil_mode=False):\n super(ComplexMaxPool2D, self).__init__()\n\n self.max_pool = MaxPool2d(kernel_size=kernel_size,\n padding=padding,\n dilation=dilation,\n return_indices=return_indices,\n ceil_mode=ceil_mode)\n\n def forward(self, x_complex):\n abs_pool = torch.sqrt(torch.square(x_complex[:,::2]) + torch.square(x_complex[:,1::2]))\n #abs_pool = x_complex.cpu().detach().numpy()\n #abs_pool = np.abs(abs_pool[:, ::2]+1j*abs_pool[:, 1::2])\n #abs_pool = torch.from_numpy(abs_pool.repeat(2, axis=1)).cuda()\n abs_pool, indices = self.max_pool(abs_pool)\n indices = indices.repeat_interleave(2, dim=1)\n \n flattened_tensor = x_complex.flatten(start_dim=2)\n output = flattened_tensor.gather(dim=2, index=indices.flatten(start_dim=2)).view_as(indices)\n return output\n\n\n\nclass Zrelu(nn.Module):\n def __init__(self):\n super().__init__()\n \n def forward(self, input_complex):\n phase = torch.atan2(input_complex[:, 1::2], input_complex[:, ::2])\n # if phase > pi/2, throw it away and set comp equal to 0\n gt = torch.gt(phase, pi / 2)\n input_complex[:, ::2][gt] = 0\n input_complex[:, 1::2][gt] = 0\n # if phase < 0, throw it away and set output equal to 0\n st = ~torch.ge(phase, 0)\n input_complex[:, ::2][st] = 0\n input_complex[:, 1::2][st] = 0\n\n return input_complex\n\n\ndef Cardioid(input_complex):\n phase = torch.atan2(input_complex[:, 1::2], input_complex[:, ::2])\n scale = 0.5 * (1 + torch.cos(phase))\n real_out = input_complex[:, ::2] * scale\n imag_out = input_complex[:, 1::2] * scale\n output = torch.empty_like(input_complex)\n output[:, ::2] = real_out\n output[:, 1::2] = imag_out\n\n return output\n\n\ndef weights_init(m, criterion='glorot'):\n classname = m.__class__.__name__\n\n if classname.find('Conv2D') != -1:\n complex_init(m.weight, criterion)\n\n\ndef complex_init(m, criterion):\n fan_in, fan_out = _calculate_fan_in_and_fan_out(m.weight)\n if criterion == 'glorot':\n s = 1. / (fan_in + fan_out)\n elif criterion == 'he':\n s = 1. / fan_in\n else:\n raise ValueError('Invalid criterion: ' + criterion)\n\n rng = np.random.RandomState(1337)\n modulus = rng.rayleigh(scale=s, size=m.weight.shape)\n phase = rng.uniform(low=-np.pi, high=np.pi, size=m.weight.shape)\n weight_real = modulus * np.cos(phase)\n weight_imag = modulus * np.sin(phase)\n weight = np.concatenate([weight_real, weight_imag], axis=-1)\n return torch.from_numpy(weight)\n\n\ndef complex_init_individual(m, criterion='glorot'):\n fan_in, fan_out = _calculate_fan_in_and_fan_out(m.weight)\n if criterion == 'glorot':\n s = 1. / (fan_in + fan_out)\n elif criterion == 'he':\n s = 1. / fan_in\n else:\n raise ValueError('Invalid criterion: ' + criterion)\n b, c, h, w = m.weight.shape\n rng = np.random.RandomState(1337)\n modulus = rng.rayleigh(scale=s, size=(b, c // 2, h, w))\n phase = rng.uniform(low=-np.pi, high=np.pi, size=(b, c // 2, h, w))\n weight_real = modulus * np.cos(phase)\n weight_imag = modulus * np.sin(phase)\n weight = np.empty(m.weight.shape)\n weight[:, ::2, :, :] = weight_real\n weight[:, 1::2, :, :] = weight_imag\n weight = torch.from_numpy(weight).float()\n return weight\n", "sub_path": "utils/complex.py", "file_name": "complex.py", "file_ext": "py", "file_size_in_byte": 6480, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sys.path.append", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 19, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 27, "usage_type": "call"}, {"api_name": "configs.config_wnet.config", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "attribute"}, {"api_name": "torch.nn.Parameter", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "attribute"}, {"api_name": "torch.cuda.is_available", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 38, "usage_type": "attribute"}, {"api_name": "torch.empty", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.empty", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 47, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 47, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 62, "usage_type": "attribute"}, {"api_name": "torch.empty", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.empty", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 71, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 71, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.square", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 95, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 95, "usage_type": "name"}, {"api_name": "torch.atan2", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.gt", "line_number": 102, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 102, "usage_type": "name"}, {"api_name": "torch.ge", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.atan2", "line_number": 114, "usage_type": "call"}, {"api_name": "torch.cos", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.empty_like", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.nn.init._calculate_fan_in_and_fan_out", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.random.RandomState", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 141, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 143, "usage_type": "attribute"}, {"api_name": "numpy.cos", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 146, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 147, "usage_type": "call"}, {"api_name": "torch.nn.init._calculate_fan_in_and_fan_out", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.random.RandomState", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 159, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 161, "usage_type": "attribute"}, {"api_name": "numpy.cos", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 164, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 167, "usage_type": "call"}]} +{"seq_id": "180383131", "text": "import os\nimport sys\nimport json\n\nNUM_FRAMES = 40\nSTART_FRAME = 80\nINPUT_WRAP_FILE = 'C:\\kyleBathStuff\\Wrap\\meshPropagationAlignmentBefore.wrap'\nOUTPUT_WRAP_FILE = 'C:\\kyleBathStuff\\Wrap\\meshPropagationAlignmentBeforeTemp.wrap'\nMESH_SOURCE = 'C:/kyleBathStuff/Wrap/paddyHD1DivTrisRA/frame.%03d.obj'\nMESH_TARGET = 'C:\\kyleBathStuff\\PaddyTracking\\meshes\\paddyFrame%d.obj'\nOUT_MESH = 'C:/kyleBathStuff/Wrap/paddyHD1DivTrisRA/frame.%03d.obj'\n\nMARKERS_TARGET = 'C:/kyleBathStuff/Wrap/WrapMarkersSparse300k/WrapMarkersSparse300kframeNew%d.txt'\nMARKERS_SOURCE = 'C:/kyleBathStuff/Wrap/templateMarkers/paddyHD1DivTemplateTris.txt'\nPOLYGON_FILE = 'C:/kyleBathStuff/Wrap/baseMeshSelection1SubDivTris.txt'\n\nFOURD_OUTFILE = 'C:/kyleBathStuff/Wrap/out4D/frame4d.%03d.obj'\nOUT_SHIFT = 'C:/kyleBathStuff/Wrap/meshPropagationShift/frameShift.%03d.obj'\n\n# Create Wrap parameter settings\nSUBDIVISIONS = 3 #default 3\nICP_ITERATIONS = 5 #default 5\nOPT_ITERATIONS = 20 #default 20\nSAMP_INIT = 5 #default 5\nSAMP_FINAL = 0.2 #default 0.2\nSMOOTH_INIT = 1 #default 1\nSMOOTH_FINAL = 0.1 #default 0.1\nCTL_POINTS_WEIGHT_INIT = 3 #default 10\nCTL_POINTS_WEIGHT_FINAL = 3 #default 10\nMAX_OPT_ITERATIONS = 100 #default 100\nNORM_THRESHOLD = 0.65 #default 0.65\nDP_INIT = 0.01 #default 0.01\nDP_FINAL = 0.002 #default 0.002\n\n# Creates meshes from frame2:NUM_FRAMES. Assumes first frame is manual.\nfor i in range(START_FRAME, START_FRAME + NUM_FRAMES):\n # Load example JSON wrap file saved from first frame\n with open(INPUT_WRAP_FILE) as json_data:\n d = json.load(json_data)\n\n # Set paths for Input and Output meshes\n d['nodes']['LoadGeom02']['params']['fileNames']['value'] = [unicode(MESH_TARGET % i)]\n d['nodes']['LoadGeom01']['params']['fileNames']['value'] = [unicode(MESH_SOURCE % (i-1))]\n d['nodes']['SaveGeom01']['params']['fileName']['value'] = unicode(OUT_MESH % i)\n\n # Set paths for Select Points and Polygons\n d['nodes']['SelectPoints01']['params']['fileNameLeft']['value'] = unicode(MARKERS_SOURCE )\n d['nodes']['SelectPoints01']['params']['fileNameRight']['value'] = unicode(MARKERS_TARGET % i)\n d['nodes']['SelectPolygons01']['params']['fileName']['value'] = unicode(POLYGON_FILE)\n\n # Remove previous marker coords\n d['nodes']['SelectPoints01']['params']['pointsLeft']['value'] = []\n d['nodes']['SelectPoints01']['params']['pointsRight']['value'] = []\n\n #Set chosen Wrap parameters\n d['nodes']['Wrapping01']['params']['nSubdivisions']['value'] = SUBDIVISIONS\n d['nodes']['Wrapping01']['params']['nICPIterations']['value'] = ICP_ITERATIONS\n d['nodes']['Wrapping01']['params']['nOptimizationIterations']['value'] = OPT_ITERATIONS\n d['nodes']['Wrapping01']['params']['samplingMaxMultiplier']['value'] = SAMP_INIT\n d['nodes']['Wrapping01']['params']['samplingMinMultiplier']['value'] = SAMP_FINAL\n d['nodes']['Wrapping01']['params']['globalSmoothWeightMax']['value'] = SMOOTH_INIT\n d['nodes']['Wrapping01']['params']['globalSmoothWeightMin']['value'] = SMOOTH_FINAL\n d['nodes']['Wrapping01']['params']['globalControlPointsWeightInitial']['value'] = CTL_POINTS_WEIGHT_INIT\n d['nodes']['Wrapping01']['params']['globalControlPointsWeightFinal']['value'] = CTL_POINTS_WEIGHT_FINAL\n d['nodes']['Wrapping01']['params']['maxOptimizationIterations']['value'] = MAX_OPT_ITERATIONS\n d['nodes']['Wrapping01']['params']['minCosBetweenNormals']['value'] = NORM_THRESHOLD\n d['nodes']['Wrapping01']['params']['maxDp']['value'] = DP_INIT\n d['nodes']['Wrapping01']['params']['minDp']['value'] = DP_FINAL\n\n d['nodes']['SaveGeom02']['params']['fileName']['value'] = unicode(FOURD_OUTFILE % i)\n d['nodes']['SaveGeom03']['params']['fileName']['value'] = unicode(OUT_SHIFT % i)\n\n\n # Save JSON file\n with open(OUTPUT_WRAP_FILE, 'w') as outfile:\n json.dump(d, outfile)\n\n # Run Wrap node script for frame\n print(\"Reconstructing Frame %d: %s\\n as %s\" % (i, (MESH_TARGET % i), (OUT_MESH % i)))\n cmd = \"wrap3cmd compute %s\" % OUTPUT_WRAP_FILE\n os.system(cmd)\n d = None # Delete all node data\n", "sub_path": "Wrap/wrapScriptV4.py", "file_name": "wrapScriptV4.py", "file_ext": "py", "file_size_in_byte": 4060, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "json.load", "line_number": 39, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 76, "usage_type": "call"}, {"api_name": "os.system", "line_number": 81, "usage_type": "call"}]} +{"seq_id": "172473306", "text": "\"\"\"\nForecast serialization and deserialization.\n\nSpecific to the 2017-2018 flu season.\n\"\"\"\n\n# standard library\nimport csv\nimport datetime\nimport json\nimport os\nimport re\n\n# first party\nfrom delphi.flu_contest.utils.forecast import Forecast\nfrom delphi.flu_contest.utils.forecast_meta import Locations, Targets, Types\nfrom delphi.utils.epidate import EpiDate\nimport delphi.utils.epiweek as Epiweek\n\n\nclass ForecastIO:\n\n _version = 3\n\n @staticmethod\n def int_or_float(x):\n # cast as int, but only if it doesn't change the value\n xf = float(x)\n xi = int(xf)\n return xi if xi == xf else xf\n\n @staticmethod\n def get_week_index(week):\n if not (40 <= week <= 52) and not (1 <= week <= 20):\n raise Exception('invalid week: %d' % week)\n return week - 40 if week >= 40 else week + 12\n\n @staticmethod\n def get_index_week(index):\n if not (0 <= index <= 32):\n raise Exception('invalid index: %d' % index)\n return index + 40 if index <= 12 else index - 12\n\n @staticmethod\n def __load_row(forecast, location, target, type_, start, value):\n # convert to delphi names\n location = Locations.get_delphi_name(location)\n target = Targets.get_delphi_name(target)\n type_ = Types.get_delphi_name(type_)\n\n # get the index, which is either a name or a number\n if type_ == 'dist':\n if start == 'none':\n # no onset\n index = 'none'\n elif Targets.is_ili(target):\n # ili bin index\n index = round(float(start) * 10)\n else:\n # week number\n index = ForecastIO.get_week_index(int(start))\n else:\n # point prediction\n index = 'point'\n\n # parse the value\n value = ForecastIO.int_or_float(value)\n\n # finally, set the value\n fc = forecast.get_or_create_forecast(location)\n fc.set_single_value(target, index, value)\n\n @staticmethod\n def extract_epiweek_and_team(filename):\n \"\"\"\n Extract the submission epiweek (epiweek of most recently published report)\n and the team name from the file name of a flu contest submission.\n\n The return value is a tuple of:\n 1. the submission epiweek (e.g. 201751)\n 2. the team name (e.g. \"delphi-epicast\")\n \"\"\"\n\n # this is the naming convention for 2017 flu contest submissions\n pattern = re.compile('^EW(\\\\d{2})-(.*)-(\\\\d{4})-(\\\\d{2})-(\\\\d{2}).csv$')\n match = pattern.match(os.path.basename(filename))\n if match is None:\n # only able to parse this specific naming convention\n raise Exception()\n\n week = int(match.group(1))\n team = match.group(2)\n year = int(match.group(3))\n month = int(match.group(4))\n day = int(match.group(5))\n epiweek = EpiDate(year, month, day).get_ew()\n\n # We know the week number, but the year has to be inferred from the\n # submission date. Since the week of submission is never less than the week\n # of the most recent report, we can step backwards from the week of\n # submission until we find the expected week number. Ordinarily, this will\n # take exactly two steps. For example, data collected on 2017w51 is\n # reported on 2017w52, and our forecast is submitted on 2018w01; so we\n # start with 2018w01 and step backwards until find the first week 51, which\n # is 2017w51.\n if not 1 <= week <= 53:\n # prevent an infinite loop\n raise Exception('invalid week number: %d' % week)\n while Epiweek.split_epiweek(epiweek)[1] != week:\n epiweek = Epiweek.add_epiweeks(epiweek, -1)\n\n return epiweek, team\n\n @staticmethod\n def load_csv(filename):\n timestamp = None\n epiweek, team = ForecastIO.extract_epiweek_and_team(filename)\n\n season_start = Epiweek.get_season(epiweek)[0]\n season = Epiweek.split_epiweek(season_start)[0]\n\n forecast = Forecast(season, timestamp, team, epiweek)\n\n # the default column layout\n # can be updated based on header row\n canonical_fields = [f.lower() for f in (\n 'Location', 'Target', 'Type', 'Unit', 'Bin_start_incl',\n 'Bin_end_notincl', 'Value'\n )]\n field_to_column = dict(zip(canonical_fields, range(len(canonical_fields))))\n\n # read the csv one row at a time\n with open(filename, 'r', newline='') as f:\n reader = csv.reader(f)\n for row in reader:\n # skip header row(s)\n fields = [f.lower() for f in row]\n if 'location' in fields:\n # update the field-to-column-index mapping\n field_to_column = dict(zip(fields, range(len(fields))))\n continue\n\n # extract values\n values = [row[field_to_column[f]] for f in canonical_fields]\n location, target, type_, unit, start, end, value = values\n\n # update forecast\n ForecastIO.__load_row(forecast, location, target, type_, start, value)\n\n # return the group of forecasts per location\n return forecast\n\n @staticmethod\n def __save_target(writer, location, target, data, unit, idx_func):\n # convert to display names\n location = Locations.get_display_name(location)\n target = Targets.get_display_name(target)\n\n # point prediction\n type_ = Types.get_display_name('point')\n value = ForecastIO.int_or_float(data['point'])\n row = [location, target, type_, unit, 'NA', 'NA', value]\n writer.writerow(row)\n\n # distribution\n type_ = Types.get_display_name('dist')\n for index, value in enumerate(data['dist']):\n start, end = idx_func(index)\n start, end, value = map(ForecastIO.int_or_float, (start, end, value))\n row = [location, target, type_, unit, start, end, value]\n writer.writerow(row)\n\n # probability of no onset (if applicable)\n if 'none' in data:\n value = ForecastIO.int_or_float(data['none'])\n row = [location, target, type_, unit, 'none', 'none', value]\n writer.writerow(row)\n\n @staticmethod\n def __save_week_target(writer, location, target, data):\n def idx_func(index):\n week = ForecastIO.get_index_week(index)\n return week, week + 1\n unit = 'week'\n ForecastIO.__save_target(writer, location, target, data, unit, idx_func)\n\n @staticmethod\n def __save_ili_target(writer, location, target, data):\n def idx_func(index):\n start, end = index / 10, (index + 1) / 10\n if index == 130:\n end = 100\n return start, end\n unit = 'percent'\n ForecastIO.__save_target(writer, location, target, data, unit, idx_func)\n\n @staticmethod\n def save_csv(forecast, filename=None):\n if filename is None:\n now = datetime.datetime.now()\n week = forecast.epiweek % 100\n args = (week, forecast.team, now.year, now.month, now.day)\n filename = 'EW%02d-%s-Mturk-%04d-%02d-%02d.csv' % args\n\n # write the csv one row at a time\n with open(filename, 'w', newline='') as f:\n dialect = csv.excel()\n dialect.lineterminator = '\\n'\n writer = csv.writer(f, dialect=dialect)\n\n # write the header row\n writer.writerow([\n 'Location', 'Target', 'Type', 'Unit', 'Bin_start_incl',\n 'Bin_end_notincl', 'Value'\n ])\n\n # write each location\n for location in forecast.get_locations():\n\n # location-specific helper functions\n def save_week_target(target, data):\n ForecastIO.__save_week_target(writer, location, target, data)\n\n def save_ili_target(target, data):\n ForecastIO.__save_ili_target(writer, location, target, data)\n\n # write each target\n fc = forecast.get_forecast(location)\n if fc.has_onset():\n save_week_target('onset', fc.get_onset())\n save_week_target('peakweek', fc.get_peakweek())\n save_ili_target('peak', fc.get_peak())\n for i in range(1, 5):\n save_ili_target('x%d' % i, fc.get_lookahead(i))\n\n return filename\n\n @staticmethod\n def import_json_delphi(json_str):\n obj = json.loads(json_str)\n if obj['_version'] > ForecastIO._version:\n raise Exception('unable to import version: %d' % obj['_version'])\n team, season, epiweek = obj['name'], obj['season'], obj['epiweek']\n timestamp = None\n forecast = Forecast(team, timestamp, season, epiweek)\n data = obj['data']\n for location in data.keys():\n fc = forecast.get_or_create_forecast(location)\n fc.data = data[location]\n return forecast\n\n @staticmethod\n def export_json_delphi(forecast):\n return json.dumps({\n '_version': ForecastIO._version,\n 'name': forecast.team,\n 'season': forecast.season,\n 'epiweek': forecast.epiweek,\n 'year_weeks': forecast.season_length + 19,\n 'season_weeks': forecast.season_length,\n 'ili_bins': forecast.num_ili_bins,\n 'ili_bin_size': forecast.ili_bin_size,\n 'data': dict(\n (location, forecast.get_forecast(location).data)\n for location in forecast.get_locations()\n ),\n })\n\n @staticmethod\n def import_json_flusight(json_str, team, timestamp, season, epiweek):\n raise NotImplementedError()\n\n @staticmethod\n def export_json_flusight(forecast):\n raise NotImplementedError()\n", "sub_path": "src/utils/forecast_io_mturk.py", "file_name": "forecast_io_mturk.py", "file_ext": "py", "file_size_in_byte": 8929, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "delphi.flu_contest.utils.forecast_meta.Locations.get_delphi_name", "line_number": 47, "usage_type": "call"}, {"api_name": "delphi.flu_contest.utils.forecast_meta.Locations", "line_number": 47, "usage_type": "name"}, {"api_name": "delphi.flu_contest.utils.forecast_meta.Targets.get_delphi_name", "line_number": 48, "usage_type": "call"}, {"api_name": "delphi.flu_contest.utils.forecast_meta.Targets", "line_number": 48, "usage_type": "name"}, {"api_name": "delphi.flu_contest.utils.forecast_meta.Types.get_delphi_name", "line_number": 49, "usage_type": "call"}, {"api_name": "delphi.flu_contest.utils.forecast_meta.Types", "line_number": 49, "usage_type": "name"}, {"api_name": "delphi.flu_contest.utils.forecast_meta.Targets.is_ili", "line_number": 56, "usage_type": "call"}, {"api_name": "delphi.flu_contest.utils.forecast_meta.Targets", "line_number": 56, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "delphi.utils.epidate.EpiDate", "line_number": 96, "usage_type": "call"}, {"api_name": "delphi.utils.epiweek.split_epiweek", "line_number": 109, "usage_type": "call"}, {"api_name": "delphi.utils.epiweek", "line_number": 109, "usage_type": "name"}, {"api_name": "delphi.utils.epiweek.add_epiweeks", "line_number": 110, "usage_type": "call"}, {"api_name": "delphi.utils.epiweek", "line_number": 110, "usage_type": "name"}, {"api_name": "delphi.utils.epiweek.get_season", "line_number": 119, "usage_type": "call"}, {"api_name": "delphi.utils.epiweek", "line_number": 119, "usage_type": "name"}, {"api_name": "delphi.utils.epiweek.split_epiweek", "line_number": 120, "usage_type": "call"}, {"api_name": "delphi.utils.epiweek", "line_number": 120, "usage_type": "name"}, {"api_name": "delphi.flu_contest.utils.forecast.Forecast", "line_number": 122, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 134, "usage_type": "call"}, {"api_name": "delphi.flu_contest.utils.forecast_meta.Locations.get_display_name", "line_number": 156, "usage_type": "call"}, {"api_name": "delphi.flu_contest.utils.forecast_meta.Locations", "line_number": 156, "usage_type": "name"}, {"api_name": "delphi.flu_contest.utils.forecast_meta.Targets.get_display_name", "line_number": 157, "usage_type": "call"}, {"api_name": "delphi.flu_contest.utils.forecast_meta.Targets", "line_number": 157, "usage_type": "name"}, {"api_name": "delphi.flu_contest.utils.forecast_meta.Types.get_display_name", "line_number": 160, "usage_type": "call"}, {"api_name": "delphi.flu_contest.utils.forecast_meta.Types", "line_number": 160, "usage_type": "name"}, {"api_name": "delphi.flu_contest.utils.forecast_meta.Types.get_display_name", "line_number": 166, "usage_type": "call"}, {"api_name": "delphi.flu_contest.utils.forecast_meta.Types", "line_number": 166, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 200, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 200, "usage_type": "attribute"}, {"api_name": "csv.excel", "line_number": 207, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 209, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 240, "usage_type": "call"}, {"api_name": "delphi.flu_contest.utils.forecast.Forecast", "line_number": 245, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 254, "usage_type": "call"}]} +{"seq_id": "531011691", "text": "import sublime\nimport sublime_plugin\nimport os\nimport re\nimport fnmatch\nimport json\n\nfrom ..ml_utils import MlUtils\nfrom ..ml_settings import MlSettings\n\nclass MarkLogicAutoComplete(sublime_plugin.EventListener):\n\n\tdef __init__(self):\n\t\tself.dynamic_snippets = None\n\t\tself.xquery_function_snippets = []\n\t\tself.javascript_function_snippets = []\n\n\t# caches a list of dynamic snippets\n\tdef gen_dynamic_snippets(self):\n\t\tif (self.dynamic_snippets == None):\n\t\t\tself.dynamic_snippets = []\n\t\t\tsnip_dir = \"Packages/MarkLogic/dynamic_snippets/\"\n\t\t\tfor filename in [\"function.json\", \"imports.json\"]:\n\t\t\t\tf = MlUtils.load_resource(os.path.join(snip_dir, filename))\n\t\t\t\tjo = json.loads(f)\n\t\t\t\tif isinstance(jo, list):\n\t\t\t\t\tfor snip in jo:\n\t\t\t\t\t\tself.dynamic_snippets.append(self.create_snippet_object(snip))\n\t\t\t\telse:\n\t\t\t\t\tself.dynamic_snippets.append(self.create_snippet_object(jo))\n\n\t# load the builtin function snippets from disk\n\tdef gen_function_snippets(self, snippets, filename):\n\t\tif (len(snippets) == 0):\n\t\t\tfunctions_file = \"Packages/MarkLogic/marklogic_builtins/%s\" % filename\n\t\t\tf = MlUtils.load_resource(functions_file)\n\t\t\tfor s in json.loads(f):\n\t\t\t\tsnippets.append(self.create_snippet_object(s))\n\n\t# creates a snippet object for storing in a cache\n\tdef create_snippet_object(self, snip):\n\t\tcompletion = snip['trigger']\n\t\tif ('description' in snip):\n\t\t\tcompletion = completion + '\\t' + snip['description']\n\t\to = {\n\t\t\t'trigger': snip['trigger'],\n\t\t\t'completion': completion,\n\t\t\t'content': snip['content']\n\t\t}\n\t\treturn o\n\n\t# get the namespace of the current xquery module\n\tdef get_module_namespace(self, view):\n\t\tcontents = view.substr(sublime.Region(0, view.size()))\n\t\tsearch = re.search(r\"^\\s*module\\s+namespace\\s+([^\\s]+)\\s+\", contents, re.MULTILINE)\n\t\tif search != None:\n\t\t\treturn search.groups()[0]\n\t\treturn 'local'\n\n\t# add dynamic snippets to the autocomplete list\n\tdef process_dynamic_snippets(self, view, prefix, completions):\n\t\tself.gen_dynamic_snippets()\n\n\t\tnamespace = self.get_module_namespace(view)\n\t\tfor snip in self.dynamic_snippets:\n\t\t\ttrigger = snip['trigger']\n\t\t\tif (prefix in trigger):\n\t\t\t\tcontent = re.sub(r'%NS%', namespace, snip['content'])\n\t\t\t\tcompletions.append((snip['completion'], content))\n\n\t# add MarkLogic builtins to the autocomplete list\n\tdef process_function_snippets(self, view, prefix, snippets, filename, completions):\n\t\tself.gen_function_snippets(snippets, filename)\n\n\t\tif MlSettings.enable_marklogic_functions() == True:\n\t\t\tfor snip in snippets:\n\t\t\t\ttrigger = snip['trigger']\n\t\t\t\tif (prefix in trigger):\n\t\t\t\t\tcontent = snip['content']\n\t\t\t\t\tcompletions.append((snip['completion'], content))\n\n\tdef snippets_from_xqy_file(self, file_name, contents, ns_prefix, show_private, prefix, completions):\n\t\tfor func in MlUtils.get_function_defs(file_name, contents, ns_prefix, show_private):\n\t\t\ttrigger = func[0]\n\t\t\tif (prefix in trigger):\n\t\t\t\tdescription = \"(%s)\" % re.sub(r\"\\s+as[^,]+\", \"\", \",\".join(func[1]))\n\t\t\t\tcompletion = \"%s\\t%s\" % (trigger, description)\n\n\t\t\t\tparams = []\n\t\t\t\tindex = 1\n\t\t\t\tfor param in func[1]:\n\t\t\t\t\tparams.append('${%d:\\\\%s}' % (index, param))\n\t\t\t\t\tindex = index + 1\n\n\t\t\t\tcontent = \"%s(%s)\" % (trigger, \", \".join(params))\n\t\t\t\tcompletions.append((completion, content))\n\n\tdef process_included_code_snippets(self, view, prefix, completions):\n\t\tcontents = view.substr(sublime.Region(0, view.size()))\n\t\tfile_name = view.file_name()\n\t\tns_prefix, ns_uri = MlUtils.get_namespace(contents)\n\t\tself.snippets_from_xqy_file(file_name, contents, ns_prefix, True, prefix, completions)\n\n\t\tif (file_name):\n\t\t\tfor other_file, ns_prefix in MlUtils.get_imported_files(file_name, contents):\n\t\t\t\twith open(other_file, \"r\", encoding='utf-8') as myfile:\n\t\t\t\t\tbuffer = myfile.read()\n\t\t\t\tself.snippets_from_xqy_file(other_file, buffer, ns_prefix, False, prefix, completions)\n\n\t# called when Sublime wants a list of autocompletes\n\tdef on_query_completions(self, view, prefix, locations):\n\t\tcompletions = []\n\n\t\tversion = str(MlSettings.ml_version())\n\t\tif view.match_selector(locations[0], \"source.xquery-ml\"):\n\t\t\tself.process_dynamic_snippets(view, prefix, completions)\n\t\t\tsnippets_file = \"ml-xquery-functions-\" + version + \".json\"\n\t\t\tself.process_function_snippets(view, prefix, self.xquery_function_snippets, snippets_file, completions)\n\t\t\tself.process_included_code_snippets(view, prefix, completions)\n\t\telif MlUtils.is_server_side_js(view):\n\t\t\tself.process_function_snippets(view, prefix, self.javascript_function_snippets, \"ml-javascript-functions-\" + version + \".json\", completions)\n\n\t\treturn completions\n", "sub_path": "ml/commands/mark_logic_auto_complete.py", "file_name": "mark_logic_auto_complete.py", "file_ext": "py", "file_size_in_byte": 4534, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "sublime_plugin.EventListener", "line_number": 11, "usage_type": "attribute"}, {"api_name": "ml_utils.MlUtils.load_resource", "line_number": 24, "usage_type": "call"}, {"api_name": "ml_utils.MlUtils", "line_number": 24, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 25, "usage_type": "call"}, {"api_name": "ml_utils.MlUtils.load_resource", "line_number": 36, "usage_type": "call"}, {"api_name": "ml_utils.MlUtils", "line_number": 36, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 37, "usage_type": "call"}, {"api_name": "sublime.Region", "line_number": 54, "usage_type": "call"}, {"api_name": "re.search", "line_number": 55, "usage_type": "call"}, {"api_name": "re.MULTILINE", "line_number": 55, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 68, "usage_type": "call"}, {"api_name": "ml_settings.MlSettings.enable_marklogic_functions", "line_number": 75, "usage_type": "call"}, {"api_name": "ml_settings.MlSettings", "line_number": 75, "usage_type": "name"}, {"api_name": "ml_utils.MlUtils.get_function_defs", "line_number": 83, "usage_type": "call"}, {"api_name": "ml_utils.MlUtils", "line_number": 83, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 86, "usage_type": "call"}, {"api_name": "sublime.Region", "line_number": 99, "usage_type": "call"}, {"api_name": "ml_utils.MlUtils.get_namespace", "line_number": 101, "usage_type": "call"}, {"api_name": "ml_utils.MlUtils", "line_number": 101, "usage_type": "name"}, {"api_name": "ml_utils.MlUtils.get_imported_files", "line_number": 105, "usage_type": "call"}, {"api_name": "ml_utils.MlUtils", "line_number": 105, "usage_type": "name"}, {"api_name": "ml_settings.MlSettings.ml_version", "line_number": 114, "usage_type": "call"}, {"api_name": "ml_settings.MlSettings", "line_number": 114, "usage_type": "name"}, {"api_name": "ml_utils.MlUtils.is_server_side_js", "line_number": 120, "usage_type": "call"}, {"api_name": "ml_utils.MlUtils", "line_number": 120, "usage_type": "name"}]} +{"seq_id": "197460397", "text": "\"\"\" kullanıcıdan doğum tarihi girmesi istenir \n yaş hesaplama işlemi yapılarak, ekrana dğru formatta çıktısı verilir \"\"\"\n\ndtarihi = int(input(\"doğum tarihinizi girin:\"))\nyas= 2020 - dtarihi\nprint(\" yaşınız : {}\" .format(yas))\n\n\n\n\n\n\"\"\" dinamik bir değer için modül atanır ( şimdilik öğrenmedik ama yine de yazacağım)\"\"\"\n\n\nimport datetime\n\ndtarihi = int(input(\"doğum tarihinizi giriniz:\"))\nyas = datetime.datetime.now().year - dtarihi\nprint(\"yaşınız: {}\".format(yas))\nprint(datetime.datetimedatetime.now().year)", "sub_path": "İnput çalışmaları/input çalışmalar 2 .py", "file_name": "input çalışmalar 2 .py", "file_ext": "py", "file_size_in_byte": 540, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "datetime.datetime.now", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 18, "usage_type": "attribute"}, {"api_name": "datetime.datetimedatetime.now", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetimedatetime", "line_number": 20, "usage_type": "attribute"}]} +{"seq_id": "552808161", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# #########################################################################\n# Copyright (c) 2016, UChicago Argonne, LLC. All rights reserved. #\n# #\n# Copyright 2016. UChicago Argonne, LLC. This software was produced #\n# under U.S. Government contract DE-AC02-06CH11357 for Argonne National #\n# Laboratory (ANL), which is operated by UChicago Argonne, LLC for the #\n# U.S. Department of Energy. The U.S. Government has rights to use, #\n# reproduce, and distribute this software. NEITHER THE GOVERNMENT NOR #\n# UChicago Argonne, LLC MAKES ANY WARRANTY, EXPRESS OR IMPLIED, OR #\n# ASSUMES ANY LIABILITY FOR THE USE OF THIS SOFTWARE. If software is #\n# modified to produce derivative works, such modified software should #\n# be clearly marked, so as not to confuse it with the version available #\n# from ANL. #\n# #\n# Additionally, redistribution and use in source and binary forms, with #\n# or without modification, are permitted provided that the following #\n# conditions are met: #\n# #\n# * Redistributions of source code must retain the above copyright #\n# notice, this list of conditions and the following disclaimer. #\n# #\n# * Redistributions in binary form must reproduce the above copyright #\n# notice, this list of conditions and the following disclaimer in #\n# the documentation and/or other materials provided with the #\n# distribution. #\n# #\n# * Neither the name of UChicago Argonne, LLC, Argonne National #\n# Laboratory, ANL, the U.S. Government, 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 UChicago Argonne, LLC AND CONTRIBUTORS #\n# \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT #\n# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS #\n# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL UChicago #\n# Argonne, LLC OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, #\n# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, #\n# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; #\n# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER #\n# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT #\n# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN #\n# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE #\n# POSSIBILITY OF SUCH DAMAGE. #\n# #########################################################################\n\"\"\"Defines methods for reconstructing data from the :mod:`.acquisition` module.\n\nThe algorithm module contains methods for reconstructing tomographic data\nincluding gridrec, SIRT, ART, and MLEM. These methods can be used as benchmarks\nfor custom reconstruction methods or as an easy way to access reconstruction\nalgorithms for developing other methods such as noise correction.\n\n.. note::\n Using `tomopy `_ is recommended instead\n of these functions for heavy computation.\n\n.. moduleauthor:: Doga Gursoy \n\"\"\"\n\nimport logging\n\nimport numpy as np\n\nfrom xdesign.acquisition import thv_to_zxy\n\nlogger = logging.getLogger(__name__)\n\n__author__ = \"Doga Gursoy\"\n__copyright__ = \"Copyright (c) 2016, UChicago Argonne, LLC.\"\n__docformat__ = 'restructuredtext en'\n__all__ = ['art', 'sirt', 'mlem', 'update_progress']\n\n\ndef update_progress(progress):\n \"\"\"Draw a process bar in the terminal.\n\n Parameters\n -------------\n process : float\n The percentage completed e.g. 0.10 for 10%\n\n \"\"\"\n percent = progress * 100\n nbars = int(progress * 10)\n print(\n '\\r[{0}{1}] {2:.2f}%'.format('#' * nbars, ' ' * (10 - nbars), percent),\n end=''\n )\n if progress == 1:\n print('')\n\n\ndef get_mids_and_lengths(x0, y0, x1, y1, gx, gy):\n \"\"\"Return the midpoints and intersection lengths of a line and a grid.\n\n Parameters\n ----------\n x0,y0,x1,y1 : float\n Two points which define the line. Points must be outside the grid\n gx,gy : :py:class:`np.array`\n Defines positions for the gridlines\n\n Return\n ------\n xm,ym : :py:class:`np.array`\n Coordinates along the line within each intersected grid pixel.\n dist : :py:class:`np.array`\n Lengths of the line segments crossing each pixel\n\n \"\"\"\n # avoid upper-right boundary errors\n if (x1 - x0) == 0:\n x0 += 1e-6\n if (y1 - y0) == 0:\n y0 += 1e-6\n\n # vector lengths (ax, ay)\n ax = (gx - x0) / (x1 - x0)\n ay = (gy - y0) / (y1 - y0)\n\n # edges of alpha (a0, a1)\n ax0 = min(ax[0], ax[-1])\n ax1 = max(ax[0], ax[-1])\n ay0 = min(ay[0], ay[-1])\n ay1 = max(ay[0], ay[-1])\n a0 = max(max(ax0, ay0), 0)\n a1 = min(min(ax1, ay1), 1)\n\n # sorted alpha vector\n cx = (ax >= a0) & (ax <= a1)\n cy = (ay >= a0) & (ay <= a1)\n alpha = np.sort(np.r_[ax[cx], ay[cy]])\n\n # lengths\n xv = x0 + alpha * (x1 - x0)\n yv = y0 + alpha * (y1 - y0)\n lx = np.ediff1d(xv)\n ly = np.ediff1d(yv)\n dist = np.sqrt(lx**2 + ly**2)\n\n # indexing\n mid = alpha[:-1] + np.ediff1d(alpha) / 2.\n xm = x0 + mid * (x1 - x0)\n ym = y0 + mid * (y1 - y0)\n\n return xm, ym, dist\n\n\ndef art(\n gmin,\n gsize,\n data,\n theta,\n h,\n init,\n niter=10,\n weights=None,\n save_interval=None\n):\n \"\"\"Reconstruct data using ART algorithm. :cite:`Gordon1970`.\"\"\"\n assert data.size == theta.size == h.size, \"theta, h, must be\" \\\n \"the equal lengths\"\n data = data.ravel()\n theta = theta.ravel()\n h = h.ravel()\n if weights is None:\n weights = np.ones(data.shape)\n if save_interval is None:\n save_interval = niter\n archive = list()\n # Convert from probe to global coords\n srcx, srcy, detx, dety = thv_to_zxy(theta, h)\n # grid frame (gx, gy)\n sx, sy = init.shape\n gx = np.linspace(gmin[0], gmin[0] + gsize[0], sx + 1, endpoint=True)\n gy = np.linspace(gmin[1], gmin[1] + gsize[1], sy + 1, endpoint=True)\n midlengths = dict() # cache the result of get_mids_and_lengths\n\n for n in range(niter):\n if n % save_interval == 0:\n archive.append(init.copy())\n\n # update = np.zeros(init.shape)\n # nupdate = np.zeros(init.shape, dtype=np.uint)\n\n update_progress(n / niter)\n for m in range(data.size):\n # get intersection locations and lengths\n if m in midlengths:\n xm, ym, dist = midlengths[m]\n else:\n xm, ym, dist = get_mids_and_lengths(\n srcx[m], srcy[m], detx[m], dety[m], gx, gy\n )\n midlengths[m] = (xm, ym, dist)\n # convert midpoints of line segments to indices\n ix = np.floor(sx * (xm - gmin[0]) / gsize[0]).astype('int')\n iy = np.floor(sy * (ym - gmin[1]) / gsize[1]).astype('int')\n # simulate acquistion from initial guess\n dist2 = np.dot(dist, dist)\n if dist2 != 0:\n ind = (dist != 0) & (0 <= ix) & (ix < sx) \\\n & (0 <= iy) & (iy < sy)\n sim = np.dot(dist[ind], init[ix[ind], iy[ind]])\n upd = np.true_divide((data[m] - sim), dist2)\n init[ix[ind], iy[ind]] += dist[ind] * upd\n\n archive.append(init.copy())\n update_progress(1)\n if save_interval == niter:\n return init\n else:\n return archive\n\n\ndef sirt(\n gmin,\n gsize,\n data,\n theta,\n h,\n init,\n niter=10,\n weights=None,\n save_interval=None\n):\n \"\"\"Reconstruct data using SIRT algorithm. :cite:`Gilbert1972`.\"\"\"\n assert data.size == theta.size == h.size, \"theta, h, must be\" \\\n \"the equal lengths\"\n data = data.ravel()\n theta = theta.ravel()\n h = h.ravel()\n if weights is None:\n weights = np.ones(data.shape)\n if save_interval is None:\n save_interval = niter\n archive = list()\n # Convert from probe to global coords\n srcx, srcy, detx, dety = thv_to_zxy(theta, h)\n # grid frame (gx, gy)\n sx, sy = init.shape\n gx = np.linspace(gmin[0], gmin[0] + gsize[0], sx + 1, endpoint=True)\n gy = np.linspace(gmin[1], gmin[1] + gsize[1], sy + 1, endpoint=True)\n midlengths = dict() # cache the result of get_mids_and_lengths\n\n for n in range(niter):\n if n % save_interval == 0:\n archive.append(init.copy())\n\n update = np.zeros(init.shape)\n nupdate = np.zeros(init.shape, dtype=np.uint)\n\n update_progress(n / niter)\n for m in range(data.size):\n # get intersection locations and lengths\n if m in midlengths:\n xm, ym, dist = midlengths[m]\n else:\n xm, ym, dist = get_mids_and_lengths(\n srcx[m], srcy[m], detx[m], dety[m], gx, gy\n )\n midlengths[m] = (xm, ym, dist)\n # convert midpoints of line segments to indices\n ix = np.floor(sx * (xm - gmin[0]) / gsize[0]).astype('int')\n iy = np.floor(sy * (ym - gmin[1]) / gsize[1]).astype('int')\n # simulate acquistion from initial guess\n dist2 = np.dot(dist, dist)\n if dist2 != 0:\n ind = (dist != 0) & (0 <= ix) & (ix < sx) \\\n & (0 <= iy) & (iy < sy)\n sim = np.dot(dist[ind], init[ix[ind], iy[ind]])\n upd = np.true_divide((data[m] - sim), dist2)\n update[ix[ind], iy[ind]] += dist[ind] * upd\n nupdate[ix[ind], iy[ind]] += 1\n\n nupdate[nupdate == 0] = 1\n init += np.true_divide(update, nupdate)\n\n archive.append(init.copy())\n update_progress(1)\n if save_interval == niter:\n return init\n else:\n return archive\n\n\ndef mlem(gmin, gsize, data, theta, h, init, niter=10):\n \"\"\"Reconstruct data using MLEM algorithm.\"\"\"\n assert data.size == theta.size == h.size, \"theta, h, must be\" \\\n \"the equal lengths\"\n data = data.ravel()\n theta = theta.ravel()\n h = h.ravel()\n # if weights is None:\n # weights = np.ones(data.shape)\n # if save_interval is None:\n # save_interval = niter\n # archive = list()\n # Convert from probe to global coords\n srcx, srcy, detx, dety = thv_to_zxy(theta, h)\n # grid frame (gx, gy)\n sx, sy = init.shape\n gx = np.linspace(gmin[0], gmin[0] + gsize[0], sx + 1, endpoint=True)\n gy = np.linspace(gmin[1], gmin[1] + gsize[1], sy + 1, endpoint=True)\n midlengths = dict() # cache the result of get_mids_and_lengths\n\n for n in range(niter):\n\n update = np.zeros(init.shape)\n sumdist = np.zeros(init.shape)\n\n update_progress(n / niter)\n for m in range(data.size):\n # get intersection locations and lengths\n if m in midlengths:\n xm, ym, dist = midlengths[m]\n else:\n xm, ym, dist = get_mids_and_lengths(\n srcx[m], srcy[m], detx[m], dety[m], gx, gy\n )\n midlengths[m] = (xm, ym, dist)\n # convert midpoints of line segments to indices\n ix = np.floor(sx * (xm - gmin[0]) / gsize[0]).astype('int')\n iy = np.floor(sy * (ym - gmin[1]) / gsize[1]).astype('int')\n # simulate acquistion from initial guess\n ind = (dist != 0)\n sumdist[ix[ind], iy[ind]] += dist\n sim = np.dot(dist[ind], init[ix[ind], iy[ind]])\n\n if not sim == 0:\n upd = np.true_divide(data[m], sim)\n update[ix[ind], iy[ind]] += dist[ind] * upd\n\n init[sumdist > 0] *= np.true_divide(\n update[sumdist > 0], sumdist[sumdist > 0] * sy\n )\n update_progress(1)\n return init\n", "sub_path": "src/xdesign/recon.py", "file_name": "recon.py", "file_ext": "py", "file_size_in_byte": 12611, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "logging.getLogger", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.r_", "line_number": 134, "usage_type": "attribute"}, {"api_name": "numpy.ediff1d", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.ediff1d", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.ediff1d", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 169, "usage_type": "call"}, {"api_name": "xdesign.acquisition.thv_to_zxy", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.true_divide", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 236, "usage_type": "call"}, {"api_name": "xdesign.acquisition.thv_to_zxy", "line_number": 241, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 245, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 252, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.uint", "line_number": 253, "usage_type": "attribute"}, {"api_name": "numpy.floor", "line_number": 266, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 267, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 269, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 273, "usage_type": "call"}, {"api_name": "numpy.true_divide", "line_number": 274, "usage_type": "call"}, {"api_name": "numpy.true_divide", "line_number": 279, "usage_type": "call"}, {"api_name": "xdesign.acquisition.thv_to_zxy", "line_number": 302, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 305, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 306, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 311, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 312, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 325, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 326, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 330, "usage_type": "call"}, {"api_name": "numpy.true_divide", "line_number": 333, "usage_type": "call"}, {"api_name": "numpy.true_divide", "line_number": 336, "usage_type": "call"}]} +{"seq_id": "114606581", "text": "import locale\nimport logging\nimport requests\n\nfrom .. import db\n\n_CHUNK_SIZE = 10000\n\nlog = logging.getLogger(__name__)\nlog.setLevel(logging.INFO)\nlocale.setlocale(locale.LC_ALL, '')\n\n\ndef bulk_insert(mapper, mapping, chunk_size=_CHUNK_SIZE, return_defaults=False):\n for i in xrange(0, len(mapping), chunk_size):\n batch = mapping[i:i + chunk_size]\n db.session.bulk_insert_mappings(mapper, batch, render_nulls=True, return_defaults=return_defaults)\n db.session.flush()\n log.info(\"inserted {0:,d} '{1}' records\".format(i + len(batch), mapper.__tablename__))\n\n\ndef insert(table, data):\n if data:\n db.session.connection().execute(table.insert(), data)\n log.info(\"inserted {0:,d} '{1}' records\".format(len(data), table.name))\n\n\ndef delete(table):\n db.session.connection().execute(table.delete())\n log.info(\"empty table: '{0}'\".format(table.name))\n\n\ndef bulk_save(objects, chunk_size=_CHUNK_SIZE, return_defaults=False):\n for i in xrange(0, len(objects), chunk_size):\n batch = objects[i:i + chunk_size]\n db.session.bulk_save_objects(batch, return_defaults=return_defaults)\n db.session.flush()\n log.info(\"saved {0:,d} '{1}' objects\".format(i + len(objects), objects[0].__class__.__name__))\n\n\ndef bulk_update(mapper, mapping, chunk_size=_CHUNK_SIZE):\n for i in xrange(0, len(mapping), chunk_size):\n batch = mapping[i:i + chunk_size]\n db.session.bulk_update_mappings(mapper, batch)\n db.session.flush()\n log.info(\"updated {0:,d} '{1}' records\".format(i + len(batch), mapper.__tablename__))\n", "sub_path": "src/annex/orm/util.py", "file_name": "util.py", "file_ext": "py", "file_size_in_byte": 1596, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 10, "usage_type": "attribute"}, {"api_name": "locale.setlocale", "line_number": 11, "usage_type": "call"}, {"api_name": "locale.LC_ALL", "line_number": 11, "usage_type": "attribute"}]} +{"seq_id": "249386169", "text": "import numpy as np\nimport probfit as pf\nimport matplotlib.pyplot as plt\nimport scipy.optimize as so\nimport scipy.stats as ss\n\nimport iminuit as im\nfrom pprint import pprint\nimport string\nimport pandas as pd\n\n# design_0_nosmear_ge_2pix.npy\nalpha_low = string.ascii_lowercase\n\n\ndef read_file(input_file_):\n return np.load(input_file_, allow_pickle=True, encoding='latin1')\n\n\ndef remake_arrays(input_arr_):\n\n w_r_bins = 0.01\n\n # need z-binning corresponding to 1 roc\n w_z_bins = 52 # # of pixels in a roc\n\n # need phi-binning corresponding to 1 roc (maybe 2?)\n w_phi_bins = 80\n\n n_z_bins = int(3328 / w_z_bins) # 3328 is number of pixels in a ladder row\n\n n_phi_bins = int(960 / w_phi_bins) # 1440 is number of pixels around phi for all ladders, 960 for inner ladders\n #n_phi_bins = int(1440 / w_phi_bins) # 1440 is number of pixels around phi for all ladders, 960 for inner ladders\n\n inner_array = np.array([row for row in input_arr_ if not np.all(row==None)])\n cleaned_array = np.array([[x if x is not None else [0, np.nan, np.nan, np.nan] for x in row]\n for row in inner_array])\n r_min = np.nanmin(cleaned_array[:, :, 1])\n r_max = np.nanmax(cleaned_array[:, :, 1])\n\n # separate pixels into groups corresponding to rocs in phi and z\n array_by_rocs = np.array([cleaned_array[j*w_phi_bins:(j+1)*w_phi_bins, i*w_z_bins:(i+1)*w_z_bins] for i in range(n_z_bins) for j in range(n_phi_bins)])\n\n #roc_index = [0, 1]\n roc_index = range(0, n_z_bins*n_phi_bins)\n\n # fig, axs = plt.subplots(8, 8, sharex=False, sharey=False, figsize=(160, 160), tight_layout=True) #all rocs and modules\n #fig, axs = plt.subplots(12, 2, sharex=True, sharey=True, figsize=(20, 20), tight_layout=False) # fraction of rocs and modules\n fig, axs = plt.subplots(12, sharex=True, sharey=True, figsize=(20, 20), tight_layout=False) # fraction of rocs and modules\n #fig, axs = plt.subplots(3, sharex=True, sharey=True, figsize=(20, 20), tight_layout=True) # fraction of rocs and modules\n\n # minus - 0-383\n # plus - 384-767\n occ_plus = []\n occ_minus = []\n r_sph_plus = []\n r_sph_minus = []\n phi_plus = []\n phi_minus = []\n z_plus = []\n z_minus = []\n\n # section off rocs into roc ladders (12 'ladders'), each true ladder is split in half 6 * 2 = 12\n n_ladders = 12\n\n for x in range(n_ladders):\n occ_plus.append([])\n occ_minus.append([])\n r_sph_plus.append([])\n r_sph_minus.append([])\n phi_plus.append([])\n phi_minus.append([])\n z_plus.append([])\n z_minus.append([])\n i_ladder = 0\n for roc in roc_index:\n\n occ_tmp = np.concatenate(array_by_rocs[roc, :, :, 0])\n r = np.concatenate(array_by_rocs[roc, :, :, 1])\n phi = np.concatenate(array_by_rocs[roc, :, :, 2])\n z = np.concatenate(array_by_rocs[roc, :, :, 3])\n z_avg = np.nanmean(z)\n\n r_sph_tmp = np.sqrt(r**2+z**2)\n\n r_sph_minus[i_ladder].append(np.nanmean(r_sph_tmp))\n occ_minus[i_ladder].append(np.sum(occ_tmp[~np.isnan(occ_tmp)]))\n phi_minus[i_ladder].append(np.nanmean(phi))\n z_minus[i_ladder].append(np.nanmean(z))\n #if roc < 384:\n # r_sph_minus[i_ladder].append(np.nanmean(r_sph_tmp))\n # occ_minus[i_ladder].append(np.sum(occ_tmp[~np.isnan(occ_tmp)]))\n # phi_minus[i_ladder].append(np.nanmean(phi))\n # z_minus[i_ladder].append(np.nanmean(z))\n #else:\n # r_sph_plus[i_ladder].append(np.nanmean(r_sph_tmp))\n # occ_plus[i_ladder].append(np.sum(occ_tmp[~np.isnan(occ_tmp)]))\n # phi_plus[i_ladder].append(np.nanmean(phi))\n # z_plus[i_ladder].append(np.nanmean(z))\n\n i_ladder += 1\n if i_ladder == n_ladders:\n i_ladder = 0\n\n #avg_phi_plus = np.array([np.mean(iphi) for iphi in phi_plus])\n avg_phi_minus = np.array([np.mean(iphi) for iphi in phi_minus])\n #phi_sort_plus = np.argsort(avg_phi_plus)\n phi_sort_minus = np.argsort(avg_phi_minus)\n # sort ladders by phi\n #r_sph_plus_tmp = np.array(r_sph_plus)\n r_sph_minus_tmp = np.array(r_sph_minus)\n #occ_plus_tmp = np.array(occ_plus)\n occ_minus_tmp = np.array(occ_minus)\n #z_plus_tmp = np.array(z_plus)\n z_minus_tmp = np.array(z_minus)\n #r_sph_plus_tmp = r_sph_plus_tmp[phi_sort_plus]\n r_sph_minus_tmp = r_sph_minus_tmp[phi_sort_minus]\n #occ_plus_tmp = occ_plus_tmp[phi_sort_plus]\n occ_minus_tmp = occ_minus_tmp[phi_sort_minus]\n #z_plus_tmp = z_plus_tmp[phi_sort_plus]\n z_minus_tmp = z_minus_tmp[phi_sort_minus]\n\n #print(avg_phi_plus[phi_sort_plus])\n print(avg_phi_minus[phi_sort_minus])\n #n_ladders = 6 # for running over modules in a ring instead of rocs\n for x in range(n_ladders):\n #fig, axs = plt.subplots(3, sharex=True, sharey=True, figsize=(20, 20),\n # tight_layout=True) # fraction of rocs and modules\n r_sph_minus[x] = np.array(r_sph_minus_tmp[x])\n occ_minus[x] = np.array(occ_minus_tmp[x])\n z_minus[x] = np.array(z_minus_tmp[x])\n sort_minus = np.argsort(r_sph_minus[x])\n r_sph_minus[x] = r_sph_minus[x][sort_minus]\n occ_minus[x] = occ_minus[x][sort_minus]\n\n #r_sph_plus[x] = np.array(r_sph_plus_tmp[x])\n #occ_plus[x] = np.array(occ_plus_tmp[x])\n #z_plus[x] = np.array(z_plus_tmp[x])\n #sort_plus = np.argsort(r_sph_plus[x])\n #r_sph_plus[x] = r_sph_plus[x][sort_plus]\n #occ_plus[x] = occ_plus[x][sort_plus]\n #z_plus[x] = z_plus[x][sort_plus]\n\n # removing rocs\n remove_z = (z_minus[x] > -25) * (z_minus[x] < 25)\n remove_blips = (z_minus[x] < -21) + (z_minus[x] > -20)\n remove_blips *= (z_minus[x] < -14.5) + (z_minus[x] > -13.5)\n remove_blips *= (z_minus[x] < -7.5) + (z_minus[x] > -6.5)\n remove_blips *= (z_minus[x] < 5.75) + (z_minus[x] > 6.5)\n remove_blips *= (z_minus[x] < 12.5) + (z_minus[x] > 13.5)\n remove_blips *= (z_minus[x] < 19) + (z_minus[x] > 20)\n\n #r_sph_plus[x] = r_sph_plus[x][remove_z_plus*remove_blips_plus]\n r_sph_minus[x] = r_sph_minus[x][remove_z*remove_blips]\n #r_sph_minus[x] = r_sph_minus[x][remove_z_minus]\n #occ_plus[x] = occ_plus[x][remove_z_plus*remove_blips_plus]\n occ_minus[x] = occ_minus[x][remove_z*remove_blips]\n #occ_minus[x] = occ_minus[x][remove_z_minus]\n\n z_condense = []\n r_sph_condense = []\n occ_condense = []\n for ir, r in enumerate(r_sph_minus[x]):\n if ir % 2 == 0 and ir+1 < len(r_sph_minus[x]):\n r_sph_condense.append((r + r_sph_minus[x][ir + 1]) / 2)\n occ_condense.append(occ_minus[x][ir] + occ_minus[x][ir + 1])\n z_condense.append((z_minus[x][ir] + z_minus[x][ir + 1]) / 2)\n else:\n continue\n\n r_sph_minus[x] = np.array(r_sph_condense)\n occ_minus[x] = np.array(occ_condense)\n z_minus[x] = np.array(z_condense)\n\n axs[x].plot(r_sph_minus[x], occ_minus[x], 'b*', label='full z hl '+str(x))\n #axs[x, 0].plot(r_sph_minus[x], occ_minus[x], 'b*', label='minus z - hl '+str(x))\n #axs[x, 1].plot(r_sph_plus[x], occ_plus[x], 'b*', label='plus z - hl '+str(x))\n #axs[0].plot(r_sph_minus[x], occ_minus[x], 'b*', label='minus z - hl '+str(x))\n #axs[1].plot(r_sph_plus[x], occ_plus[x], 'b*', label='plus z - hl '+str(x))\n\n #axs[2].plot(r_sph_minus[x], occ_minus[x], 'r*', label='occ minus')\n #axs[2].plot(r_sph_plus[x], occ_plus[x], 'b*', label='occ plus')\n chi2_minus = pf.Chi2Regression(func, r_sph_minus[x], occ_minus[x])\n #chi2_plus = pf.Chi2Regression(func, r_sph_plus[x], occ_plus[x])\n #minuit_minus = im.Minuit(chi2_minus, a=0, b=1.16, c=2000,\n # error_a=1, error_b=0.01, error_c=1,\n # fix_b=False,\n # limit_a=(None, None), limit_b=(None, None), limit_c=(None, None),\n # errordef=1)\n minuit_minus = im.Minuit(chi2_minus, a=200000, b=-0.4, c=2000,\n error_a=1, error_b=0.01, error_c=1,\n fix_b=False,\n limit_a=(None, None), limit_b=(0, 10), limit_c=(None, None),\n errordef=1)\n #minuit_minus = im.Minuit(chi2_minus, a=200000, b=0.5989, c=2000, r0=0,\n # error_a=1, error_b=0.01, error_c=1, error_r0=0.01,\n # fix_b=True,\n # limit_a=(None, None), limit_b=(0, 10), limit_c=(None, None), limit_r0=(None, None),\n # errordef=1)\n #minuit_plus = im.Minuit(chi2_plus, a=200000, b=0.5989, c=2000, r0=0,\n # error_a=1, error_b=0.01, error_c=1, error_r0=0.01,\n # fix_b=True,\n # limit_a=(None, None), limit_b=(0, 10), limit_c=(None, None), limit_r0=(None, None),\n # errordef=1)\n #print(minuit.get_param_states())\n minuit_minus.migrad()\n #minuit_plus.migrad()\n #print(minuit_plus.get_param_states())\n #print(minuit_plus.get_fmin())\n print(minuit_minus.get_param_states())\n print(minuit_minus.get_fmin())\n\n # chi2_minus.draw(minuit_minus, axs[x, 0])\n # chi2_plus.draw(minuit_plus, axs[x, 1])\n # chi2_minus.draw(minuit_minus, axs[0])\n # chi2_plus.draw(minuit_plus, axs[1])\n param_string = ''\n for p in minuit_minus.parameters:\n param_string += '{}: {} +/- {}\\n'.format(p, np.format_float_scientific(minuit_minus.values[p], precision=3),\n np.format_float_scientific(minuit_minus.errors[p], precision=1))\n #axs[x, 0].text(15, 550000, param_string, fontsize='xx-small')\n #axs[x].text(7.5, 20000, param_string, fontsize='xx-small')\n axs[x].text(22, 6500, param_string, fontsize='xx-small')\n\n #param_string = ''\n #for p in minuit_plus.parameters:\n # param_string += '{}: {} +/- {}\\n'.format(p, np.format_float_scientific(minuit_plus.values[p], precision=3),\n # np.format_float_scientific(minuit_plus.errors[p], precision=1))\n #axs[x, 1].text(15, 550000, param_string, fontsize='xx-small')\n\n axs[x].plot(r_sph_minus[x], func(r_sph_minus[x], *minuit_minus.values.values()), color='red', label='fit')\n #axs[x, 0].plot(r_sph_minus[x], func(r_sph_minus[x], *minuit_minus.values.values()), color='red', label='fit minus')\n #axs[x, 1].plot(r_sph_plus[x], func(r_sph_plus[x], *minuit_plus.values.values()), color='red', linestyle='solid', label='fit plus')\n #axs[0].plot(r_sph_minus[x], func(r_sph_minus[x], *minuit_minus.values.values()), color='red', label='fit minus')\n #axs[1].plot(r_sph_plus[x], func(r_sph_plus[x], *minuit_plus.values.values()), color='red', linestyle='solid', label='fit plus')\n #axs[2].plot(r_sph_minus[x], func(r_sph_minus[x], *minuit_minus.values.values()), color='red', label='fit minus')\n #axs[2].plot(r_sph_plus[x], func(r_sph_plus[x], *minuit_plus.values.values()), color='blue', linestyle='dashed', label='fit plus')\n\n axs[x].legend(fontsize='xx-small', loc='upper right')\n #axs[x, 1].legend(fontsize='xx-small', loc='upper right')\n #axs[x, 0].legend(fontsize='xx-small', loc='upper right')\n #axs[2].legend()\n #axs[1].legend(fontsize='xx-small')\n #axs[0].legend(fontsize='xx-small')\n #axs[2].grid()\n\n #plt.savefig('plots_0_smear/fit_ladders_pm_'+str(x)+'_layer1.png')\n #plt.clf()\n plt.show()\n\n\n#def func(x, a, b, c, r0):\n# return a*(1/(x-r0)**(2*b)) + c\n\n\ndef func(x, a, b, c):\n#def func(x, a, b):\n\n return a*(1/(x)**(b)) + c\n #return a*x + b\n\n\ndef roc_map(roc_num):\n base_index = {'0': 7,\n '1': 6,\n '2': 5,\n '3': 4,\n '4': 3,\n '5': 2,\n '6': 1,\n '7': 0}\n roc_floor = roc_num // 8\n roc_mod = roc_num % 8\n return base_index[str(roc_mod)] + (roc_floor*8)\n\n\nif __name__ == \"__main__\":\n #in_array = read_file(\"design_0_nosmear_ge_2pix.npy\")\n #in_array = read_file(\"design_0_no_outer_ge_2pix_smear.npy\")\n #in_array = read_file(\"design_z10_no_outer_ge_2pix.npy\")\n #in_array = read_file(\"design_zneg10_no_outer_ge_2pix.npy\")\n #in_array = read_file(\"design_0_nosmear_no_outer_ge_2pix.npy\")\n #in_array = read_file(\"design_0_no_outer_all_pix_nosmear_phifix.npy\")\n in_array = read_file(\"design_0p3_no_outer_all_pix_nosmear.npy\")\n #in_array = read_file(\"design_0p1_II_no_outer_all_pix_nosmear.npy\")\n #in_array = read_file(\"design_0_no_outer_all_pix_nosmear_chargel200.npy\")\n #in_array = read_file(\"design_0p3_no_outer_all_pix_nosmear_chargel200.npy\")\n #in_array = read_file(\"design_0p1_no_outer_all_pix_nosmear_phifix.npy\")\n remake_arrays(in_array)", "sub_path": "python/occupancy_plotting/occupancy_plots_minuit_ladders.py", "file_name": "occupancy_plots_minuit_ladders.py", "file_ext": "py", "file_size_in_byte": 13166, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "string.ascii_lowercase", "line_number": 13, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.nanmin", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.nanmax", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 173, "usage_type": "call"}, {"api_name": "probfit.Chi2Regression", "line_number": 183, "usage_type": "call"}, {"api_name": "iminuit.Minuit", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.format_float_scientific", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.format_float_scientific", "line_number": 220, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 249, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 249, "usage_type": "name"}]} +{"seq_id": "164721839", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import, unicode_literals\n\nfrom django.conf.urls import url\n\nfrom . import views\n\nurlpatterns = [\n # URL pattern for the UserListView\n url(\n regex=r'^list/$',\n view=views.TeamListView.as_view(),\n name='team-list'\n ),\n url(\n regex=r'^create/$',\n view=views.TeamCreateView.as_view(),\n name='team-create'\n ),\n # url(\n # regex=r'^~sms/$',\n # view=views.sms,\n # name='sms'\n # ),\n \n\n]\n", "sub_path": "project_x/teams/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 521, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "229740321", "text": "from unittest import TestCase\nfrom selenium import webdriver\n\ndriver=webdriver.Chrome()\nclass testcounterweb(TestCase):\n\n def test_increase_button(self):\n driver = webdriver.Chrome()\n driver.get(\"http://127.0.0.1:8000/flights/counter\")\n increase=driver.find_element_by_id(\"increase\")\n increase.click()\n self.assertEqual(driver.find_element_by_id(\"result\").text,\"1\")", "sub_path": "flights/selenium_test.py", "file_name": "selenium_test.py", "file_ext": "py", "file_size_in_byte": 404, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 4, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 4, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 5, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 8, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 8, "usage_type": "name"}]} +{"seq_id": "21918233", "text": "\"\"\"Datasets.\"\"\"\n\nimport tensorflow_datasets as tfds\nimport tensorflow_text as text\nimport tensorflow as tf\n\nfrom renn import utils\nfrom renn.data.tokenizers import load_tokenizer, SEP\n\n__all__ = ['ag_news', 'goemotions', 'imdb', 'snli', 'tokenize_fun', 'mnist']\n\n\ndef pipeline(dset, preprocess_fun=utils.identity, filter_fn=None, bufsize=1024):\n \"\"\"Common (standard) dataset pipeline.\n Preprocesses the data, filters it (if a filter function is specified),\n caches it, and shuffles it.\n\n Note: Does not batch\"\"\"\n\n # Apply custom preprocessing.\n dset = dset.map(preprocess_fun)\n\n # Apply custom filter.\n if filter_fn is not None:\n dset = dset.filter(filter_fn)\n\n # Cache and shuffle.\n dset = dset.cache().shuffle(buffer_size=bufsize)\n\n return dset\n\n\ndef tokenize_fun(tokenizer):\n \"\"\"Standard text processing function.\"\"\"\n wsp = text.WhitespaceTokenizer()\n return utils.compose(tokenizer.tokenize, wsp.tokenize, text.case_fold_utf8)\n\n\ndef padded_batch(dset, batch_size, sequence_length, label_shape=()):\n \"\"\"Pads examples to a fixed length, and collects them into batches.\"\"\"\n\n # We assume the dataset contains inputs, labels, and an index.\n padded_shapes = {\n 'inputs': (sequence_length,),\n 'labels': label_shape,\n 'index': (),\n }\n\n # Filter out examples longer than sequence length.\n dset = dset.filter(lambda d: d['index'] <= sequence_length)\n\n # Pad remaining examples to the sequence length.\n dset = dset.padded_batch(batch_size, padded_shapes)\n\n return dset\n\n\ndef load_text_classification(name,\n split,\n preprocess_fun,\n filter_fn=None,\n data_dir=None):\n \"\"\"Helper that loads a text classification dataset.\"\"\"\n\n # Load raw dataset.\n dset = tfds.load(name, split=split, data_dir=data_dir)\n\n # Apply common dataset pipeline.\n dset = pipeline(dset, preprocess_fun=preprocess_fun, filter_fn=filter_fn)\n\n return dset\n\n\ndef ag_news(split,\n vocab_file,\n sequence_length=100,\n batch_size=64,\n transform_fn=utils.identity,\n filter_fn=None,\n data_dir=None):\n \"\"\"Loads the ag news dataset.\"\"\"\n tokenize = tokenize_fun(load_tokenizer(vocab_file))\n\n def _preprocess(d):\n \"\"\"Applies tokenization.\"\"\"\n tokens = tokenize(d['description']).flat_values # Note: we ignore 'title'\n preprocessed = {\n 'inputs': tokens,\n 'labels': d['label'],\n 'index': tf.size(tokens),\n }\n return transform_fn(preprocessed)\n\n # Load dataset.\n dset = load_text_classification('ag_news_subset',\n split,\n _preprocess,\n filter_fn,\n data_dir=data_dir)\n\n # Pad remaining examples to the sequence length.\n dset = padded_batch(dset, batch_size, sequence_length)\n\n return dset\n\n\ndef goemotions(split,\n vocab_file,\n sequence_length=50,\n batch_size=64,\n emotions=None,\n transform=utils.identity,\n filter_fn=None,\n data_dir=None):\n \"\"\"Loads the goemotions dataset.\"\"\"\n tokenize = tokenize_fun(load_tokenizer(vocab_file))\n\n if emotions is not None: # Use all emotions.\n emotions = ('admiration', 'amusement', 'anger', 'annoyance', 'approval',\n 'caring', 'confusion', 'curiosity', 'desire', 'disappointment',\n 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear',\n 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'neutral',\n 'optimism', 'pride', 'realization', 'relief', 'remorse',\n 'sadness', 'surprise')\n\n def _preprocess(d):\n tokens = tokenize(d['comment_text']).flat_values\n index = tf.size(tokens)\n labels = tf.convert_to_tensor([d[e] for e in emotions], dtype=tf.int64)\n preprocessed = {\n 'inputs': tokens,\n 'labels': labels,\n 'index': index,\n }\n return transform(preprocessed)\n\n # Load dataset.\n dset = load_text_classification('goemotions',\n split,\n _preprocess,\n filter_fn,\n data_dir=data_dir)\n\n # Pad remaining examples to the sequence length.\n dset = padded_batch(dset,\n batch_size,\n sequence_length,\n label_shape=(len(emotions),))\n\n return dset\n\n\ndef imdb(split,\n vocab_file,\n sequence_length=1000,\n batch_size=64,\n transform=utils.identity,\n filter_fn=None,\n data_dir=None):\n \"\"\"Loads the imdb reviews dataset.\"\"\"\n tokenize = tokenize_fun(load_tokenizer(vocab_file))\n\n def _preprocess(d):\n \"\"\"Applies tokenization.\"\"\"\n tokens = tokenize(d['text']).flat_values\n preprocessed = {\n 'inputs': tokens,\n 'labels': d['label'],\n 'index': tf.size(tokens),\n }\n return transform(preprocessed)\n\n # Load dataset.\n dset = load_text_classification('imdb_reviews',\n split,\n _preprocess,\n filter_fn,\n data_dir=data_dir)\n\n # Pad remaining examples to the sequence length.\n dset = padded_batch(dset, batch_size, sequence_length)\n\n return dset\n\n\ndef snli(split,\n vocab_file,\n sequence_length=75,\n batch_size=64,\n transform=utils.identity,\n filter_fn=None,\n data_dir=None):\n \"\"\"Loads the SNLI dataset.\"\"\"\n tokenize = tokenize_fun(load_tokenizer(vocab_file))\n\n def _preprocess(d):\n \"\"\"Applies tokenization.\"\"\"\n hypothesis = tokenize(d['hypothesis']).flat_values\n premise = tokenize(d['premise']).flat_values\n sep = tokenize(SEP).flat_values\n tokens = tf.concat([hypothesis, sep, premise], axis=0)\n return transform({\n 'inputs': tokens,\n 'labels': d['label'],\n 'index': tf.size(tokens),\n })\n\n # Load dataset.\n dset = load_text_classification('snli',\n split,\n _preprocess,\n filter_fn,\n data_dir=data_dir)\n\n # Pad remaining examples to the sequence length.\n dset = padded_batch(dset, batch_size, sequence_length)\n\n return dset\n\n\ndef mnist(split,\n order='row',\n batch_size=64,\n transform=utils.identity,\n filter_fn=None,\n data_dir=None,\n classes=None):\n \"\"\"Loads the serialized MNIST dataset.\n\n Args:\n classes - the subset of classes to keep.\n If None, all will be kept\"\"\"\n\n def _preprocess(example):\n image = tf.squeeze(example['image'])\n image = tf.cast(image, tf.float32) / 255.\n\n if order == 'col':\n image = tf.transpose(image, perm=[1, 0])\n\n return transform({'inputs': image, 'labels': example['label'], 'index': 28})\n\n # Load dataset.\n dset = tfds.load('mnist', data_dir=data_dir)[split]\n\n if classes is not None:\n # Filter out images without the proper label\n allowed_fn = _in_subset(classes)\n # Remap labels to be in range (0, number of classes)\n relabel_fn = _relabel_subset(classes)\n\n dset = dset.filter(allowed_fn).map(relabel_fn)\n\n dset = pipeline(dset, _preprocess, filter_fn)\n\n # Batch dataset.\n return dset.batch(batch_size)\n\n\ndef _relabel_subset(subclasses):\n \"\"\"Provides a function for relabeling classes.\n Example should contain key 'label' \"\"\"\n\n s = tf.constant(subclasses, dtype=tf.int64)\n\n def relabel(example):\n example.update({'label': tf.where(s == example['label'])[0][0]})\n return example\n\n return relabel\n\n\ndef _in_subset(subclasses):\n \"\"\"Provides a function for determining whether\n an example is in one of the provided subclasses.\n Expmle should contain a key 'label' \"\"\"\n\n s = tf.constant(subclasses, dtype=tf.int64)\n\n def in_subset(example):\n label = example['label']\n isallowed = tf.equal(s, label)\n reduced = tf.reduce_sum(tf.cast(isallowed, tf.float32))\n return tf.greater(reduced, tf.constant(0.))\n\n return in_subset\n", "sub_path": "renn/data/datasets.py", "file_name": "datasets.py", "file_ext": "py", "file_size_in_byte": 8223, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "renn.utils.identity", "line_number": 13, "usage_type": "attribute"}, {"api_name": "renn.utils", "line_number": 13, "usage_type": "name"}, {"api_name": "tensorflow_text.WhitespaceTokenizer", "line_number": 35, "usage_type": "call"}, {"api_name": "renn.utils.compose", "line_number": 36, "usage_type": "call"}, {"api_name": "renn.utils", "line_number": 36, "usage_type": "name"}, {"api_name": "tensorflow_text.case_fold_utf8", "line_number": 36, "usage_type": "attribute"}, {"api_name": "tensorflow_datasets.load", "line_number": 66, "usage_type": "call"}, {"api_name": "renn.utils.identity", "line_number": 78, "usage_type": "attribute"}, {"api_name": "renn.utils", "line_number": 78, "usage_type": "name"}, {"api_name": "renn.data.tokenizers.load_tokenizer", "line_number": 82, "usage_type": "call"}, {"api_name": "tensorflow.size", "line_number": 90, "usage_type": "call"}, {"api_name": "renn.utils.identity", "line_number": 112, "usage_type": "attribute"}, {"api_name": "renn.utils", "line_number": 112, "usage_type": "name"}, {"api_name": "renn.data.tokenizers.load_tokenizer", "line_number": 116, "usage_type": "call"}, {"api_name": "tensorflow.size", "line_number": 128, "usage_type": "call"}, {"api_name": "tensorflow.convert_to_tensor", "line_number": 129, "usage_type": "call"}, {"api_name": "tensorflow.int64", "line_number": 129, "usage_type": "attribute"}, {"api_name": "renn.utils.identity", "line_number": 157, "usage_type": "attribute"}, {"api_name": "renn.utils", "line_number": 157, "usage_type": "name"}, {"api_name": "renn.data.tokenizers.load_tokenizer", "line_number": 161, "usage_type": "call"}, {"api_name": "tensorflow.size", "line_number": 169, "usage_type": "call"}, {"api_name": "renn.utils.identity", "line_number": 190, "usage_type": "attribute"}, {"api_name": "renn.utils", "line_number": 190, "usage_type": "name"}, {"api_name": "renn.data.tokenizers.load_tokenizer", "line_number": 194, "usage_type": "call"}, {"api_name": "renn.data.tokenizers.SEP", "line_number": 200, "usage_type": "argument"}, {"api_name": "tensorflow.concat", "line_number": 201, "usage_type": "call"}, {"api_name": "tensorflow.size", "line_number": 205, "usage_type": "call"}, {"api_name": "renn.utils.identity", "line_number": 224, "usage_type": "attribute"}, {"api_name": "renn.utils", "line_number": 224, "usage_type": "name"}, {"api_name": "tensorflow.squeeze", "line_number": 235, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 236, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 236, "usage_type": "attribute"}, {"api_name": "tensorflow.transpose", "line_number": 239, "usage_type": "call"}, {"api_name": "tensorflow_datasets.load", "line_number": 244, "usage_type": "call"}, {"api_name": "tensorflow.constant", "line_number": 264, "usage_type": "call"}, {"api_name": "tensorflow.int64", "line_number": 264, "usage_type": "attribute"}, {"api_name": "tensorflow.where", "line_number": 267, "usage_type": "call"}, {"api_name": "tensorflow.constant", "line_number": 278, "usage_type": "call"}, {"api_name": "tensorflow.int64", "line_number": 278, "usage_type": "attribute"}, {"api_name": "tensorflow.equal", "line_number": 282, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 283, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 283, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 283, "usage_type": "attribute"}, {"api_name": "tensorflow.greater", "line_number": 284, "usage_type": "call"}, {"api_name": "tensorflow.constant", "line_number": 284, "usage_type": "call"}]} +{"seq_id": "580420359", "text": "# -*- coding: utf-8 -*-\n# @author: yangyd\n# @file: app_driver.py\n# @time: 2019/9/9 20:29\nimport time\n\nfrom appium.webdriver import Remote\n\ncaps = {\n \"platformName\": \"Android\",\n \"platformVersion\": \"5.1\",\n \"deviceName\": \"Android Emulator\",\n \"appActivity\": \"com.xxzb.fenwoo.activity.addition.WelcomeActivity\",\n \"appPackage\": \"com.xxzb.fenwoo\",\n \"noReset\": \"False\"\n}\n\nandroid_driver = Remote(desired_capabilities=caps)\n\nandroid_driver.implicitly_wait(10)\n\n# 获取手机的尺寸\nphone_size = android_driver.get_window_size()\n\n# 等待启动\ntime.sleep(2)\nfor i in range(4):\n android_driver.swipe(phone_size['width'] * 0.9, phone_size['width'] * 0.5,\n phone_size['width'] * 0.1, phone_size['width'] * 0.5)\n time.sleep(1)\n\n# 点击立即体检进入首页\n# android_driver.find_element_by_id(\"com.xxzb.fenwoo:id/btn_start\").click()\nandroid_driver.find_element_by_android_uiautomator(\n 'new UiSelector().resourceId(\"com.xxzb.fenwoo:id/btn_start\")').click()\ntime.sleep(2)\n\nandroid_driver.quit()\n", "sub_path": "APP_Auto_Test/study_code/app_driver.py", "file_name": "app_driver.py", "file_ext": "py", "file_size_in_byte": 1039, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "appium.webdriver.Remote", "line_number": 18, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 26, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 30, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "320481534", "text": "\"\"\"Update user table for github oauth\n\nRevision ID: c70816b44dbb\nRevises: 4c7c660a1eab\nCreate Date: 2019-09-27 15:12:42.080508\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'c70816b44dbb'\ndown_revision = '4c7c660a1eab'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('users', sa.Column('avatar_url', sa.String(length=280), nullable=True))\n op.add_column('users', sa.Column('github_id', sa.Integer(), nullable=False))\n op.add_column('users', sa.Column('login', sa.String(length=280), nullable=False))\n op.alter_column('users', 'api_key',\n existing_type=sa.VARCHAR(length=280),\n nullable=False)\n op.alter_column('users', 'name',\n existing_type=sa.VARCHAR(length=280),\n nullable=True)\n op.create_unique_constraint(None, 'users', ['api_key'])\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_constraint(None, 'users', type_='unique')\n op.alter_column('users', 'name',\n existing_type=sa.VARCHAR(length=280),\n nullable=False)\n op.alter_column('users', 'api_key',\n existing_type=sa.VARCHAR(length=280),\n nullable=True)\n op.drop_column('users', 'login')\n op.drop_column('users', 'github_id')\n op.drop_column('users', 'avatar_url')\n # ### end Alembic commands ###\n", "sub_path": "migrations/versions/c70816b44dbb_update_user_table_for_github_oauth.py", "file_name": "c70816b44dbb_update_user_table_for_github_oauth.py", "file_ext": "py", "file_size_in_byte": 1535, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "alembic.op.add_column", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op.add_column", "line_number": 22, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 22, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 22, "usage_type": "call"}, {"api_name": "alembic.op.add_column", "line_number": 23, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 23, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 23, "usage_type": "call"}, {"api_name": "alembic.op.alter_column", "line_number": 24, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 24, "usage_type": "name"}, {"api_name": "sqlalchemy.VARCHAR", "line_number": 25, "usage_type": "call"}, {"api_name": "alembic.op.alter_column", "line_number": 27, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 27, "usage_type": "name"}, {"api_name": "sqlalchemy.VARCHAR", "line_number": 28, "usage_type": "call"}, {"api_name": "alembic.op.create_unique_constraint", "line_number": 30, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 30, "usage_type": "name"}, {"api_name": "alembic.op.drop_constraint", "line_number": 36, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 36, "usage_type": "name"}, {"api_name": "alembic.op.alter_column", "line_number": 37, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 37, "usage_type": "name"}, {"api_name": "sqlalchemy.VARCHAR", "line_number": 38, "usage_type": "call"}, {"api_name": "alembic.op.alter_column", "line_number": 40, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 40, "usage_type": "name"}, {"api_name": "sqlalchemy.VARCHAR", "line_number": 41, "usage_type": "call"}, {"api_name": "alembic.op.drop_column", "line_number": 43, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 43, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 44, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 44, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 45, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 45, "usage_type": "name"}]} +{"seq_id": "470850079", "text": "\"\"\"\nUsage:\n delete-duplicate-file.py -s SOURCE -d DELETE\n\nArguments:\n SOURCE 需要整理文件目录\n DELETE 需要删除的文件目录\n\nOptions:\n -h --help show help\n -s 输入需要整理文件的目录\n -d 输入保存要删除文件的目录\n\"\"\"\nimport hashlib\nimport os\nimport shutil\n\nfrom docopt import docopt\nfrom sqlalchemy import BigInteger, Column, Integer, String, create_engine\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy.orm import Session\n\nBase = declarative_base()\n\nBLOCKSIZE = 1024 * 1024 * 64\n\n\nclass FileInfo(Base):\n __tablename__ = 'file_info'\n\n id = Column(Integer, primary_key=True, autoincrement='ignore_fk')\n filename = Column(String)\n sha1_info = Column(String)\n file_size = Column(BigInteger)\n filepath = Column(String)\n\n def __repr__(self):\n return (f\" 0 and sommex_y != 0 :\n somme = 2 * sommex_y/(sommex_x + sommey_y)\n documentList[documentNumber+1]= somme\n elif similarityMeasure == \"Cosine similarity\" : \n for documentNumber in range (len(matrixDoxumentTerm)):\n sommex_y = 0\n sommey_y = 0\n sommex_x = 0\n somme = 0\n for key in queryVictor.keys(): \n sommex_x = sommex_x + queryVictor[key]*queryVictor[key]\n sommey_y = sommey_y + matrixDoxumentTerm[documentNumber][key] * matrixDoxumentTerm[documentNumber][key]\n sommex_y = sommex_y + queryVictor[key]*matrixDoxumentTerm[documentNumber][key]\n if sommex_x * sommey_y > 0 and sommex_y != 0 :\n somme = sommex_y/math.sqrt(sommex_x * sommey_y)\n documentList[documentNumber+1]= somme\n\n elif similarityMeasure == \"Jaccard index\" :\n for documentNumber in range (len(matrixDoxumentTerm)):\n sommex_y = 0\n sommey_y = 0\n sommex_x = 0\n somme = 0\n for key in queryVictor.keys():\n sommex_x = sommex_x + queryVictor[key]*queryVictor[key]\n sommey_y = sommey_y + matrixDoxumentTerm[documentNumber][key] * matrixDoxumentTerm[documentNumber][key]\n sommex_y = sommex_y + queryVictor[key]*matrixDoxumentTerm[documentNumber][key]\n if sommex_x + sommey_y - sommex_y > 0 and sommex_y != 0 :\n somme = sommex_y/(sommex_x + sommey_y - sommex_y)\n documentList[documentNumber+1]= somme\n \n \n documentListResult = list(documentList.items())\n documentListResult = sorted(documentListResult)\n return documentListResult\n\n\n\n#evaluation module victorial \n#calculer recall\n\ndef calculeRecall():\n\n return\n#calculate Precision\ndef calculatePrecision():\n \n return\n \n###################################\n###################################\n#### applying the fonctions########\n###################################\n###################################\nif __name__ == '__main__':\n documentList = extract_information()\n wordFrequenctList = calculate_frequency(documentList)\n\n \n invertedFile = create_invertedFile(wordFrequenctList)\n # saveInvertedFile(\"data/invertedFile.pkl\",invertedFile)\n \n \n repetitionDict = list_repetition(wordFrequenctList)\n # invertedFile_weights =createInvertedFileWeights(wordFrequenctList,repetitionDict)\n # saveInvertedFileWeights(\"data/invertedFileWeights.pkl\",invertedFile_weights)\n matrixDoxumentTerm = preparationVectorialSearch(repetitionDict,invertedFile,len(documentList) )\n \n query = 'Dictionary construction and accessing methods for fast retrieval of words or lexical items or morphologically related information. Hashing or indexing methods are usually applied to English spelling or natural language problems.'\n \n similarityMeasure = \"Sørensen–Dice coefficient\"#type de similariy 4\n listWords = list(repetitionDict.keys())\n \n documentListResult = vectorialModelSearh(query , matrixDoxumentTerm , similarityMeasure , listWords )\n print(documentListResult)\n \n \n similarityMeasure = \"Cosine similarity\"#type de similariy 4\n documentListResult = vectorialModelSearh(query , matrixDoxumentTerm , similarityMeasure , listWords )\n print(documentListResult)\n \n similarityMeasure = \"Jaccard index\"#type de similariy 4\n documentListResult = vectorialModelSearh(query , matrixDoxumentTerm , similarityMeasure , listWords )\n print(documentListResult)\n\n similarityMeasure = \"Inner product\"#type de similariy 4\n documentListResult = vectorialModelSearh(query , matrixDoxumentTerm , similarityMeasure , listWords )\n print(documentListResult)", "sub_path": "server/vectorialSearch.py", "file_name": "vectorialSearch.py", "file_ext": "py", "file_size_in_byte": 6660, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "numpy.zeros", "line_number": 15, "usage_type": "call"}, {"api_name": "nltk.RegexpTokenizer", "line_number": 38, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 91, "usage_type": "call"}, {"api_name": "server.utils.extract_information", "line_number": 132, "usage_type": "call"}, {"api_name": "server.utils.calculate_frequency", "line_number": 133, "usage_type": "call"}, {"api_name": "server.utils.create_invertedFile", "line_number": 136, "usage_type": "call"}, {"api_name": "server.utils.list_repetition", "line_number": 140, "usage_type": "call"}]} +{"seq_id": "206871881", "text": "import json\nimport os\nfrom time import sleep\n\nfrom elasticsearch import Elasticsearch, TransportError\n\nes = Elasticsearch([{'host': 'localhost', 'port': 9200}])\n\nINDEX_NAME = \"wikipedia_pages\"\nDOC_TYPE = \"Wikipage\"\n\nif es.indices.exists(INDEX_NAME):\n es.indices.delete(index=INDEX_NAME)\n\nif not es.indices.exists(INDEX_NAME):\n es.indices.create(index=INDEX_NAME)\n\n with open(\"data/mapping.json\", \"r\", encoding=\"utf-8\") as file:\n mapping_json: dict = json.load(file)\n es.indices.put_mapping(body=mapping_json, index=INDEX_NAME)\n\n path: str = \"data/docs/\"\n for r, d, f in os.walk(path):\n counter = 0\n for file_name in f:\n if file_name.endswith(\".json\"):\n try:\n with open(path + file_name, \"r\", encoding=\"utf-8\") as file:\n json_object: dict = json.load(file)\n es.index(index=INDEX_NAME, body=json_object)\n counter += 1\n sleep(0.1)\n print(\"Indexed file: %s, doc_number: %i\" % (file_name, counter))\n except TransportError as e:\n print(e.info)\n", "sub_path": "index.py", "file_name": "index.py", "file_ext": "py", "file_size_in_byte": 1163, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "elasticsearch.Elasticsearch", "line_number": 7, "usage_type": "call"}, {"api_name": "json.load", "line_number": 19, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 23, "usage_type": "call"}, {"api_name": "json.load", "line_number": 29, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 32, "usage_type": "call"}, {"api_name": "elasticsearch.TransportError", "line_number": 34, "usage_type": "name"}]} +{"seq_id": "496089296", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nimport matplotlib.cm as cm\nimport pylab\nimport h5py\nfrom sklearn.model_selection import train_test_split\n\ndef plot(a,b,c):\n fig = plt.figure()\n ax = fig.gca(projection='3d')\n ax.scatter(a, b, c, cmap = cm.jet)\n ax.set_xlabel('x')\n ax.set_ylabel('y')\n ax.set_zlabel('z')\n pylab.show()\n\nname = \"Project_officer\"\n\nfile = h5py.File( 'PU-Net_input/Project_officer.h5', 'r')\nwi = np.array(file['wi'])\nfile.close()\n\n\n#xs = np.load(name+\"xs.npy\")\n#ys = np.load(name+\"ys.npy\")\n#zs = np.load(name+\"zs.npy\")\n#\n#x_cent = np.sum(xs)/len(xs)\n#y_cent = np.sum(ys)/len(ys)\n#z_cent = np.sum(zs)/len(zs)\n#\n#xs = (xs - x_cent)\n#ys = (ys - y_cent)\n#zs = (zs - z_cent)\n#\n#distance = np.sqrt(np.square(xs)+np.square(ys)+np.square(zs))\n#scale = np.max(distance)\n#\n#xs = (xs)/scale\n#ys = (ys)/scale\n#zs = (zs)/scale\n#\n#x_cent = np.sum(xs)/len(xs)\n#y_cent = np.sum(ys)/len(ys)\n#z_cent = np.sum(zs)/len(zs)\n#\n#plot(xs, zs, ys)\n\nwi_data = np.stack((wi[0,:,0],wi[0,:,1],wi[0,:,2]),axis=1)\n#\n#if(len(data)>4096):\n# gt_data, tmp= train_test_split(data,train_size=4096/len(data), random_state=55)\n#else:\n# gt_data = np.pad(data,((0,4096-len(data)),(0,0)),'wrap')\n \n#wi_data = np.repeat((wi_data),2,axis=1)\nwi_data = np.pad(wi_data,((0,0),(0,3)),'wrap')\nplot(wi_data[:,0],wi_data[:,1],wi_data[:,2])\nnp.save('PU-net_test/'+name+'.npy',wi_data)\nnp.savetxt('PU-net_test/'+name+'_gt.xyz', wi_data, delimiter=' ')", "sub_path": "convertToxyz.py", "file_name": "convertToxyz.py", "file_ext": "py", "file_size_in_byte": 1498, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "matplotlib.cm.jet", "line_number": 12, "usage_type": "attribute"}, {"api_name": "matplotlib.cm", "line_number": 12, "usage_type": "name"}, {"api_name": "pylab.show", "line_number": 16, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "465599142", "text": "# coding: utf-8\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport os.path as osp\n\nimport cv2\nimport numpy as np\nfrom PIL import ImageFont, Image, ImageDraw\n\ndirname = os.path.dirname(__file__)\nFont = ImageFont.truetype(os.path.join(dirname, 'huawenfangsong.ttf'), 20)\n\n\ndef order_points(pts):\n pts = np.array(pts)\n # sort the points based on their x-coordinates\n xSorted = pts[np.argsort(pts[:, 0]), :]\n \n # grab the left-most and right-most points from the sorted\n # x-roodinate points\n leftMost = xSorted[:2, :]\n rightMost = xSorted[2:, :]\n \n # now, sort the left-most coordinates according to their\n # y-coordinates so we can grab the top-left and bottom-left\n # points, respectively\n tl, bl = leftMost[np.argsort(leftMost[:, 1]), :]\n tr, br = rightMost[np.argsort(rightMost[:, 1]), :]\n \n # return the coordinates in top-left, top-right,\n # bottom-right, and bottom-left order\n return [tl.tolist(), tr.tolist(), br.tolist(), bl.tolist()]\n\n\ndef cv2ImgAddText(img, text, left, top, textColor=(0, 255, 0), textSize=20):\n \"\"\"\n interface that supports chinese text visualization\n \"\"\"\n if (isinstance(img, np.ndarray)): # 判断是否OpenCV图片类型\n img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\n draw = ImageDraw.Draw(img)\n draw.text((left, top), text, textColor, font=Font)\n return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)\n\n\nclass Vistool(object):\n \n def __init__(self, image_np_or_path):\n if isinstance(image_np_or_path, np.ndarray):\n self._canvas = image_np_or_path\n else:\n self._canvas = cv2.imread(image_np_or_path)\n \n def save(self, savepath):\n if len(savepath.split('/')) >= 2:\n savedir = osp.dirname(savepath)\n if not osp.exists(savedir):\n os.makedirs(savedir)\n cv2.imwrite(savepath, self._canvas)\n \n @property\n def canvas(self):\n return self._canvas\n \n def draw(self, coords, color=[255, 0, 0], prefix=None, canvas=None):\n \n if coords.shape[0] == 0:\n return self._canvas\n if coords.shape[1] == 8:\n coord = coords[:, :8]\n score = [1.0 for _ in range(coords.shape[0])]\n if coords.shape[1] == 9: # x1,y1,x2,y2,x3,y3,x4,y4,score\n coord = coords[:, :8]\n score = coords[:, 8]\n if coords.shape[1] == 5: # x1,y1,x2,y2,score\n score = coords[:, 4]\n x1, y1, x2, y2 = np.split(coords[:, :4], 4, axis=1)\n coord = np.concatenate([x1, y1, x2, y1, x2, y2, x1, y2], axis=1)\n \n coords = coord.reshape([-1, 4, 2])\n confs = score\n canvas = canvas if canvas else self._canvas\n self._canvas = self.mold_vertext_on_image(canvas, coords, confs, color, prefix)\n \n def mold_vertext_on_image(self, image_np, coords, confs, color, prefix):\n \"\"\"\n :param image_np:\n :param coords: batchx #vertext x 2\n :param content:\n :return:\n \"\"\"\n num = coords.shape[0]\n if num == 0: return image_np\n if coords.shape[1] == 4:\n coords = np.array([order_points(coords[i]) for i in range(num)])\n vex_coords = np.reshape(coords, [num, -1, 2]).astype(np.int32)\n if prefix is not None:\n for i in range(num):\n loc = tuple(vex_coords[i][0])\n image_np = cv2ImgAddText(image_np, prefix[i], loc[0], loc[1] - 20, (255, 0, 0), 1)\n for i in range(num):\n cv2.polylines(image_np, [vex_coords[i]], True, color=color, thickness=1, lineType=30)\n return image_np\n\n\n# Copied from https://github.com/facebookresearch/Detectron/blob/master/detectron/utils/colormap.py\nDETECTRON_PALETTE = np.array(\n [\n 0.000, 0.447, 0.741,\n 0.850, 0.325, 0.098,\n 0.929, 0.694, 0.125,\n 0.494, 0.184, 0.556,\n 0.466, 0.674, 0.188,\n 0.301, 0.745, 0.933,\n 0.635, 0.078, 0.184,\n 0.300, 0.300, 0.300,\n 0.600, 0.600, 0.600,\n 1.000, 0.000, 0.000,\n 1.000, 0.500, 0.000,\n 0.749, 0.749, 0.000,\n 0.000, 1.000, 0.000,\n 0.000, 0.000, 1.000,\n 0.667, 0.000, 1.000,\n 0.333, 0.333, 0.000,\n 0.333, 0.667, 0.000,\n 0.333, 1.000, 0.000,\n 0.667, 0.333, 0.000,\n 0.667, 0.667, 0.000,\n 0.667, 1.000, 0.000,\n 1.000, 0.333, 0.000,\n 1.000, 0.667, 0.000,\n 1.000, 1.000, 0.000,\n 0.000, 0.333, 0.500,\n 0.000, 0.667, 0.500,\n 0.000, 1.000, 0.500,\n 0.333, 0.000, 0.500,\n 0.333, 0.333, 0.500,\n 0.333, 0.667, 0.500,\n 0.333, 1.000, 0.500,\n 0.667, 0.000, 0.500,\n 0.667, 0.333, 0.500,\n 0.667, 0.667, 0.500,\n 0.667, 1.000, 0.500,\n 1.000, 0.000, 0.500,\n 1.000, 0.333, 0.500,\n 1.000, 0.667, 0.500,\n 1.000, 1.000, 0.500,\n 0.000, 0.333, 1.000,\n 0.000, 0.667, 1.000,\n 0.000, 1.000, 1.000,\n 0.333, 0.000, 1.000,\n 0.333, 0.333, 1.000,\n 0.333, 0.667, 1.000,\n 0.333, 1.000, 1.000,\n 0.667, 0.000, 1.000,\n 0.667, 0.333, 1.000,\n 0.667, 0.667, 1.000,\n 0.667, 1.000, 1.000,\n 1.000, 0.000, 1.000,\n 1.000, 0.333, 1.000,\n 1.000, 0.667, 1.000,\n 0.167, 0.000, 0.000,\n 0.333, 0.000, 0.000,\n 0.500, 0.000, 0.000,\n 0.667, 0.000, 0.000,\n 0.833, 0.000, 0.000,\n 1.000, 0.000, 0.000,\n 0.000, 0.167, 0.000,\n 0.000, 0.333, 0.000,\n 0.000, 0.500, 0.000,\n 0.000, 0.667, 0.000,\n 0.000, 0.833, 0.000,\n 0.000, 1.000, 0.000,\n 0.000, 0.000, 0.167,\n 0.000, 0.000, 0.333,\n 0.000, 0.000, 0.500,\n 0.000, 0.000, 0.667,\n 0.000, 0.000, 0.833,\n 0.000, 0.000, 1.000,\n 0.000, 0.000, 0.000,\n 0.143, 0.143, 0.143,\n 0.286, 0.286, 0.286,\n 0.429, 0.429, 0.429,\n 0.571, 0.571, 0.571,\n 0.714, 0.714, 0.714,\n 0.857, 0.857, 0.857,\n 1.000, 1.000, 1.000\n ]\n).astype(np.float32).reshape(-1, 3) * 255\n\n\nclass Box(object):\n \n def __init__(self, bndbox, text='', label_score=1.0, source=''):\n \"\"\"\n Interface Class for Visualization tools\n Args:\n bndbox: list or array-like coords for bounding box, supporting both 2-point rep style:[x1, y1, x2, y2]\n and 8-point rep style: [x1, y1, x2, y2, x3, y3, x4, y4]\n text(str): text to display above bounding box\n label_score(float): bounding box score\n source(str): box's source model, support visualization for multiple models' outputs\n \"\"\"\n self.bndbox = bndbox\n self.text = text\n self.label_score = label_score\n self.source = source\n\n\ndef draw(image_path, box_list, color=None):\n \"\"\"\n \n Args:\n image_path: BGR format numpy or image_path\n box_list(Box): list of Box object\n\n Returns:\n Vistool object, with .canvas property to access drawn image\n \n \"\"\"\n vt = Vistool(image_path)\n sources = list(set([getattr(box, 'source', '') for box in box_list]))\n colors = [hash(s) % DETECTRON_PALETTE.shape[0] for s in sources]\n for i, source in enumerate(sources):\n source_box_list = list(filter(lambda x: getattr(x, 'source', '') == source, box_list))\n coord = np.array([b.bndbox for b in source_box_list], dtype=np.float32) # nx8\n scores = np.array([getattr(b, 'label_score', 1.0) for b in source_box_list], dtype=np.float32) # n\n coords = np.concatenate([coord, np.expand_dims(scores, -1)], axis=1)\n display_info = [b.text for b in source_box_list]\n vt.draw(coords, color=colors[i] if color is None else color, prefix=display_info)\n return vt\n", "sub_path": "vis_tools.py", "file_name": "vis_tools.py", "file_ext": "py", "file_size_in_byte": 7905, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.path.dirname", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "PIL.ImageFont.truetype", "line_number": 14, "usage_type": "call"}, {"api_name": "PIL.ImageFont", "line_number": 14, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 42, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 43, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 43, "usage_type": "name"}, {"api_name": "cv2.cvtColor", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 43, "usage_type": "attribute"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 44, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 44, "usage_type": "name"}, {"api_name": "cv2.cvtColor", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 46, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2BGR", "line_number": 46, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 52, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.split", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 99, "usage_type": "attribute"}, {"api_name": "cv2.polylines", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 192, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 229, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 229, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 230, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 231, "usage_type": "call"}]} +{"seq_id": "570146733", "text": "#!/usr/bin/env python\n# coding=utf-8\n# ** -----------------------------------------------------------------------------------------------\n# ** 文件名称: userScoreSimStep1.py\n# ** 功能描述: 计算用户评分相似度 job1\n# ** 创建者: sunwj\n# ** 创建日期: 2018-02-06\n# ** 修改日志:\n# ** 修改日期:\n# ** -----------------------------------------------------------------------------------------------\nfrom mrjob.job import MRJob\nimport os\nimport sys\n\nclass UserScoreSimStep1(MRJob):\n \"\"\" \n 聚合单个资产的下的所有用户的评分数据 \n 格式为：user_id, (item_count, rating_sum, [(item_id,rating)...]) \n \"\"\"\n os.environ['HADOOP_HOME'] = '/opt/cloudera/parcels/CDH/lib/hadoop/'\n third_libs = [\"numpy/numpy/lib/python2.7\", \"mrjob/mrjob/lib/python2.7\"]\n\n for lib in third_libs:\n sys.path.append(lib)\n\n\n def group_by_user_rating(self, _, line):\n \"\"\"\n \n :param _: \n :param line: \n :return: \n \"\"\"\n\n # 解析行: 用户,资产,评分,兴趣评分,评价总条数\n user, content, score1, score2, item_cnt = line.split(',')\n # 输出串: 资产,用户,评价总条数,评分,兴趣评分\n values = \"{user}:{cnt}:{score1}:{score2}\".format(user=user, cnt=item_cnt, score1=score1, score2=score2)\n yield content, values\n\n\n def count_ratings_users_freq(self, key, values):\n \"\"\"\n \n :param key: \n :param values: \n :return: \n \"\"\"\n yield key, \",\".join(values)\n\n\n def steps(self):\n return [self.mr(mapper=self.group_by_user_rating,\n reducer=self.count_ratings_users_freq)]\n\n\nif __name__ == '__main__':\n\n UserScoreSimStep1.run()", "sub_path": "AiInsight/datamining/userrecm/userScoreSimStep1.py", "file_name": "userScoreSimStep1.py", "file_ext": "py", "file_size_in_byte": 1771, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "mrjob.job.MRJob", "line_number": 15, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 24, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 24, "usage_type": "attribute"}]} +{"seq_id": "501028919", "text": "\nfrom django.shortcuts import render,HttpResponse\nfrom .models import *\nfrom django.core.paginator import Paginator\nimport json\nimport urllib\n\ndef index(request):\n limit = 5\n joblist = getAllJob()\n paginator = Paginator(joblist,limit)\n #t通过request 变成页码\n page = request.GET.get('page',1)\n loaded = paginator.page(page)\n #进行分页设计值，一共展示11页\n total = int(paginator.num_pages)\n #当前的页数\n now = int(page)\n # 用来装页码的数组\n pagenums = pageBean(now,total,11)\n\n newjobs = getNewJob()\n newagina = Paginator(newjobs,limit)\n newpage = request.GET.get('newpage',1)\n newloaded = newagina.page(newpage)\n newpagenums = pageBean(int(newpage),int(newagina.num_pages),6)\n try:\n username = request.session['username']\n collectloaded = getcollectByname(username)\n except KeyError:\n collectloaded = []\n pass\n print(collectloaded)\n context = {\n 'jobs':loaded,\n 'pagenums':pagenums,\n 'newjobs':newloaded,\n 'newpagenums':newpagenums,\n 'collect':collectloaded\n }\n return render(request,'index.html',context)\n#传入当前的页码，总页码，和需要分的页数，返回页码的list集合\ndef fsearch1(request):\n q = request.GET['q']\n if q == '':\n return None\n else:\n #对url的中文进行解析\n key_values = urllib.parse.unquote(q)\n print(key_values)\n #获得返回的匹配数据的list\n result = getTagByPoisitonOrCompany(key_values)\n return HttpResponse(json.dumps(result),content_type='application/json')\n\ndef fsearch2(request):\n cy = request.GET['cy']\n if cy == '':\n return None\n else:\n #对url的中文进行解析\n key_values = urllib.parse.unquote(cy)\n #获得返回的匹配数据的list\n result = getTagByCity(key_values)\n return HttpResponse(json.dumps(result),content_type='application/json')\n\ndef search(request):\n s_name = request.GET.get('s_name')\n s_city = request.GET.get('s_city')\n print(\"s_name=\"+s_name+\"s_city=\"+s_city)\n if (s_name == '')and (s_city == ''):\n return render(request, 'index.html')\n else:\n if s_name == '':\n city = urllib.parse.unquote(s_city)\n joblist = getSearchJob('',city)\n print(city)\n elif s_city == '':\n name = urllib.parse.unquote(s_name)\n joblist = getSearchJob(name,'')\n print(name)\n else:\n name = urllib.parse.unquote(s_name)\n city = urllib.parse.unquote(s_city)\n joblist = getSearchJob(name,city)\n print(city+name)\n limit = 5\n paginator = Paginator(joblist, limit)\n # t通过request 变成页码\n page = request.GET.get('page', 2)\n loaded = paginator.page(page)\n # 进行分页设计值，一共展示11页\n total = int(paginator.num_pages)\n # 当前的页数\n now = int(page)\n # 用来装页码的数组\n pagenums = pageBean(now, total, 11)\n newjobs = getNewJob()\n newagina = Paginator(newjobs, limit)\n page = request.GET.get('newpage', 1)\n newloaded = newagina.page(page)\n newpagenums = pageBean(int(page), int(newagina.num_pages), 6)\n context = {\n 'jobs': loaded,\n 'pagenums': pagenums,\n 'newjobs': newloaded,\n 'newpagenums': newpagenums,\n 'thejoburl':'s_name='+s_name+'&s_city='+s_city+'&',\n }\n return render(request,'index.html',context)\n\ndef classfy(request):\n back_list = []\n return back_list\n\ndef emaliExit(request):\n email = request.GET.get('email')\n if emaliIsExit(email):\n status = 'yes'\n else:\n status = 'erro'\n return HttpResponse(status)\n\ndef register(request):\n email = request.GET.get('email')\n psd1 = request.GET.get('psd1')\n if adduser(email,psd1):\n status = 'yes'\n else:\n status ='error'\n return HttpResponse(status)\n\ndef login(request):\n email = request.GET.get('email')\n psd1 = request.GET.get('psd1')\n if finduser(email,psd1):\n status = 'yes'\n request.session['username']=email\n else:\n status = 'error'\n return HttpResponse(status)\n\ndef logout(request):\n del request.session['username']\n status = 'ok'\n return HttpResponse(status)\n\ndef collect(request):\n job_url = request.GET.get('url')\n username = request.session['username']\n if addcollect(username,job_url):\n status = 'yes'\n else:\n status = 'error'\n return HttpResponse(status)\ndef escollect(request):\n job_url = request.GET.get('url')\n username = request.session['username']\n if delcollectByname(username,job_url):\n status = 'yes'\n else:\n status = 'error'\n return HttpResponse(status)\n\ndef personal(request):\n try:\n username = request.session['username']\n collect_list = getperson(username)\n limit = 5\n pntor = Paginator(collect_list,limit)\n pn = request.GET.get('pn',1)\n collectloaded = pntor.page(pn)\n pbeans = pageBean(int(pn),int(pntor.num_pages),7)\n except KeyError:\n collectloaded = []\n pass\n content = {\n 'collectlist':collectloaded,\n 'pbeans':pbeans\n }\n return render(request,'admin-table.html',content)\n\ndef chart(request):\n citys = ['北京','上海','深圳','广州','杭州','武汉','大连','厦门','北京','天津','成都','苏州','南京']\n series_JAVA = []\n for i in citys:\n series_JAVA.append({'name':i,'data':[cityandposition('java',i)],'type':'column'})\n series_PYTHON = []\n for i in citys:\n series_PYTHON.append({'name':i,'data':[cityandposition('python',i)],'type':'column'})\n series_C = []\n for i in citys:\n series_C.append({'name':i,'data':[cityandposition('c',i)],'type':'column'})\n series_post_data = total_nums()\n series_post = [{'name': '开发', 'y': series_post_data['develop']},\n {'name': '运维', 'y': series_post_data['operation']},\n {'name': '测试', 'y': series_post_data['test']},\n {'name': '实施', 'y': series_post_data['implement']},\n ]\n devolop_data = devolop_nums()\n pie1_data = [{'name': 'java开发', 'y':devolop_data['java'] },\n {'name': 'python开发', 'y': devolop_data['python']},\n {'name': 'php开发', 'y': devolop_data['php']},\n {'name': 'ui设计', 'y': devolop_data['ui']},\n {'name': '大数据', 'y': devolop_data['bignum']},\n {'name': '前端', 'y': devolop_data['front']},\n {'name': 'C++', 'y': devolop_data['c']},]\n # pie2_data = [i for i in one_day_deal_area()]\n pie2_data = []\n area_data = area_num()\n for i in area_data:\n pie2_data.append({'name':i,'y':area_data[i]})\n context = {\n 'series_JAVA':series_JAVA,\n 'series_PYTHON':series_PYTHON,\n 'series_C':series_C,\n 'series_post':series_post,\n 'pie1_data':pie1_data,\n 'pie2_data':pie2_data\n }\n return render(request,'chart2.html',context)\n\ndef news(request):\n return render(request,'index2.html')\n\n\n\n", "sub_path": "jobweb/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 7313, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "django.core.paginator.Paginator", "line_number": 11, "usage_type": "call"}, {"api_name": "django.core.paginator.Paginator", "line_number": 23, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 41, "usage_type": "call"}, {"api_name": "urllib.parse.unquote", "line_number": 49, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 49, "usage_type": "attribute"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 53, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 53, "usage_type": "call"}, {"api_name": "urllib.parse.unquote", "line_number": 61, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 61, "usage_type": "attribute"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 64, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 64, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 71, "usage_type": "call"}, {"api_name": "urllib.parse.unquote", "line_number": 74, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 74, "usage_type": "attribute"}, {"api_name": "urllib.parse.unquote", "line_number": 78, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 78, "usage_type": "attribute"}, {"api_name": "urllib.parse.unquote", "line_number": 82, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 82, "usage_type": "attribute"}, {"api_name": "urllib.parse.unquote", "line_number": 83, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 83, "usage_type": "attribute"}, {"api_name": "django.core.paginator.Paginator", "line_number": 87, "usage_type": "call"}, {"api_name": "django.core.paginator.Paginator", "line_number": 98, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 109, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 121, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 130, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 140, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 145, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 154, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 162, "usage_type": "call"}, {"api_name": "django.core.paginator.Paginator", "line_number": 169, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 180, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 220, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 223, "usage_type": "call"}]} +{"seq_id": "544821252", "text": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport datetime\nimport redis\nfrom dateutil import parser\n\nimport constraints as c\n\n__global_db = None\n__history_db = None\n__config_db = None\n__history_db_cache = None\n\n# Tunnel: ssh remote_instance -L 6379:remote_instance:6379\n\n# Default responses\n## NOK\n__unknown_asn = {'return_code': 0, 'return_vebose': 'Unknown ASN'}\n__no_ranks = {'return_code': 0,\n 'return_vebose': 'No ranks in this timeframe.'}\n__unknown_block = {'return_code': 0,\n 'return_vebose': 'Unable to find this IP block.'}\n## OK\n__ranks = {'return_code': 1, 'return_vebose': 'Got ranks.'}\n__owner = {'return_code': 1, 'return_vebose': 'Got owner name.'}\n\ndef get_all_weights(date = None):\n \"\"\"\n Get the weights for all the sources.\n\n :param date: Date of the information (default: last ranked day)\n\n :rtype: Dictionary\n\n .. note:: Format of the dictionary:\n\n .. code-block:: python\n\n {\n source: date,\n ...\n }\n \"\"\"\n if date is None:\n date = get_default_date()\n sources = daily_sources([date])\n to_return = {}\n if len(sources) > 0:\n impacts = __config_db.mget(sources)\n to_return = dict(zip(sources, impacts))\n return to_return\n\ndef daily_sources(dates):\n \"\"\"\n Get the sources parsed during a list of dates\n\n :param dates: List of dates\n\n :rtype: Set of sources for each date\n\n \"\"\"\n if len(dates) == 1:\n return __global_db.smembers('{date}|sources'.format(date = dates[0]))\n else:\n p = __global_db.pipeline(False)\n [p.smembers('{date}|sources'.format(date = date)) for date in dates]\n return p.execute()\n\ndef prepare_sources_by_dates(last_day = None, timeframe = None):\n \"\"\"\n Get a dictionary of sources by dates.\n\n :param last_day: Last day of the interval\n :param timeframe: size of the interval\n :rtype: Dictionary\n\n .. note:: Format of the dictionary:\n\n .. code-block:: python\n\n {\n date: [source1, source2, ...],\n ...\n }\n \"\"\"\n dates = __dates_interval(last_day, timeframe)\n sources = daily_sources(dates)\n if len(dates) == 1:\n return {dates[0]: sources}\n return dict(zip(dates, sources))\n\ndef get_sources_by_dates(sources, last_day = None, timeframe = None):\n \"\"\"\n Get a dictionary of sources by dates. Only with the sources passed\n in parameter IF they exist for the day.\n :param sources: List of sources\n :param last_day: Last day of the interval\n :param timeframe: size of the interval\n\n :rtype: Dictionary\n\n .. note:: Format of the dictionary:\n\n .. code-block:: python\n\n {\n date: [source1, source2, ...],\n ...\n }\n\n\n \"\"\"\n dates = __dates_interval(last_day, timeframe)\n p = __global_db.pipeline(False)\n for date in dates:\n [p.sismember('{date}|sources'.format(date = date), source)\n for source in sources]\n exists = p.execute()\n i = 0\n to_return = {}\n for date in dates:\n if to_return.get(date) is None:\n to_return[date] = []\n for source in sources:\n if exists[i]:\n to_return[date].append(source)\n i += 1\n return to_return\n\ndef last_seen_sources(date_sources):\n \"\"\"\n Get the last time a source has been seen.\n :param dates_sources: Dictionaries of the dates and sources\n\n .. note:: Format of the dictionary:\n\n .. code-block:: python\n\n {\n YYYY-MM-DD: [source1, source2, ...],\n YYYY-MM-DD: [source1, source2, ...],\n ...\n }\n\n :rype: Dictionary\n\n .. note:: Format of the dictionary:\n\n .. code-block:: python\n\n {\n source: date,\n ...\n }\n\n \"\"\"\n last_seen = {}\n for date in sorted(date_sources.iterkeys(), reverse = True):\n for source in date_sources[date]:\n if last_seen.get(source) is None:\n last_seen[source] = date\n return last_seen\n\ndef asn_exists(asn):\n \"\"\"\n Check if the ASN exists in the database.\n \"\"\"\n return __global_db.exists(asn)\n\ndef get_default_date(delta_days=1):\n \"\"\"\n Get the latest ranked day.\n \"\"\"\n return __get_default_date_raw(delta_days).isoformat()\n\ndef __prepare():\n global __global_db\n global __history_db\n global __config_db\n global __history_db_cache\n __global_db = redis.Redis(port = c.redis_port, db = c.redis_db_global,\n host = c.redis_hostname)\n __history_db = redis.Redis(port = c.redis_port, db = c.redis_db_history,\n host = c.redis_hostname)\n __config_db = redis.Redis(port = c.redis_port, db = c.redis_db_config,\n host = c.redis_hostname)\n __history_db_cache = redis.Redis(port = c.redis_cached_port,\n db = c.redis_cached_db_history, host = c.redis_hostname)\n\n\ndef __get_default_date_raw(delta_days=1):\n \"\"\"\n Get the default date displayed on the website.\n \"\"\"\n delta = datetime.timedelta(days=delta_days)\n try:\n timestamp = __history_db.get('latest_ranking')\n except:\n # TODO: hotfix, can be better\n timestamp = None\n if timestamp is not None:\n default_date_raw = parser.parse(timestamp.split()[0]).date() - delta\n else:\n default_date_raw = datetime.date.today() - delta\n return default_date_raw\n\ndef __dates_interval(last_day = None, timeframe = None):\n if last_day is None:\n last_day = __get_default_date_raw()\n else:\n last_day = parser.parse(last_day).date()\n if timeframe is None:\n timeframe = c.default_timeframe\n return [(last_day - datetime.timedelta(days=i)).isoformat()\n for i in range(timeframe)]\n", "sub_path": "bgpranking_redis/helper_global.py", "file_name": "helper_global.py", "file_ext": "py", "file_size_in_byte": 6166, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "redis.Redis", "line_number": 182, "usage_type": "call"}, {"api_name": "constraints.redis_port", "line_number": 182, "usage_type": "attribute"}, {"api_name": "constraints.redis_db_global", "line_number": 182, "usage_type": "attribute"}, {"api_name": "constraints.redis_hostname", "line_number": 183, "usage_type": "attribute"}, {"api_name": "redis.Redis", "line_number": 184, "usage_type": "call"}, {"api_name": "constraints.redis_port", "line_number": 184, "usage_type": "attribute"}, {"api_name": "constraints.redis_db_history", "line_number": 184, "usage_type": "attribute"}, {"api_name": "constraints.redis_hostname", "line_number": 185, "usage_type": "attribute"}, {"api_name": "redis.Redis", "line_number": 186, "usage_type": "call"}, {"api_name": "constraints.redis_port", "line_number": 186, "usage_type": "attribute"}, {"api_name": "constraints.redis_db_config", "line_number": 186, "usage_type": "attribute"}, {"api_name": "constraints.redis_hostname", "line_number": 187, "usage_type": "attribute"}, {"api_name": "redis.Redis", "line_number": 188, "usage_type": "call"}, {"api_name": "constraints.redis_cached_port", "line_number": 188, "usage_type": "attribute"}, {"api_name": "constraints.redis_cached_db_history", "line_number": 189, "usage_type": "attribute"}, {"api_name": "constraints.redis_hostname", "line_number": 189, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 196, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 203, "usage_type": "call"}, {"api_name": "dateutil.parser", "line_number": 203, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 205, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 205, "usage_type": "attribute"}, {"api_name": "dateutil.parser.parse", "line_number": 212, "usage_type": "call"}, {"api_name": "dateutil.parser", "line_number": 212, "usage_type": "name"}, {"api_name": "constraints.default_timeframe", "line_number": 214, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 215, "usage_type": "call"}]} +{"seq_id": "646519632", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('servers', '0013_auto_20151027_1628'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='server',\n name='server_group',\n field=models.ForeignKey(to='servers.ServerGroup', on_delete=django.db.models.deletion.SET_NULL, null=True, blank=True),\n ),\n migrations.AlterField(\n model_name='server',\n name='server_type',\n field=models.ForeignKey(to='servers.ServerType', on_delete=django.db.models.deletion.SET_NULL, null=True, blank=True),\n ),\n ]\n", "sub_path": "ops/servers/migrations/0014_auto_20151029_1559.py", "file_name": "0014_auto_20151029_1559.py", "file_ext": "py", "file_size_in_byte": 760, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.db", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.db", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.db", "line_number": 23, "usage_type": "attribute"}, {"api_name": "django.db", "line_number": 23, "usage_type": "name"}]} +{"seq_id": "40396524", "text": "import schedule\nimport time\nfrom datetime import datetime\nfrom download import Download\ndownload = Download()\n\n\ndef time_writer():\n time_dict = download.get_time()\n if isinstance(time_dict, dict):\n try:\n my_timestamp = time_dict['serverTimestamp']\n try:\n # transfer timestamp to datetime\n my_datetime = datetime.fromtimestamp(my_timestamp/1000)\n print(my_datetime)\n # errors for unexpected input\n except ValueError as error:\n print(error)\n except KeyError as error:\n print(error)\n\n\nif __name__ == '__main__':\n print(\"Start printing time every 1 minute.\")\n time_writer()\n schedule.every(1).minutes.do(time_writer)\n while True:\n schedule.run_pending()\n time.sleep(1)\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 822, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "download.Download", "line_number": 5, "usage_type": "call"}, {"api_name": "download.get_time", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 15, "usage_type": "name"}, {"api_name": "schedule.every", "line_number": 27, "usage_type": "call"}, {"api_name": "schedule.run_pending", "line_number": 29, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "376159223", "text": "import time\nimport re\nimport json\n\nfrom django.shortcuts import get_object_or_404\nfrom django.http import Http404\nfrom django.conf import settings\nfrom django.db.models import Max\n\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom rest_framework import status\nfrom rest_framework import viewsets\nfrom rest_framework.decorators import api_view, permission_classes\nfrom rest_framework import permissions\nfrom rest_framework import mixins\n\nfrom lner.models import LightningNode\nfrom lner.models import Invoice\nfrom lner.models import InvoiceRequest\nfrom lner.models import VerifyMessageResult\nfrom lner.models import PayAwardResult\n\nfrom bounty.models import Bounty, BountyAward\n\nfrom lner.serializers import LightningNodeSerializer\nfrom lner.serializers import InvoiceSerializer\nfrom lner.serializers import InvoiceRequestSerializer\nfrom lner.serializers import CheckPaymentSerializer\nfrom lner.serializers import VerifyMessageResponseSerializer\nfrom lner.serializers import PayAwardResponseSerializer\n\nfrom common import log\nfrom common import lnclient\nfrom common import validators\nfrom common import json_util\n\nfrom common.const import MEMO_RE\n\n\nlogger = log.getLogger(\"lner.views\")\n\n\nclass LightningNodeViewSet(viewsets.ModelViewSet):\n \"\"\"\n List all available lightning nodes\n \"\"\"\n queryset = LightningNode.objects.all()\n serializer_class = LightningNodeSerializer\n\n\nclass CreateInvoiceError(Exception):\n pass\n\n\nclass CreateInvoiceViewSet(viewsets.ModelViewSet):\n \"\"\"\n Create a new lightning invoice\n \"\"\"\n queryset = []\n serializer_class = InvoiceRequestSerializer\n\n MAX_RETRIES = 3\n RETRY_SLEEP_SECONDS = 1\n\n def create(self, request, format=None, retry_addinvoice=False, retry_num=0):\n memo = request.POST[\"memo\"]\n\n if retry_num >= CreateInvoiceViewSet.MAX_RETRIES:\n raise CreateInvoiceError(\n \"Retry count exceeded: {}\".format(retry_num)\n )\n\n node = LightningNode.objects.get(id=request.POST[\"node_id\"])\n request_obj, created = InvoiceRequest.objects.get_or_create(\n lightning_node=node,\n memo=memo\n )\n\n if created or retry_addinvoice:\n logger.info(\"New invoice request created: {}\".format(request_obj))\n # InvoiceRequest just got created? do:\n # 1. addinvoice RPC to the node\n # 2. create Invoice\n\n if settings.MOCK_LN_CLIENT:\n invoice_stdout = {}\n\n invoice_stdout[\"pay_req\"] = \"FAKE\"\n invoice_stdout[\"r_hash\"] = \"FAKE\"\n invoice_stdout[\"node_id\"] = node.id\n\n if len(Invoice.objects.all()) == 0:\n invoice_stdout[\"add_index\"] = 1\n else:\n # TODO: Mock multiple nodes. Currently Mock uses Invoice.objects.aggregate which ignores node.\n invoice_stdout[\"add_index\"] = Invoice.objects.aggregate(Max('add_index'))[\"add_index__max\"] + 1\n\n serializer = InvoiceSerializer(data=invoice_stdout, many=False) # re-serialize\n is_valid = serializer.is_valid(raise_exception=False) # validate data going into the database\n\n invoice_obj = Invoice(\n invoice_request=request_obj,\n lightning_node=node,\n pay_req=serializer.validated_data.get(\"pay_req\"),\n r_hash=serializer.validated_data.get(\"r_hash\"),\n add_index=serializer.validated_data.get(\"add_index\")\n )\n invoice_obj.save()\n return Response(serializer.validated_data)\n\n else:\n # TODO: surface addinvoice timeout and other exceptions back to the user\n # Bonties can specify amount in the memo, everithing else defaults to settings.PAYMENT_AMOUNT\n deserialized_memo = json_util.deserialize_memo(memo)\n invoice_stdout = lnclient.addinvoice(\n memo,\n node.rpcserver,\n amt=deserialized_memo.get(\"amt\", settings.PAYMENT_AMOUNT),\n expiry=settings.INVOICE_EXPIRY,\n )\n logger.info(\"Finished addinvoice on the node\")\n\n invoice_stdout[\"node_id\"] = node.id\n if \"payment_request\" in invoice_stdout:\n # lncli returns \"payment_request\" instead of \"pay_req\", probably since\n # commit 8f5d78c875b8eca436f7ee2e86e743afee262386 (Dec 20 2019) build+lncli: default to hex encoding for byte slices\n invoice_stdout[\"pay_req\"] = invoice_stdout[\"payment_request\"]\n\n serializer = InvoiceSerializer(data=invoice_stdout, many=False) # re-serialize\n is_valid = serializer.is_valid(raise_exception=False) # validate data going into the database\n\n if not is_valid:\n msg = \"Output of addinvoice was not valid: errors={} stdout={}\".format(serializer.errors, invoice_stdout)\n logger.error(msg)\n raise CreateInvoiceError(msg)\n\n invoice_obj = Invoice(\n invoice_request=request_obj,\n lightning_node=node,\n pay_req=serializer.validated_data.get(\"pay_req\"),\n r_hash=serializer.validated_data.get(\"r_hash\"),\n add_index=serializer.validated_data.get(\"add_index\")\n )\n logger.info(\"New invoice created! {}\".format(invoice_obj))\n\n invoice_obj.save()\n logger.info(\"Saved results of addinvoice to DB\")\n\n return Response(serializer.validated_data)\n\n else:\n logger.info(\"Good it already exists: {}\".format(request_obj))\n try:\n invoice_obj = Invoice.objects.get(invoice_request=request_obj, lightning_node_id=node.id)\n except Invoice.DoesNotExist:\n logger.info(\"Re-trying to create new invoice\")\n retry_num += 1\n time.sleep(CreateInvoiceViewSet.RETRY_SLEEP_SECONDS)\n return self.create(request, format=format, retry_addinvoice=True, retry_num=retry_num)\n\n logger.info(\"Fetched invoice from DB: {}\".format(invoice_obj))\n invoice_obj.node_id = node.id\n serializer = InvoiceSerializer(invoice_obj)\n\n if serializer.is_valid:\n logger.info(\"Invoice is valid\")\n else:\n msg = \"Invoice is NOT valid, errors: {}\".format(serializer.errors)\n logger.error(msg)\n raise CreateInvoiceError(msg)\n\n return Response(serializer.data)\n\n\nclass CheckPaymentViewSet(viewsets.ModelViewSet):\n \"\"\"\n Check invoice to see if payment was settled\n \"\"\"\n\n queryset = []\n serializer_class = CheckPaymentSerializer\n\n def get_queryset(self):\n memo = self.request.query_params.get(\"memo\")\n node_id = self.request.query_params.get(\"node_id\")\n\n assert re.match(MEMO_RE, memo), \"Got invalid memo {}\".format(memo)\n\n invoice_request = get_object_or_404(InvoiceRequest, memo=memo, lightning_node_id=node_id)\n invoice = get_object_or_404(Invoice, invoice_request=invoice_request, lightning_node_id=node_id)\n\n return [invoice]\n\n\n\nclass VerifyMessageViewSet(viewsets.ModelViewSet):\n \"\"\"\n Check message against a signature\n \"\"\"\n\n queryset = []\n serializer_class = VerifyMessageResponseSerializer\n\n def get_queryset(self):\n memo = self.request.query_params.get(\"memo\")\n sig = self.request.query_params.get(\"sig\")\n\n assert memo is not None, \"Missing a required field: memo\"\n assert sig is not None, \"Missing a required field: sig\"\n\n sig = validators.pre_validate_signature(sig)\n\n node = LightningNode.objects.filter(enabled=True).order_by(\"-qos_score\").first()\n\n result_json = lnclient.verifymessage(msg=memo, sig=sig, rpcserver=node.rpcserver, mock=settings.MOCK_LN_CLIENT)\n pubkey = result_json[\"pubkey\"]\n valid = result_json[\"valid\"]\n\n verify_message_result = VerifyMessageResult(memo=memo, identity_pubkey=pubkey, valid=valid)\n\n return [verify_message_result]\n\n\ndef payment_fail(msg):\n logger.error(msg)\n return [PayAwardResult(payment_successful=False, failure_message=msg)]\n\n\nclass PayAwardViewSet(viewsets.ModelViewSet):\n \"\"\"\n Check message against a signature\n \"\"\"\n\n queryset = []\n serializer_class = PayAwardResponseSerializer\n\n def get_queryset(self):\n node_id = self.request.query_params.get(\"node_id\")\n award_id = self.request.query_params.get(\"award_id\")\n invoice = self.request.query_params.get(\"invoice\")\n sig = self.request.query_params.get(\"sig\")\n\n assert invoice is not None, \"Missing a required field: invoice\"\n assert sig is not None, \"Missing a required field: sig\"\n\n sig = validators.pre_validate_signature(sig)\n\n node_id = self.request.query_params.get(\"node_id\")\n logger.info(\"Looking up node id: {}\".format(node_id))\n\n # Lookup node\n node = get_object_or_404(LightningNode.objects, id=node_id)\n if not node.enabled:\n return payment_fail(\"Node is not enabled, try a different node\")\n\n sig_verify_json = lnclient.verifymessage(msg=invoice, sig=sig, rpcserver=node.rpcserver, mock=settings.MOCK_LN_CLIENT)\n logger.info(\"Attempting to pay award for: {}\".format(sig_verify_json))\n\n valid = sig_verify_json[\"valid\"]\n sig_pubkey = sig_verify_json[\"pubkey\"]\n\n if not valid:\n return payment_fail(\"Signature is invalid\")\n\n # Lookup Award\n award = BountyAward.objects.get(id=award_id)\n\n # Check award recipient\n award_pubkey = award.post.author.pubkey\n if award_pubkey != sig_pubkey:\n return payment_fail(\"Incorrect signature, this award will be payed out only to {}\".format(award_pubkey))\n\n # Calculate award amount\n # TODO: put into a shard function get_bounty_sats\n bounty_sats = 0\n\n bounties_to_pay = []\n for b in Bounty.objects.filter(post_id=award.post.parent.id, is_active=True, is_payed=False):\n bounties_to_pay.append(b)\n bounty_sats += b.amt\n\n logger.info(\"Need to pay award in the amount of: {} sat\".format(bounty_sats))\n\n # Decode invoice and lookup amount\n decodepayreq_out = lnclient.decodepayreq(payreq=invoice, rpcserver=node.rpcserver, mock=settings.MOCK_LN_CLIENT)\n if decodepayreq_out[\"success\"] is not True:\n if decodepayreq_out[\"failure_type\"] == \"timeout\":\n return payment_fail(\"LND decodepayreq timed out\")\n else:\n # TODO: from stdouterr remove anything that looks like an IP address\n # E.g. [lncli] rpc error: code = Unknown desc = caveat \"ipaddr 172.1.1.1\" not satisfied: macaroon locked to different IP address\n return payment_fail(\"LND decodepayreq failed. LND error message was: {}\".format(decodepayreq_out[\"stdouterr\"]))\n\n payreq_decoded = json.loads(decodepayreq_out[\"stdouterr\"])\n\n num_satoshis = payreq_decoded[\"num_satoshis\"]\n num_msat = payreq_decoded[\"num_msat\"]\n logger.info(\"User requested: num_satoshis={} and num_msat={} \".format(num_satoshis, num_msat))\n\n if int(bounty_sats) == 0:\n return payment_fail(\"This bounty has already been payed out\")\n\n # Check invoice amount\n if not settings.MOCK_LN_CLIENT:\n if int(bounty_sats) != int(num_satoshis):\n return payment_fail(\n (\n \"Invoice num_satoshis amount is incorrect, \"\n \"we expect to send you {} sats, yet the invoice says {}\"\n ).format(bounty_sats, num_satoshis)\n )\n\n if int(bounty_sats) != int(int(num_msat) / 1000):\n return payment_fail(\n (\n \"Invoice num_satoshis amount is incorrect, \"\n \"we expect to send you {} sats, yet your invoice says {} msats which is {} sats\"\n ).format(\n bounty_sats,\n num_msat,\n int(int(num_msat) / 1000)\n )\n )\n\n logger.info(\"Entered critical section\")\n\n # ! TODO (2020-05-19): Check for recent payments on all nodes, in case we crash in the middle of critical section\n\n logger.info(\"about to pay\")\n\n pay_result = lnclient.payinvoice(payreq=invoice, rpcserver=node.rpcserver, mock=settings.MOCK_LN_CLIENT)\n logger.info(\"pay_result: {}\".format(pay_result))\n\n if pay_result[\"success\"] is not True:\n if pay_result[\"failure_type\"] == \"timeout\":\n return payment_fail(\"LND payinvoice timed out\")\n else:\n # TODO: from stdouterr remove anything that looks like an IP address\n # E.g. [lncli] rpc error: code = Unknown desc = caveat \"ipaddr 172.1.1.1\" not satisfied: macaroon locked to different IP address\n return payment_fail(\"LND payinvoice failed. LND error message was: {}\".format(pay_result[\"stdouterr\"]))\n\n logger.info(\"payed, about to update db\")\n\n for b in bounties_to_pay:\n b.is_payed = True\n b.is_active = False\n b.save()\n\n logger.info(\"updated db\")\n\n logger.info(\"Exited critical section\")\n\n return [PayAwardResult(payment_successful=True)]\n", "sub_path": "writer/project-basedir/lner/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 13709, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "common.log.getLogger", "line_number": 41, "usage_type": "call"}, {"api_name": "common.log", "line_number": 41, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 44, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 44, "usage_type": "name"}, {"api_name": "lner.models.LightningNode.objects.all", "line_number": 48, "usage_type": "call"}, {"api_name": "lner.models.LightningNode.objects", "line_number": 48, "usage_type": "attribute"}, {"api_name": "lner.models.LightningNode", "line_number": 48, "usage_type": "name"}, {"api_name": "lner.serializers.LightningNodeSerializer", "line_number": 49, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 56, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 56, "usage_type": "name"}, {"api_name": "lner.serializers.InvoiceRequestSerializer", "line_number": 61, "usage_type": "name"}, {"api_name": "lner.models.LightningNode.objects.get", "line_number": 74, "usage_type": "call"}, {"api_name": "lner.models.LightningNode.objects", "line_number": 74, "usage_type": "attribute"}, {"api_name": "lner.models.LightningNode", "line_number": 74, "usage_type": "name"}, {"api_name": "lner.models.InvoiceRequest.objects.get_or_create", "line_number": 75, "usage_type": "call"}, {"api_name": "lner.models.InvoiceRequest.objects", "line_number": 75, "usage_type": "attribute"}, {"api_name": "lner.models.InvoiceRequest", "line_number": 75, "usage_type": "name"}, {"api_name": "django.conf.settings.MOCK_LN_CLIENT", "line_number": 86, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 86, "usage_type": "name"}, {"api_name": "lner.models.Invoice.objects.all", "line_number": 93, "usage_type": "call"}, {"api_name": "lner.models.Invoice.objects", "line_number": 93, "usage_type": "attribute"}, {"api_name": "lner.models.Invoice", "line_number": 93, "usage_type": "name"}, {"api_name": "lner.models.Invoice.objects.aggregate", "line_number": 97, "usage_type": "call"}, {"api_name": "lner.models.Invoice.objects", "line_number": 97, "usage_type": "attribute"}, {"api_name": "lner.models.Invoice", "line_number": 97, "usage_type": "name"}, {"api_name": "django.db.models.Max", "line_number": 97, "usage_type": "call"}, {"api_name": "lner.serializers.InvoiceSerializer", "line_number": 99, "usage_type": "call"}, {"api_name": "lner.models.Invoice", "line_number": 102, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 110, "usage_type": "call"}, {"api_name": "common.json_util.deserialize_memo", "line_number": 115, "usage_type": "call"}, {"api_name": "common.json_util", "line_number": 115, "usage_type": "name"}, {"api_name": "common.lnclient.addinvoice", "line_number": 116, "usage_type": "call"}, {"api_name": "common.lnclient", "line_number": 116, "usage_type": "name"}, {"api_name": "django.conf.settings.PAYMENT_AMOUNT", "line_number": 119, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 119, "usage_type": "name"}, {"api_name": "django.conf.settings.INVOICE_EXPIRY", "line_number": 120, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 120, "usage_type": "name"}, {"api_name": "lner.serializers.InvoiceSerializer", "line_number": 130, "usage_type": "call"}, {"api_name": "lner.models.Invoice", "line_number": 138, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 150, "usage_type": "call"}, {"api_name": "lner.models.Invoice.objects.get", "line_number": 155, "usage_type": "call"}, {"api_name": "lner.models.Invoice.objects", "line_number": 155, "usage_type": "attribute"}, {"api_name": "lner.models.Invoice", "line_number": 155, "usage_type": "name"}, {"api_name": "lner.models.Invoice.DoesNotExist", "line_number": 156, "usage_type": "attribute"}, {"api_name": "lner.models.Invoice", "line_number": 156, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 159, "usage_type": "call"}, {"api_name": "lner.serializers.InvoiceSerializer", "line_number": 164, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 173, "usage_type": "call"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 176, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 176, "usage_type": "name"}, {"api_name": "lner.serializers.CheckPaymentSerializer", "line_number": 182, "usage_type": "name"}, {"api_name": "re.match", "line_number": 188, "usage_type": "call"}, {"api_name": "common.const.MEMO_RE", "line_number": 188, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 190, "usage_type": "call"}, {"api_name": "lner.models.InvoiceRequest", "line_number": 190, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 191, "usage_type": "call"}, {"api_name": "lner.models.Invoice", "line_number": 191, "usage_type": "argument"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 197, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 197, "usage_type": "name"}, {"api_name": "lner.serializers.VerifyMessageResponseSerializer", "line_number": 203, "usage_type": "name"}, {"api_name": "common.validators.pre_validate_signature", "line_number": 212, "usage_type": "call"}, {"api_name": "common.validators", "line_number": 212, "usage_type": "name"}, {"api_name": "lner.models.LightningNode.objects.filter", "line_number": 214, "usage_type": "call"}, {"api_name": "lner.models.LightningNode.objects", "line_number": 214, "usage_type": "attribute"}, {"api_name": "lner.models.LightningNode", "line_number": 214, "usage_type": "name"}, {"api_name": "common.lnclient.verifymessage", "line_number": 216, "usage_type": "call"}, {"api_name": "common.lnclient", "line_number": 216, "usage_type": "name"}, {"api_name": "django.conf.settings.MOCK_LN_CLIENT", "line_number": 216, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 216, "usage_type": "name"}, {"api_name": "lner.models.VerifyMessageResult", "line_number": 220, "usage_type": "call"}, {"api_name": "lner.models.PayAwardResult", "line_number": 227, "usage_type": "call"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 230, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 230, "usage_type": "name"}, {"api_name": "lner.serializers.PayAwardResponseSerializer", "line_number": 236, "usage_type": "name"}, {"api_name": "common.validators.pre_validate_signature", "line_number": 247, "usage_type": "call"}, {"api_name": "common.validators", "line_number": 247, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 253, "usage_type": "call"}, {"api_name": "lner.models.LightningNode.objects", "line_number": 253, "usage_type": "attribute"}, {"api_name": "lner.models.LightningNode", "line_number": 253, "usage_type": "name"}, {"api_name": "common.lnclient.verifymessage", "line_number": 257, "usage_type": "call"}, {"api_name": "common.lnclient", "line_number": 257, "usage_type": "name"}, {"api_name": "django.conf.settings.MOCK_LN_CLIENT", "line_number": 257, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 257, "usage_type": "name"}, {"api_name": "bounty.models.BountyAward.objects.get", "line_number": 267, "usage_type": "call"}, {"api_name": "bounty.models.BountyAward.objects", "line_number": 267, "usage_type": "attribute"}, {"api_name": "bounty.models.BountyAward", "line_number": 267, "usage_type": "name"}, {"api_name": "bounty.models.Bounty.objects.filter", "line_number": 279, "usage_type": "call"}, {"api_name": "bounty.models.Bounty.objects", "line_number": 279, "usage_type": "attribute"}, {"api_name": "bounty.models.Bounty", "line_number": 279, "usage_type": "name"}, {"api_name": "common.lnclient.decodepayreq", "line_number": 286, "usage_type": "call"}, {"api_name": "common.lnclient", "line_number": 286, "usage_type": "name"}, {"api_name": "django.conf.settings.MOCK_LN_CLIENT", "line_number": 286, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 286, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 295, "usage_type": "call"}, {"api_name": "django.conf.settings.MOCK_LN_CLIENT", "line_number": 305, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 305, "usage_type": "name"}, {"api_name": "common.lnclient.payinvoice", "line_number": 332, "usage_type": "call"}, {"api_name": "common.lnclient", "line_number": 332, "usage_type": "name"}, {"api_name": "django.conf.settings.MOCK_LN_CLIENT", "line_number": 332, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 332, "usage_type": "name"}, {"api_name": "lner.models.PayAwardResult", "line_number": 354, "usage_type": "call"}]} +{"seq_id": "322980454", "text": "import serial\nimport time\n\nser = serial.Serial(\n\tport = '/dev/serial0',\n\tbaudrate = 4800,\n\tparity = serial.PARITY_NONE,\n\tstopbits = serial.STOPBITS_ONE,\n\tbytesize = serial.EIGHTBITS,\n\ttimeout = 1\n)\n\nser.close()\nser.open()\n\ndef main(downlink):\n\tcounter = 0\n\twhile True:\n#\t\ta = downlink.get()\n\t\ta = (\"hi micah\\n\")\n\t\tprint('message: ' + a)\n\t\tser.write(a.encode('utf-8'))\n#\t\tprint(\"packet sent\",counter)\n\t\ttime.sleep(1)\n\t\tcounter+=1\n", "sub_path": "dwlk/testdwlk.py", "file_name": "testdwlk.py", "file_ext": "py", "file_size_in_byte": 429, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "serial.Serial", "line_number": 4, "usage_type": "call"}, {"api_name": "serial.PARITY_NONE", "line_number": 7, "usage_type": "attribute"}, {"api_name": "serial.STOPBITS_ONE", "line_number": 8, "usage_type": "attribute"}, {"api_name": "serial.EIGHTBITS", "line_number": 9, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "551701294", "text": "import json\nimport requests\nimport tree as tr\nimport chess.pgn\nimport io\n\n\n# Returns list of dictionaries received from server\ndef getAPI(query):\n queries = []\n req = \"https://api.chess.com/pub/player/\" + query\n queries.append(requests.get(req).json())\n return queries\n\n\n# Returns list of dictionaries containing all games ever played.\ndef getGames(user):\n req = \"https://api.chess.com/pub/player/\" + user + \"/games/archives\"\n li = requests.get(req).json()[\"archives\"]\n li_games = []\n for x in li:\n li_games.append(requests.get(x).json())\n all_games = []\n for x in li_games:\n all_games.extend(x[\"games\"])\n return all_games\n\n\n# Prints list of dictionaries in a formatted manner\ndef display(li):\n for x in li:\n print(json.dumps(x, indent=4))\n\n\n# Filter to only classical games\ndef filterList(li, user):\n for x in li:\n if x[\"rules\"] != \"chess\":\n li.remove(x)\n\n x[\"black\"].pop(\"rating\")\n x[\"white\"].pop(\"@id\")\n if x[\"white\"][\"username\"] == user:\n x.update({\"color\": \"white\", \"result\": x[\"white\"][\"result\"]})\n else:\n x.update({\"color\": \"black\", \"result\": x[\"black\"][\"result\"]})\n\n j = [\"url\", \"rated\", \"time_control\", \"end_time\", \"fen\", \"time_class\", \"rules\", \"white\", \"black\"]\n for k in j:\n x.pop(k)\n\n\ndef buildOpeningTree(openings):\n tree = tr.Tree()\n for x in reversed(openings):\n li = openings[x].split(\" \")\n tree.builder(tree.root, x, li)\n return tree\n\n\ndef traverseToNode(trie):\n s = \"e2e4\"\n p = s.split(\" \")\n q = trie.traverse(p, trie.root)\n\n\ndef otherMethodCalls():\n # query1 = getAPI(username)\n # query2 = getAPI(username+\"/stats\")\n # display([query1,query2])\n pass\n\n\ndef convertPGN(openings, white, black):\n for x in openings:\n pgn = io.StringIO(x[\"pgn\"])\n game = chess.pgn.read_game(pgn)\n li = []\n for move in game.mainline_moves():\n li.append(str(move))\n if x[\"color\"] == \"white\":\n white.insertGames(li, white.root, x[\"result\"])\n else:\n white.insertGames(li, black.root, x[\"result\"])\n\n\n#def findFavOpening(openings, trie, )\n\n", "sub_path": "methods.py", "file_name": "methods.py", "file_ext": "py", "file_size_in_byte": 2206, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "requests.get", "line_number": 12, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 19, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 22, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 32, "usage_type": "call"}, {"api_name": "tree.Tree", "line_number": 54, "usage_type": "call"}, {"api_name": "tree.builder", "line_number": 57, "usage_type": "call"}, {"api_name": "tree.root", "line_number": 57, "usage_type": "attribute"}, {"api_name": "io.StringIO", "line_number": 76, "usage_type": "call"}, {"api_name": "chess.pgn.pgn.read_game", "line_number": 77, "usage_type": "call"}, {"api_name": "chess.pgn.pgn", "line_number": 77, "usage_type": "attribute"}, {"api_name": "chess.pgn", "line_number": 77, "usage_type": "name"}]} +{"seq_id": "298103379", "text": "#Importeren van module voor het inlezen van data van de planeten en voor het \n#gebruik van pi\n\nfrom astropy.table import Table\n\n#Natuurkundige constantes\n\nG = 6.673e-11 #m3s-2kg-1\nh = 6.626e-34 #m2kgs-1\nc = 2.998e-11 #ms-1\nAU = 1.496e11 #m\nMz = 1.9889e30 #kg\nRz = 6.9634e8 #m\nMa = 5.9722e24 #kg\nRa = 6.371e6 #m \nMj = 1.898e27 #kg\npc = 3.08568e16 #m\nMm = 7.342e22 #kg\nDam = 384400e3 #m\n\n#Formule zwaartekracht\n\ndef Fgrav(m1,m2,r):\n F = (m1 * m2)/(r**2) * G\n return F\n\n#Versnellingen van het ISS door verschillende hemellichamen. Aangezien a = F/m\n#valt de massa van het ISS(m2) weg, dus vullen we daarvoor 1 in.\n\na_ISS_aarde = Fgrav(Ma,1, Ra + 407000)\nprint(a_ISS_aarde)\na_ISS_zon = Fgrav(Mz, 1, AU)\nprint(a_ISS_zon)\na_ISS_maan = Fgrav(Mm, 1, Dam - 407000)\nprint(a_ISS_maan)\n\n#Importeren van de gegevens van sommige planeten\n\nplaneten = Table.read(\"planeet-gegevens.txt\", format='csv').to_pandas(index\n ='name')\n\n#De nodige modules importeren\nfrom matplotlib import pyplot as plt\n\ny = planeten[\"density\"]\nx = planeten[\"Rmean\"]\n\nplt.plot(x, y, \"ro\", label= \"planeten\")\nplt.hlines(0.997,0,8e4, label= \"dichtheid water\")\nplt.hlines(7.874,0,5e4, label= \"dichtheid ijzer\")\nplt.hlines(1.5 , 0, 8e4, label= \"dichtheid zand\")\nplt.title(\"Dichtheid tegen straal van 8 planeten\")\nplt.xlabel(\"Straal in km\")\nplt.ylabel(\"Dichtheid in g/cm3\")\nplt.legend(loc=\"upper right\")", "sub_path": "astronomy.py", "file_name": "astronomy.py", "file_ext": "py", "file_size_in_byte": 1381, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "astropy.table.Table.read", "line_number": 39, "usage_type": "call"}, {"api_name": "astropy.table.Table", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hlines", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hlines", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hlines", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}]} +{"seq_id": "102006565", "text": "# vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4 mouse=a\nimport nipype.pipeline.engine as pe\nimport os\nimport nipype.interfaces.utility as niu\nfrom nipype.interfaces.base import (TraitedSpec, File, traits, InputMultiPath,\n BaseInterface, OutputMultiPath, BaseInterfaceInputSpec, isdefined)\nfrom nipype.utils.filemanip import (load_json, save_json, split_filename, fname_presuffix, copyfile)\n\nimport numpy as np\nimport ntpath\nfrom nipype.interfaces.base import CommandLine, CommandLineInputSpec\nfrom nipype.interfaces.base import (TraitedSpec, File, traits, InputMultiPath,isdefined)\n\nclass PVCInput(CommandLineInputSpec):\n out_file = File(position=3, argstr=\"-o %s\",desc=\"image to operate on\")\n mask = File( position=2, argstr=\"-mask %s\", desc=\"Integer mask file\")\n input_file = File(exists=True, position=1, argstr=\"-pet %s\", desc=\"PET file\")\n z_fwhm = traits.Float( argstr=\"-z %f\", desc=\"FWHM of PSF along z-axis\")\n y_fwhm = traits.Float( argstr=\"-y %f\", desc=\"FWHM of PSF along y-axis\")\n x_fwhm = traits.Float( argstr=\"-x %f\", desc=\"FWHM of PSF along x-axis\")\n fwhm = traits.Float( argstr=\"-fwhm %f\", desc=\"FWHM of PSF all axes\")\n \n max_iterations = traits.Int(argstr=\"-max-iterations %d\", desc=\"Maximum number of iterations\")\n tolerance = traits.Float( argstr=\"-tolerance %f\", desc=\"Tolerance\")\n denoise_fwhm = traits.Float( argstr=\"-denoise_fwhm %f\", desc=\"FWHM for denoising image\")\n lambda_var = traits.Float( argstr=\"-lambda %f\", desc=\"Lambda for controlling smoothing across regions\")\n nvoxel_to_average = traits.Int( argstr=\"-nvoxel-to-average %f\", desc=\"Number of voxels to average over.\")\n pvc_method = traits.Str(argstr=\"--pvc %s\",mandatory=False, desc=\"PVC type\")\n \nclass PVCOutput(TraitedSpec):\n out_file = File(argstr=\"-o %s\", exists=True, desc=\"Output PET image\")\n\nclass PVCCommand(CommandLine):\n input_spec = PVCInput\n output_spec = PVCOutput\n _cmd='gtm'\n _suffix='_gtm'\n #def __init__(self, pvc_method):\n # pass\n #self._cmd = pvc_method \n # self._suffix = \"_\" + self._cmd \n\n def _list_outputs(self):\n outputs = self.output_spec().get()\n outputs[\"out_file\"] = self.inputs.out_file\n return outputs\n\n def _gen_filename(self, name):\n if name == \"out_file\":\n return self._list_outputs()[\"out_file\"]\n return None\n\n def _gen_output(self, basefile, _suffix):\n fname = ntpath.basename(basefile)\n fname_list = os.path.splitext(fname) # [0]= base filename; [1] =extension\n dname = os.getcwd() \n return dname+ os.sep+fname_list[0] + _suffix + fname_list[1]\n\n def _parse_inputs(self, skip=None):\n if skip is None:\n skip = []\n if not isdefined(self.inputs.out_file):\n self.inputs.out_file = self._gen_output(self.inputs.input_file, self._suffix)\n return super(PVCCommand, self)._parse_inputs(skip=skip)\n\n\ndef get_workflow(name, infosource, datasink, opts):\n\n workflow = pe.Workflow(name=name)\n\n #Define input node that will receive input from outside of workflow\n inputnode = pe.Node(niu.IdentityInterface(fields=[\"pet_center\", \"pet_mask\"]), name='inputnode')\n\n #Define empty node for output\n outputnode = pe.Node(niu.IdentityInterface(fields=[\"out_file\"]), name='outputnode')\n node_name=opts.pvc_method\n PVCNode = pe.Node(interface=PVCCommand(), name=node_name)\n if opts.pvc_method == \"gtm\":\n PVCNode.inputs.fwhm = opts.scanner_fwhm[0]\n elif opts.pvc_method == \"idSURF\":\n PVCNode.inputs.max_iterations = opts.max_iterations\n PVCNode.inputs.tolerance = opts.tolerance\n PVCNode.inputs.denoise_fwhm = opts.denoise_fwhm\n PVCNode.inputs.lambda_var = opts.lambda_var\n PVCNode.inputs.nvoxel_to_average=opts.nvoxel_to_average\n else:\n PVCNode.inputs.z_fwhm = opts.scanner_fwhm[0]\n PVCNode.inputs.y_fwhm = opts.scanner_fwhm[1]\n PVCNode.inputs.x_fwhm = opts.scanner_fwhm[2]\n PVCNode.inputs.pvc_method = opts.pvc_method\n workflow.connect([\n (inputnode, PVCNode, [('pet_center','input_file')]),\n (inputnode, PVCNode, [('pet_mask','mask')])\n ])\n workflow.connect(PVCNode, 'out_file', datasink, 'pvc')\n workflow.connect(PVCNode, 'out_file', outputnode, \"out_file\")\n\n return(workflow)\n\n", "sub_path": "Partial_Volume_Correction/pvc.py", "file_name": "pvc.py", "file_ext": "py", "file_size_in_byte": 4392, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "nipype.interfaces.base.CommandLineInputSpec", "line_number": 14, "usage_type": "name"}, {"api_name": "nipype.interfaces.base.File", "line_number": 15, "usage_type": "call"}, {"api_name": "nipype.interfaces.base.File", "line_number": 16, "usage_type": "call"}, {"api_name": "nipype.interfaces.base.File", "line_number": 17, "usage_type": "call"}, {"api_name": "nipype.interfaces.base.traits.Float", "line_number": 18, "usage_type": "call"}, {"api_name": "nipype.interfaces.base.traits", "line_number": 18, "usage_type": "name"}, {"api_name": "nipype.interfaces.base.traits.Float", "line_number": 19, "usage_type": "call"}, {"api_name": "nipype.interfaces.base.traits", "line_number": 19, "usage_type": "name"}, {"api_name": "nipype.interfaces.base.traits.Float", "line_number": 20, "usage_type": "call"}, {"api_name": "nipype.interfaces.base.traits", "line_number": 20, "usage_type": "name"}, {"api_name": "nipype.interfaces.base.traits.Float", "line_number": 21, "usage_type": "call"}, {"api_name": "nipype.interfaces.base.traits", "line_number": 21, "usage_type": "name"}, {"api_name": "nipype.interfaces.base.traits.Int", "line_number": 23, "usage_type": "call"}, {"api_name": "nipype.interfaces.base.traits", "line_number": 23, "usage_type": "name"}, {"api_name": "nipype.interfaces.base.traits.Float", "line_number": 24, "usage_type": "call"}, {"api_name": "nipype.interfaces.base.traits", "line_number": 24, "usage_type": "name"}, {"api_name": "nipype.interfaces.base.traits.Float", "line_number": 25, "usage_type": "call"}, {"api_name": "nipype.interfaces.base.traits", "line_number": 25, "usage_type": "name"}, {"api_name": "nipype.interfaces.base.traits.Float", "line_number": 26, "usage_type": "call"}, {"api_name": "nipype.interfaces.base.traits", "line_number": 26, "usage_type": "name"}, {"api_name": "nipype.interfaces.base.traits.Int", "line_number": 27, "usage_type": "call"}, {"api_name": "nipype.interfaces.base.traits", "line_number": 27, "usage_type": "name"}, {"api_name": "nipype.interfaces.base.traits.Str", "line_number": 28, "usage_type": "call"}, {"api_name": "nipype.interfaces.base.traits", "line_number": 28, "usage_type": "name"}, {"api_name": "nipype.interfaces.base.TraitedSpec", "line_number": 30, "usage_type": "name"}, {"api_name": "nipype.interfaces.base.File", "line_number": 31, "usage_type": "call"}, {"api_name": "nipype.interfaces.base.CommandLine", "line_number": 33, "usage_type": "name"}, {"api_name": "ntpath.basename", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 56, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 57, "usage_type": "attribute"}, {"api_name": "nipype.interfaces.base.isdefined", "line_number": 62, "usage_type": "call"}, {"api_name": "nipype.pipeline.engine.Workflow", "line_number": 69, "usage_type": "call"}, {"api_name": "nipype.pipeline.engine", "line_number": 69, "usage_type": "name"}, {"api_name": "nipype.pipeline.engine.Node", "line_number": 72, "usage_type": "call"}, {"api_name": "nipype.pipeline.engine", "line_number": 72, "usage_type": "name"}, {"api_name": "nipype.interfaces.utility.IdentityInterface", "line_number": 72, "usage_type": "call"}, {"api_name": "nipype.interfaces.utility", "line_number": 72, "usage_type": "name"}, {"api_name": "nipype.pipeline.engine.Node", "line_number": 75, "usage_type": "call"}, {"api_name": "nipype.pipeline.engine", "line_number": 75, "usage_type": "name"}, {"api_name": "nipype.interfaces.utility.IdentityInterface", "line_number": 75, "usage_type": "call"}, {"api_name": "nipype.interfaces.utility", "line_number": 75, "usage_type": "name"}, {"api_name": "nipype.pipeline.engine.Node", "line_number": 77, "usage_type": "call"}, {"api_name": "nipype.pipeline.engine", "line_number": 77, "usage_type": "name"}]} +{"seq_id": "112978736", "text": "import cv2 as cv\r\nimport numpy\r\nimport os\r\nimport re\r\n\r\nimport face_extractor\r\n\r\n\r\nclass Codebook:\r\n \"\"\"Codebook conatins larened feature vectors saved in txt files,\r\n each feature vector consist of calculated HOG for image,\r\n txt filenames in codebook corresponds to image file from learning set \"\"\"\r\n\r\n def __init__(self):\r\n '''\r\n constructor function for the class\r\n '''\r\n self.templates_list = []\r\n self.ext_list = ['.jpg', '.bmp', '.png']\r\n\r\n def create_codebook(self, in_path='cropped_images', out_path='Codebook'):\r\n '''\r\n Main function of the class to prepare codebook\r\n Function loads in sequence all preprocesses image files from specified in_path,\r\n and create feature vector for each image\r\n\r\n :param in_path root directory for all the cropped images prepared for learning\r\n :param out_path root directory to store codebook\r\n '''\r\n\r\n if not os.path.isdir(out_path):\r\n os.mkdir(out_path)\r\n\r\n img_paths = self._get_img_paths(in_path)\r\n iter = 0 # to test purpose\r\n print(\"Creating codebook...\")\r\n\r\n # Initial call to print 0% progress\r\n l = len(img_paths)\r\n i = 0\r\n printProgressBar(0, l, prefix='Progress:', suffix='Complete', length=50)\r\n\r\n for img in img_paths:\r\n hog_features_vect = self.Calc_descriptors(img)\r\n dir_list = img.split(os.sep)\r\n file_name = dir_list[-1] # get filename of jpeg image file\r\n file_name = os.path.splitext(file_name)[0] + \".npy\" # change extension\r\n outfile = os.path.join(out_path, file_name)\r\n numpy.save(outfile, hog_features_vect)\r\n # time.sleep(0.1)\r\n # Update Progress Bar\r\n i = i + 1\r\n printProgressBar(i, l, prefix='Progress:', suffix='Complete', length=50)\r\n print(\"Codebook created\")\r\n\r\n def _get_img_paths(self, in_path):\r\n '''\r\n get paths of all images in root directory \r\n (specific to the current dir structure!)\r\n \r\n :return img_paths list of paths of pictures to learn\r\n '''\r\n\r\n dir_list = os.listdir(in_path)\r\n img_paths = []\r\n\r\n for directory in dir_list:\r\n directory = os.path.join(in_path, directory)\r\n if not os.path.isdir(directory):\r\n continue\r\n\r\n img_list = os.listdir(directory)\r\n # get rid of all files that aren't images\r\n img_list = [img for img in img_list if\r\n any(ext in img for ext in self.ext_list)]\r\n\r\n # add directory\r\n img_list = [os.path.join(directory, img) for img in img_list]\r\n img_paths = img_paths + img_list\r\n\r\n return img_paths\r\n\r\n def _get_template_paths(self, codebook_path):\r\n '''\r\n get paths of all templates in codebook \r\n (specific to the current dir structure!)\r\n \r\n :return template_paths list of paths of templates with calculated HOGs\r\n '''\r\n\r\n dir_list = os.listdir(codebook_path)\r\n template_paths = []\r\n\r\n for directory in dir_list:\r\n directory = os.path.join(codebook_path, directory)\r\n template_paths.append(directory)\r\n\r\n return template_paths\r\n\r\n def get_img_data(self, img_path):\r\n '''\r\n get information from dtext filename,\r\n returns dictionary with image vesrsion, person number, series number, file number \r\n in directory, vertical and horizontal angle in degrees\r\n\r\n :return img_data dictionary with img data \r\n '''\r\n\r\n img_data = {\r\n 'image_version': 0,\r\n 'person_number': 0,\r\n 'series_number': 0,\r\n 'file_number': 0,\r\n 'vertical': 0,\r\n 'horizontal': 0\r\n }\r\n filename = img_path.split(os.sep)[-1]\r\n data = re.findall(\"\\d*\\d\", filename)\r\n signs = re.findall('[+ -]', filename)\r\n img_data['image_version'] = int(data[0])\r\n img_data['person_number'] = int(data[1][0:1])\r\n img_data['series_number'] = int(data[1][2])\r\n img_data['file_number'] = int(data[1][3:4])\r\n img_data['vertical'] = \\\r\n int(data[2]) if signs[0] == '+' else -int(data[2])\r\n img_data['horizontal'] = \\\r\n int(data[3]) if signs[1] == '+' else -int(data[3])\r\n\r\n return img_data\r\n\r\n def get_template_data(self, template_path):\r\n '''\r\n Get angle values from txt template filename\r\n\r\n :param template_path - path to txt file in codebook dir\r\n :return v_angle & h_angle obtained from template filename\r\n '''\r\n\r\n v_angle = 0;\r\n h_angle = 0\r\n filename = template_path.split(os.sep)[-1]\r\n data = re.findall(\"\\d*\\d\", filename)\r\n signs = re.findall('[+ -]', filename)\r\n v_angle = int(data[2]) if signs[0] == '+' else -int(data[2])\r\n h_angle = int(data[3]) if signs[1] == '+' else -int(data[3])\r\n\r\n return v_angle, h_angle\r\n\r\n def Calc_descriptors(self, img_path):\r\n '''\r\n Calculate descriptors (HOG) for an image and return feature vector\r\n\r\n :param img_path directory to image for wich calculating descriptors\r\n\r\n :return hog_features as numpy array - our feature vector for this image\r\n '''\r\n\r\n img = cv.imread(img_path)\r\n cell_size = (32, 32) # w x h in pixels\r\n block_size = (4, 4) # w x h in cells\r\n nbins = 9 # number of orientation bins\r\n hog_desc = cv.HOGDescriptor(_winSize=(img.shape[0] // cell_size[0] * cell_size[0],\r\n img.shape[1] // cell_size[1] * cell_size[1]),\r\n _blockSize=(block_size[0] * cell_size[0],\r\n block_size[1] * cell_size[1]),\r\n _blockStride=(cell_size[0], cell_size[1]),\r\n _cellSize=(cell_size[0], cell_size[1]),\r\n _nbins=nbins)\r\n hog_features = hog_desc.compute(img)\r\n print(len(hog_features))\r\n return hog_features\r\n\r\n def Estimate_angles_for_img(self, test_img_path):\r\n '''\r\n Calculate descriptors (HOG) for test image, finding best matching\r\n feature vector in Codebook and based on that information estimating angles\r\n\r\n :return verical_angle & horizontal_angle estimated for input image\r\n '''\r\n\r\n distance = 100000\r\n vertical_angle = 0\r\n horizontal_angle = 0\r\n\r\n test_img_hog = self.Calc_descriptors(test_img_path)\r\n print(\"Estimating orientation for: \" + test_img_path)\r\n\r\n # Initial call to print 0% progress\r\n l = len(self.templates_list)\r\n i = 0\r\n printProgressBar(0, l, prefix='Progress:', suffix='Complete', length=50)\r\n\r\n for template in self.templates_list:\r\n if not (test_img_hog.shape[0] == template['hog'].shape[0]):\r\n continue\r\n temp = numpy.linalg.norm((template['hog'] - test_img_hog))\r\n if (temp < distance):\r\n distance = temp\r\n vertical_angle, horizontal_angle = template['vertical'], template['horizontal']\r\n\r\n # Update Progress Bar\r\n i = i + 1\r\n printProgressBar(i, l, prefix='Progress:', suffix='Complete', length=50)\r\n\r\n return vertical_angle, horizontal_angle\r\n\r\n def Load_codebook_to_mem(self, codebook_path='Codebook'):\r\n '''\r\n Load codebook templates to memory for later estimating head orientation\r\n\r\n :param codebook_path directory to image for estimate face orientation \r\n '''\r\n template_paths = self._get_template_paths(codebook_path)\r\n\r\n print(\"Loading Codebook from files...\")\r\n # Initial call to print 0% progress\r\n l = len(template_paths)\r\n i = 0\r\n printProgressBar(0, l, prefix='Progress:', suffix='Complete', length=50)\r\n\r\n for template_path in template_paths:\r\n template = {\r\n 'hog': 0,\r\n 'vertical': 0,\r\n 'horizontal': 0\r\n }\r\n template['hog'] = numpy.load(template_path)\r\n template['vertical'], template['horizontal'] = \\\r\n self.get_template_data(template_path)\r\n self.templates_list.append(template)\r\n # Update Progress Bar\r\n i = i + 1\r\n printProgressBar(i, l, prefix='Progress:', suffix='Complete', length=50)\r\n\r\n # Print iterations progress\r\n # Code from https://stackoverflow.com/questions/3173320/text-progress-bar-in-the-console\r\n\r\n\r\ndef printProgressBar(iteration, total, prefix='', suffix='',\r\n decimals=1, length=100, fill='█', printEnd=\"\\r\"):\r\n \"\"\"\r\n Call in a loop to create terminal progress bar\r\n @params:\r\n iteration - Required : current iteration (Int)\r\n total - Required : total iterations (Int)\r\n prefix - Optional : prefix string (Str)\r\n suffix - Optional : suffix string (Str)\r\n decimals - Optional : positive number of decimals in percent complete (Int)\r\n length - Optional : character length of bar (Int)\r\n fill - Optional : bar fill character (Str)\r\n printEnd - Optional : end character (e.g. \"\\r\", \"\\r\\n\") (Str)\r\n \"\"\"\r\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\r\n filledLength = int(length * iteration // total)\r\n bar = fill * filledLength + '-' * (length - filledLength)\r\n print(f'\\r{prefix} |{bar}| {percent}% {suffix}', end=printEnd)\r\n # Print New Line on Complete\r\n if iteration == total:\r\n print()", "sub_path": "Codebook.py", "file_name": "Codebook.py", "file_ext": "py", "file_size_in_byte": 9844, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.path.isdir", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 32, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "numpy.save", "line_number": 49, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "os.sep", "line_number": 117, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 118, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 119, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 141, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 142, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 143, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 158, "usage_type": "call"}, {"api_name": "cv2.HOGDescriptor", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 196, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 227, "usage_type": "call"}]} +{"seq_id": "30770803", "text": "# Copyright 2017 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\nimport collections\nfrom enum import Enum as StdLibEnum\nfrom typing import (\n Callable,\n DefaultDict,\n Iterable,\n Mapping,\n MutableMapping,\n TypeVar,\n ValuesView,\n cast,\n)\n\n\n_K = TypeVar('_K')\n_V = TypeVar('_V')\n\n\ndef factory_dict(value_factory: Callable[[_K], _V], *args, **kwargs) -> DefaultDict:\n \"\"\"A dict whose values are computed by `value_factory` when a `__getitem__` key is missing.\n\n Note that values retrieved by any other method will not be lazily computed; eg: via `get`.\n\n :param value_factory:\n :param *args: Any positional args to pass through to `dict`.\n :param **kwrags: Any kwargs to pass through to `dict`.\n \"\"\"\n class FactoryDict(collections.defaultdict):\n @staticmethod\n def __never_called():\n raise AssertionError('The default factory should never be called since we override '\n '__missing__.')\n\n def __init__(self):\n super().__init__(self.__never_called, *args, **kwargs)\n\n def __missing__(self, key):\n value = value_factory(key)\n self[key] = value\n return value\n\n return FactoryDict()\n\n\ndef recursively_update(d: MutableMapping, d2: MutableMapping) -> None:\n \"\"\"dict.update but which merges child dicts (dict2 takes precedence where there's conflict).\"\"\"\n for k, v in d2.items():\n if k in d:\n if isinstance(v, dict):\n recursively_update(d[k], v)\n continue\n d[k] = v\n\n\n_T = TypeVar('_T')\n\n\ndef assert_single_element(iterable: Iterable[_T]) -> _T:\n \"\"\"Get the single element of `iterable`, or raise an error.\n\n :raise: :class:`StopIteration` if there is no element.\n :raise: :class:`ValueError` if there is more than one element.\n \"\"\"\n it = iter(iterable)\n first_item = next(it)\n\n try:\n next(it)\n except StopIteration:\n return first_item\n\n raise ValueError(f\"iterable {iterable!r} has more than one element.\")\n\n\n_E = TypeVar('_E', bound='Enum')\n\n\nclass EnumMatchError(ValueError):\n \"\"\"Issue when using match() on an enum.\"\"\"\n\n\nclass InexhaustiveMatchError(EnumMatchError):\n \"\"\"Not all values of the enum specified in the pattern match.\"\"\"\n\n\nclass UnrecognizedMatchError(EnumMatchError):\n \"\"\"A value is used that is not a part of the enum.\"\"\"\n\n\nclass Enum(StdLibEnum):\n\n @classmethod\n def all_values(cls) -> ValuesView['Enum']:\n return cls.__members__.values()\n\n def match(self, enum_values_to_results: Mapping[_E, _V]) -> _V:\n unrecognized_values = [\n value for value in enum_values_to_results if value not in self.all_values()\n ]\n missing_values = [\n value for value in self.all_values() if value not in enum_values_to_results\n ]\n if unrecognized_values:\n raise UnrecognizedMatchError(\n f\"Match includes values not defined in the enum. Unrecognized: {unrecognized_values}\"\n )\n if missing_values:\n raise InexhaustiveMatchError(\n f\"All enum values must be covered by the match. Missing: {missing_values}\"\n )\n typed_self = cast(_E, self)\n return enum_values_to_results[typed_self]\n", "sub_path": "src/python/pants/util/collections.py", "file_name": "collections.py", "file_ext": "py", "file_size_in_byte": 3131, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "typing.TypeVar", "line_number": 18, "usage_type": "call"}, {"api_name": "typing.TypeVar", "line_number": 19, "usage_type": "call"}, {"api_name": "typing.Callable", "line_number": 22, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 31, "usage_type": "attribute"}, {"api_name": "typing.DefaultDict", "line_number": 22, "usage_type": "name"}, {"api_name": "typing.MutableMapping", "line_number": 48, "usage_type": "name"}, {"api_name": "typing.TypeVar", "line_number": 58, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 61, "usage_type": "name"}, {"api_name": "typing.TypeVar", "line_number": 78, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 93, "usage_type": "name"}, {"api_name": "typing.ValuesView", "line_number": 96, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 99, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 114, "usage_type": "call"}]} +{"seq_id": "69903413", "text": "import os.path as osp\nimport pandas as pd\nimport pickle\nimport mmcv\nimport numpy as np\nfrom mmcv.parallel import DataContainer as DC\nfrom torch.utils.data import Dataset\n\nfrom .transforms import (ImageTransform, BboxTransform, MaskTransform,\n SegMapTransform, Numpy2Tensor)\nfrom .utils import to_tensor, random_scale\nfrom .extra_aug import ExtraAugmentation\nfrom sklearn.utils import shuffle\n#from detect.utils import get_image_size\nfrom multiprocessing import Pool\nfrom tqdm import tqdm\nimport struct\nimport imghdr\nimport cv2\nimport glob\nfrom .settings import REL_DATA_DIR as DATA_DIR, IMG_DIR, TEST_IMG_DIR, VAL_IMG_DIR\n\nclasses = [\n '/m/04yx4,/m/01226z',\n '/m/04yx4,/m/0wdt60w',\n '/m/01bl7v,/m/01226z',\n '/m/05r655,/m/01226z',\n '/m/03bt1vf,/m/01226z',\n '/m/04yx4,/m/05ctyq',\n '/m/03bt1vf,/m/05ctyq',\n '/m/05r655,/m/05ctyq',\n '/m/01226z',\n '/m/0wdt60w',\n '/m/05ctyq',\n '/m/04yx4',\n '/m/01bl7v',\n '/m/05r655',\n '/m/03bt1vf']\nstoi = { classes[i]: i for i in range(len(classes)) }\n\n#print('IMG_DIR', IMG_DIR)\n\ndef get_image_size(fname):\n '''Determine the image type of fhandle and return its size.\n from draco'''\n with open(fname, 'rb') as fhandle:\n head = fhandle.read(24)\n if len(head) != 24:\n raise AssertionError('imghead len != 24')\n if imghdr.what(fname) == 'png':\n check = struct.unpack('>i', head[4:8])[0]\n if check != 0x0d0a1a0a:\n raise AssertionError('png check failed')\n width, height = struct.unpack('>ii', head[16:24])\n elif imghdr.what(fname) == 'gif':\n width, height = struct.unpack('H', fhandle.read(2))[0] - 2\n # We are at a SOFn block\n fhandle.seek(1, 1) # Skip `precision' byte.\n height, width = struct.unpack('>HH', fhandle.read(4))\n except Exception: #IGNORE:W0703\n raise\n else:\n print(fname, imghdr.what(fname))\n #raise AssertionError('file format not supported')\n img = cv2.imread(fname)\n print(img.shape)\n height, width, _ = img.shape\n\n return width, height\n\n\ndef group2mmdetection(group: dict) -> dict:\n \"\"\"Custom dataset for detection.\n\n Annotation format:\n [\n {\n 'filename': 'a.jpg',\n 'width': 1280,\n 'height': 720,\n 'ann': {\n 'bboxes': (n, 4),\n 'labels': (n, ),\n 'bboxes_ignore': (k, 4),\n 'labels_ignore': (k, 4) (optional field)\n }\n },\n ...\n ]\n\n The `ann` field is optional for testing.\n \"\"\"\n\n image_id, group = group\n filename = group['filename'].values[0]\n fullpath = osp.join(IMG_DIR, filename)\n assert image_id == osp.basename(filename).split('.')[0]\n\n width, height = get_image_size(fullpath)\n\n group['XMin'] = group['XMin'] * width\n group['XMax'] = group['XMax'] * width\n group['YMin'] = group['YMin'] * height\n group['YMax'] = group['YMax'] * height\n\n bboxes = [np.expand_dims(group[col].values, -1) for col in['XMin', 'YMin', 'XMax', 'YMax']]\n bboxes = np.concatenate(bboxes, axis=1)\n #print(bboxes)\n #print(bboxes.shape)\n return {\n 'filename': group['filename'].values[0], #image_id+'.jpg',\n 'width': width,\n 'height': height,\n 'ann': {\n 'bboxes': np.array(bboxes, dtype=np.float32),\n 'labels': np.array([stoi[x] for x in group['LabelName'].values]) + 1\n }\n }\n\ndef get_balanced_meta():\n df_vrd = pd.read_csv(osp.join(DATA_DIR, 'challenge-2019-train-vrd.csv'))\n df_vrd['target'] = df_vrd.LabelName1.str.cat(df_vrd.LabelName2, sep=',')\n df_box = pd.read_csv(osp.join(DATA_DIR, 'challenge-2019-train-vrd-bbox.csv'))\n\n df_hits = df_vrd.loc[df_vrd.RelationshipLabel=='hits'].copy()\n df_hits = df_hits.drop(columns=['LabelName1', 'LabelName2', 'RelationshipLabel', 'XMin2', 'YMin2', 'XMax2', 'YMax2'], axis=1)\n df_hits.rename(columns={'XMin1': 'XMin', 'XMax1': 'XMax', 'YMin1': 'YMin', 'YMax1': 'YMax', 'target': 'LabelName'}, inplace=True)\n img_hits = df_hits.ImageID.unique()\n\n df_human = df_box.loc[df_box.LabelName.isin(classes[11:])]\n df_human_neg = df_human.loc[~df_human.ImageID.isin(set(img_hits))]\n img_human_neg = df_human_neg.ImageID.unique()[:1000]\n df_football = df_box.loc[df_box.LabelName=='/m/01226z']\n img_football = df_football.ImageID.unique()[:1500]\n img_otherball = df_box.loc[df_box.LabelName.isin(['/m/0wdt60w', '/m/05ctyq'])].ImageID.unique()\n\n selected_imgs = set(img_human_neg) | set(img_football) | set(img_otherball)\n df_box_hits = df_box.loc[df_box.ImageID.isin(selected_imgs)]\n df_box_hits = df_box_hits.loc[df_box_hits.LabelName.isin(classes[8:])].copy()\n\n\n df_box_hits = df_box_hits.drop(columns=['IsGroupOf'], axis=1)\n meta = pd.concat([df_hits, df_box_hits], sort=False)\n\n #df_human_neg.shape\n\n img_files = glob.glob(IMG_DIR + '/**/*.jpg')\n fullpath_dict = {}\n for fn in img_files:\n fullpath_dict[osp.basename(fn).split('.')[0]] = osp.join(osp.basename(osp.dirname(fn)), osp.basename(fn))\n \n meta['filename'] = meta.ImageID.map(lambda x: fullpath_dict[x])\n\n return meta\n\ndef get_val_meta():\n df_box = pd.read_csv(osp.join(DATA_DIR, 'challenge-2019-validation-vrd-bbox.csv'))\n df_box['filename'] = df_box.ImageID.map(lambda x: x+'.jpg')\n\ndef id2mmdetection(img_id):\n fn = osp.join(TEST_IMG_DIR, '{}.jpg'.format(img_id))\n width, height = get_image_size(fn)\n return {\n 'filename': img_id+'.jpg',\n 'width': width,\n 'height': height,\n }\n\ndef get_test_ds():\n print('reading VRD_sample_submission.csv...')\n df = pd.read_csv(osp.join(DATA_DIR, 'VRD_sample_submission.csv'))\n print('creating mmdet test data...')\n #with Pool(50) as p:\n # img_ids = df.ImageId.values\n # annos = list(tqdm(iterable=p.map(id2mmdetection, img_ids), total=len(img_ids)))\n annos = []\n for img_id in tqdm(df.ImageId.values):\n annos.append(id2mmdetection(img_id))\n print(annos[0])\n print('DATASET LEN:', len(annos))\n return annos\n\ndef id2mmdetection_val(img_id):\n fn = osp.join(VAL_IMG_DIR, '{}.jpg'.format(img_id))\n width, height = get_image_size(fn)\n return {\n 'filename': img_id+'.jpg',\n 'width': width,\n 'height': height,\n }\n\ndef get_val_ds():\n df = pd.read_csv(osp.join(DATA_DIR, 'val_imgs.csv'))\n with Pool(50) as p:\n img_ids = df.ImageId.values\n annos = list(tqdm(iterable=p.map(id2mmdetection_val, img_ids), total=len(img_ids)))\n print(annos[0])\n print('DATASET LEN:', len(annos))\n return annos\n\nclass HitsDetectCustomDataset(Dataset):\n \"\"\"Custom dataset for detection.\n\n Annotation format:\n [\n {\n 'filename': 'a.jpg',\n 'width': 1280,\n 'height': 720,\n 'ann': {\n 'bboxes': (n, 4),\n 'labels': (n, ),\n 'bboxes_ignore': (k, 4),\n 'labels_ignore': (k, 4) (optional field)\n }\n },\n ...\n ]\n\n The `ann` field is optional for testing.\n \"\"\"\n\n CLASSES = None\n\n def __init__(self,\n ann_file,\n img_prefix,\n img_scale,\n img_norm_cfg,\n multiscale_mode='value',\n size_divisor=None,\n proposal_file=None,\n num_max_proposals=1000,\n flip_ratio=0,\n with_mask=True,\n with_crowd=True,\n with_label=True,\n with_semantic_seg=False,\n seg_prefix=None,\n seg_scale_factor=1,\n extra_aug=None,\n resize_keep_ratio=True,\n test_mode=False):\n # prefix of images path\n self.img_prefix = img_prefix\n\n # load annotations (and proposals)\n self.img_infos = self.load_annotations(ann_file)\n if proposal_file is not None:\n self.proposals = self.load_proposals(proposal_file)\n else:\n self.proposals = None\n # filter images with no annotation during training\n if not test_mode:\n valid_inds = self._filter_imgs()\n self.img_infos = [self.img_infos[i] for i in valid_inds]\n if self.proposals is not None:\n self.proposals = [self.proposals[i] for i in valid_inds]\n\n # (long_edge, short_edge) or [(long1, short1), (long2, short2), ...]\n self.img_scales = img_scale if isinstance(img_scale,\n list) else [img_scale]\n assert mmcv.is_list_of(self.img_scales, tuple)\n # normalization configs\n self.img_norm_cfg = img_norm_cfg\n\n # multi-scale mode (only applicable for multi-scale training)\n self.multiscale_mode = multiscale_mode\n assert multiscale_mode in ['value', 'range']\n\n # max proposals per image\n self.num_max_proposals = num_max_proposals\n # flip ratio\n self.flip_ratio = flip_ratio\n assert flip_ratio >= 0 and flip_ratio <= 1\n # padding border to ensure the image size can be divided by\n # size_divisor (used for FPN)\n self.size_divisor = size_divisor\n\n # with mask or not (reserved field, takes no effect)\n self.with_mask = with_mask\n # some datasets provide bbox annotations as ignore/crowd/difficult,\n # if `with_crowd` is True, then these info is returned.\n self.with_crowd = with_crowd\n # with label is False for RPN\n self.with_label = with_label\n # with semantic segmentation (stuff) annotation or not\n self.with_seg = with_semantic_seg\n # prefix of semantic segmentation map path\n self.seg_prefix = seg_prefix\n # rescale factor for segmentation maps\n self.seg_scale_factor = seg_scale_factor\n # in test mode or not\n self.test_mode = test_mode\n\n # set group flag for the sampler\n if not self.test_mode:\n self._set_group_flag()\n # transforms\n self.img_transform = ImageTransform(\n size_divisor=self.size_divisor, **self.img_norm_cfg)\n self.bbox_transform = BboxTransform()\n self.mask_transform = MaskTransform()\n self.seg_transform = SegMapTransform(self.size_divisor)\n self.numpy2tensor = Numpy2Tensor()\n\n # if use extra augmentation\n if extra_aug is not None:\n self.extra_aug = ExtraAugmentation(**extra_aug)\n else:\n self.extra_aug = None\n\n # image rescale if keep ratio\n self.resize_keep_ratio = resize_keep_ratio\n\n def __len__(self):\n return len(self.img_infos)\n\n def load_train_annotations(self):\n meta = get_balanced_meta()\n print('grouping...')\n groups = list(meta.groupby('ImageID'))\n\n with Pool(50) as p:\n annos = list(tqdm(iterable=p.imap_unordered(group2mmdetection, groups), total=len(groups)))\n\n print('DATASET LEN:', len(annos))\n \n return shuffle(annos)\n\n def load_annotations(self, ann_file):\n if 'train' in ann_file:\n return self.load_train_annotations()\n elif 'test' in ann_file:\n return get_test_ds()\n elif 'val' in ann_file:\n return get_val_ds()\n\n def load_proposals(self, proposal_file):\n return mmcv.load(proposal_file)\n\n def get_ann_info(self, idx):\n return self.img_infos[idx]['ann']\n\n def _filter_imgs(self, min_size=32):\n \"\"\"Filter images too small.\"\"\"\n valid_inds = []\n for i, img_info in enumerate(self.img_infos):\n if min(img_info['width'], img_info['height']) >= min_size:\n valid_inds.append(i)\n return valid_inds\n\n def _set_group_flag(self):\n \"\"\"Set flag according to image aspect ratio.\n\n Images with aspect ratio greater than 1 will be set as group 1,\n otherwise group 0.\n \"\"\"\n self.flag = np.zeros(len(self), dtype=np.uint8)\n for i in range(len(self)):\n img_info = self.img_infos[i]\n if img_info['width'] / img_info['height'] > 1:\n self.flag[i] = 1\n\n def _rand_another(self, idx):\n pool = np.where(self.flag == self.flag[idx])[0]\n return np.random.choice(pool)\n\n def __getitem__(self, idx):\n if self.test_mode:\n return self.prepare_test_img(idx)\n while True:\n data = self.prepare_train_img(idx)\n if data is None:\n idx = self._rand_another(idx)\n continue\n return data\n\n def prepare_train_img(self, idx):\n img_info = self.img_infos[idx]\n # load image\n img = mmcv.imread(osp.join(self.img_prefix, img_info['filename']))\n # load proposals if necessary\n if self.proposals is not None:\n proposals = self.proposals[idx][:self.num_max_proposals]\n # TODO: Handle empty proposals properly. Currently images with\n # no proposals are just ignored, but they can be used for\n # training in concept.\n if len(proposals) == 0:\n return None\n if not (proposals.shape[1] == 4 or proposals.shape[1] == 5):\n raise AssertionError(\n 'proposals should have shapes (n, 4) or (n, 5), '\n 'but found {}'.format(proposals.shape))\n if proposals.shape[1] == 5:\n scores = proposals[:, 4, None]\n proposals = proposals[:, :4]\n else:\n scores = None\n\n ann = self.get_ann_info(idx)\n gt_bboxes = ann['bboxes']\n gt_labels = ann['labels']\n if self.with_crowd:\n gt_bboxes_ignore = ann['bboxes_ignore']\n\n # skip the image if there is no valid gt bbox\n if len(gt_bboxes) == 0:\n return None\n\n # extra augmentation\n if self.extra_aug is not None:\n img, gt_bboxes, gt_labels = self.extra_aug(img, gt_bboxes,\n gt_labels)\n\n # apply transforms\n flip = True if np.random.rand() < self.flip_ratio else False\n # randomly sample a scale\n img_scale = random_scale(self.img_scales, self.multiscale_mode)\n img, img_shape, pad_shape, scale_factor = self.img_transform(\n img, img_scale, flip, keep_ratio=self.resize_keep_ratio)\n img = img.copy()\n if self.with_seg:\n gt_seg = mmcv.imread(\n osp.join(self.seg_prefix, img_info['file_name'].replace(\n 'jpg', 'png')),\n flag='unchanged')\n gt_seg = self.seg_transform(gt_seg.squeeze(), img_scale, flip)\n gt_seg = mmcv.imrescale(\n gt_seg, self.seg_scale_factor, interpolation='nearest')\n gt_seg = gt_seg[None, ...]\n if self.proposals is not None:\n proposals = self.bbox_transform(proposals, img_shape, scale_factor,\n flip)\n proposals = np.hstack(\n [proposals, scores]) if scores is not None else proposals\n gt_bboxes = self.bbox_transform(gt_bboxes, img_shape, scale_factor,\n flip)\n if self.with_crowd:\n gt_bboxes_ignore = self.bbox_transform(gt_bboxes_ignore, img_shape,\n scale_factor, flip)\n if self.with_mask:\n gt_masks = self.mask_transform(ann['masks'], pad_shape,\n scale_factor, flip)\n\n ori_shape = (img_info['height'], img_info['width'], 3)\n img_meta = dict(\n ori_shape=ori_shape,\n img_shape=img_shape,\n pad_shape=pad_shape,\n scale_factor=scale_factor,\n flip=flip)\n\n data = dict(\n img=DC(to_tensor(img), stack=True),\n img_meta=DC(img_meta, cpu_only=True),\n gt_bboxes=DC(to_tensor(gt_bboxes)))\n if self.proposals is not None:\n data['proposals'] = DC(to_tensor(proposals))\n if self.with_label:\n data['gt_labels'] = DC(to_tensor(gt_labels))\n if self.with_crowd:\n data['gt_bboxes_ignore'] = DC(to_tensor(gt_bboxes_ignore))\n if self.with_mask:\n data['gt_masks'] = DC(gt_masks, cpu_only=True)\n if self.with_seg:\n data['gt_semantic_seg'] = DC(to_tensor(gt_seg), stack=True)\n return data\n\n def prepare_test_img(self, idx):\n \"\"\"Prepare an image for testing (multi-scale and flipping)\"\"\"\n img_info = self.img_infos[idx]\n img = mmcv.imread(osp.join(self.img_prefix, img_info['filename']))\n if self.proposals is not None:\n proposal = self.proposals[idx][:self.num_max_proposals]\n if not (proposal.shape[1] == 4 or proposal.shape[1] == 5):\n raise AssertionError(\n 'proposals should have shapes (n, 4) or (n, 5), '\n 'but found {}'.format(proposal.shape))\n else:\n proposal = None\n\n def prepare_single(img, scale, flip, proposal=None):\n _img, img_shape, pad_shape, scale_factor = self.img_transform(\n img, scale, flip, keep_ratio=self.resize_keep_ratio)\n _img = to_tensor(_img)\n _img_meta = dict(\n ori_shape=(img_info['height'], img_info['width'], 3),\n img_shape=img_shape,\n pad_shape=pad_shape,\n scale_factor=scale_factor,\n flip=flip)\n if proposal is not None:\n if proposal.shape[1] == 5:\n score = proposal[:, 4, None]\n proposal = proposal[:, :4]\n else:\n score = None\n _proposal = self.bbox_transform(proposal, img_shape,\n scale_factor, flip)\n _proposal = np.hstack(\n [_proposal, score]) if score is not None else _proposal\n _proposal = to_tensor(_proposal)\n else:\n _proposal = None\n return _img, _img_meta, _proposal\n\n imgs = []\n img_metas = []\n proposals = []\n for scale in self.img_scales:\n _img, _img_meta, _proposal = prepare_single(\n img, scale, False, proposal)\n imgs.append(_img)\n img_metas.append(DC(_img_meta, cpu_only=True))\n proposals.append(_proposal)\n if self.flip_ratio > 0:\n _img, _img_meta, _proposal = prepare_single(\n img, scale, True, proposal)\n imgs.append(_img)\n img_metas.append(DC(_img_meta, cpu_only=True))\n proposals.append(_proposal)\n data = dict(img=imgs, img_meta=img_metas)\n if self.proposals is not None:\n data['proposals'] = proposals\n return data\n", "sub_path": "mmdet/datasets/hits_detect_custom.py", "file_name": "hits_detect_custom.py", "file_ext": "py", "file_size_in_byte": 19770, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "imghdr.what", "line_number": 50, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 51, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 54, "usage_type": "call"}, {"api_name": "imghdr.what", "line_number": 55, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 56, "usage_type": "call"}, {"api_name": "imghdr.what", "line_number": 57, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 68, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 71, "usage_type": "call"}, {"api_name": "imghdr.what", "line_number": 75, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 108, "usage_type": "call"}, {"api_name": "settings.IMG_DIR", "line_number": 108, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 108, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path", "line_number": 109, "usage_type": "name"}, {"api_name": "numpy.expand_dims", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 127, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 128, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 133, "usage_type": "call"}, {"api_name": "settings.REL_DATA_DIR", "line_number": 133, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 133, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 135, "usage_type": "call"}, {"api_name": "settings.REL_DATA_DIR", "line_number": 135, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 135, "usage_type": "name"}, {"api_name": "pandas.concat", "line_number": 155, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 159, "usage_type": "call"}, {"api_name": "settings.IMG_DIR", "line_number": 159, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 162, "usage_type": "call"}, {"api_name": "os.path", "line_number": 162, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 162, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 162, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 169, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 169, "usage_type": "call"}, {"api_name": "settings.REL_DATA_DIR", "line_number": 169, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 169, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 173, "usage_type": "call"}, {"api_name": "settings.TEST_IMG_DIR", "line_number": 173, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 173, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 183, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 183, "usage_type": "call"}, {"api_name": "settings.REL_DATA_DIR", "line_number": 183, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 183, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 189, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 196, "usage_type": "call"}, {"api_name": "settings.VAL_IMG_DIR", "line_number": 196, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 196, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 205, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 205, "usage_type": "call"}, {"api_name": "settings.REL_DATA_DIR", "line_number": 205, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 205, "usage_type": "name"}, {"api_name": "multiprocessing.Pool", "line_number": 206, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 208, "usage_type": "call"}, {"api_name": "torch.utils.data.Dataset", "line_number": 213, "usage_type": "name"}, {"api_name": "mmcv.is_list_of", "line_number": 275, "usage_type": "call"}, {"api_name": "transforms.ImageTransform", "line_number": 312, "usage_type": "call"}, {"api_name": "transforms.BboxTransform", "line_number": 314, "usage_type": "call"}, {"api_name": "transforms.MaskTransform", "line_number": 315, "usage_type": "call"}, {"api_name": "transforms.SegMapTransform", "line_number": 316, "usage_type": "call"}, {"api_name": "transforms.Numpy2Tensor", "line_number": 317, "usage_type": "call"}, {"api_name": "extra_aug.ExtraAugmentation", "line_number": 321, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 336, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 337, "usage_type": "call"}, {"api_name": "sklearn.utils.shuffle", "line_number": 341, "usage_type": "call"}, {"api_name": "mmcv.load", "line_number": 352, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 371, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 371, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 378, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 379, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 379, "usage_type": "attribute"}, {"api_name": "mmcv.imread", "line_number": 394, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 394, "usage_type": "call"}, {"api_name": "os.path", "line_number": 394, "usage_type": "name"}, {"api_name": "numpy.random.rand", "line_number": 429, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 429, "usage_type": "attribute"}, {"api_name": "utils.random_scale", "line_number": 431, "usage_type": "call"}, {"api_name": "mmcv.imread", "line_number": 436, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 437, "usage_type": "call"}, {"api_name": "os.path", "line_number": 437, "usage_type": "name"}, {"api_name": "mmcv.imrescale", "line_number": 441, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 447, "usage_type": "call"}, {"api_name": "mmcv.parallel.DataContainer", "line_number": 467, "usage_type": "call"}, {"api_name": "utils.to_tensor", "line_number": 467, "usage_type": "call"}, {"api_name": "mmcv.parallel.DataContainer", "line_number": 468, "usage_type": "call"}, {"api_name": "mmcv.parallel.DataContainer", "line_number": 469, "usage_type": "call"}, {"api_name": "utils.to_tensor", "line_number": 469, "usage_type": "call"}, {"api_name": "mmcv.parallel.DataContainer", "line_number": 471, "usage_type": "call"}, {"api_name": "utils.to_tensor", "line_number": 471, "usage_type": "call"}, {"api_name": "mmcv.parallel.DataContainer", "line_number": 473, "usage_type": "call"}, {"api_name": "utils.to_tensor", "line_number": 473, "usage_type": "call"}, {"api_name": "mmcv.parallel.DataContainer", "line_number": 475, "usage_type": "call"}, {"api_name": "utils.to_tensor", "line_number": 475, "usage_type": "call"}, {"api_name": "mmcv.parallel.DataContainer", "line_number": 477, "usage_type": "call"}, {"api_name": "mmcv.parallel.DataContainer", "line_number": 479, "usage_type": "call"}, {"api_name": "utils.to_tensor", "line_number": 479, "usage_type": "call"}, {"api_name": "mmcv.imread", "line_number": 485, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 485, "usage_type": "call"}, {"api_name": "os.path", "line_number": 485, "usage_type": "name"}, {"api_name": "utils.to_tensor", "line_number": 498, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 513, "usage_type": "call"}, {"api_name": "utils.to_tensor", "line_number": 515, "usage_type": "call"}, {"api_name": "mmcv.parallel.DataContainer", "line_number": 527, "usage_type": "call"}, {"api_name": "mmcv.parallel.DataContainer", "line_number": 533, "usage_type": "call"}]} +{"seq_id": "83057846", "text": "import json\nimport imghdr\nfrom pathlib import Path\nfrom my_spotify_data import __version__, download\n\ntest_path = Path(__file__).parents[1] / \"tests\"\n\nwith open(test_path / \"example-response.json\", \"r\") as f:\n response = json.load(f)\n\n\ndef test_version():\n assert __version__ == \"0.1.0\"\n\n\ndef test_search_response_basic():\n assert \"tracks\" in response\n\n\ndef test_download_jpeg(tmp_path):\n image_url = \"https://i.scdn.co/image/ab67616d0000b273bc97aa53df9447f9dc1b4dcb\"\n file_path = tmp_path / image_url.split(\"/\")[-1]\n new_file_path = download.get_image(image_url, file_path)\n assert new_file_path\n assert imghdr.what(new_file_path) == \"jpeg\"\n\n\ndef test_get_track_ids():\n id_list = download.get_track_ids(response)\n assert len(id_list) > 0\n\n\ndef test_get_track_features():\n feats = download.get_features(\"6b2oQwSGFkzsMtQruIWm2p\")\n assert feats\n assert isinstance(feats, dict)\n empty_feats = download.get_features(\"madeup\")\n assert empty_feats is None\n\n\ndef test_get_album():\n album = download.get_album(response)\n data = next(album)\n assert data[\"name\"] == \"Pablo Honey\"\n", "sub_path": "tests/test_my_spotify_data.py", "file_name": "test_my_spotify_data.py", "file_ext": "py", "file_size_in_byte": 1124, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "pathlib.Path", "line_number": 6, "usage_type": "call"}, {"api_name": "json.load", "line_number": 9, "usage_type": "call"}, {"api_name": "my_spotify_data.__version__", "line_number": 13, "usage_type": "name"}, {"api_name": "my_spotify_data.download.get_image", "line_number": 23, "usage_type": "call"}, {"api_name": "my_spotify_data.download", "line_number": 23, "usage_type": "name"}, {"api_name": "imghdr.what", "line_number": 25, "usage_type": "call"}, {"api_name": "my_spotify_data.download.get_track_ids", "line_number": 29, "usage_type": "call"}, {"api_name": "my_spotify_data.download", "line_number": 29, "usage_type": "name"}, {"api_name": "my_spotify_data.download.get_features", "line_number": 34, "usage_type": "call"}, {"api_name": "my_spotify_data.download", "line_number": 34, "usage_type": "name"}, {"api_name": "my_spotify_data.download.get_features", "line_number": 37, "usage_type": "call"}, {"api_name": "my_spotify_data.download", "line_number": 37, "usage_type": "name"}, {"api_name": "my_spotify_data.download.get_album", "line_number": 42, "usage_type": "call"}, {"api_name": "my_spotify_data.download", "line_number": 42, "usage_type": "name"}]} +{"seq_id": "301242232", "text": "\"\"\"\r\nCreated on Mon Apr 29 05:21:13 2019\r\n\"\"\"\r\nimport numpy as np\r\nfrom dipy.viz import window, actor\r\nfrom dipy.tracking.streamline import transform_streamlines\r\nimport vtk.util.colors as colors\r\nfrom dipy.tracking import utils\r\nfrom dipy.tracking.streamline import set_number_of_points\r\nfrom sklearn import svm\r\nimport nibabel as nib\r\nfrom dipy.tracking.vox2track import streamline_mapping\r\nimport time\r\n\r\n \r\ndef show_tract(segmented_tract, color_positive ,segmented_tract_negative, color_negative, out_path):\r\n \"\"\"Visualization of the segmented tract.\r\n \"\"\" \r\n affine=utils.affine_for_trackvis(voxel_size=np.array([1.25,1.25,1.25]))\r\n bundle_native = transform_streamlines(segmented_tract, np.linalg.inv(affine))\r\n \r\n bundle_nativeNeg = transform_streamlines(segmented_tract_negative, np.linalg.inv(affine))\r\n\r\n renderer = window.Renderer()\r\n stream_actor2 = actor.line(bundle_native,\r\n colors=color_positive, linewidth=0.1)\r\n \r\n stream_actorNeg = actor.line(bundle_nativeNeg, colors=color_negative,\r\n opacity=0.01, linewidth=0.1)\r\n renderer.set_camera(position=(408.85, -26.23, 92.12),\r\n focal_point=(0.42, -14.03, 0.82),\r\n view_up=(-0.09, 0.85, 0.51))\r\n \r\n bar = actor.scalar_bar()\r\n renderer.add(stream_actor2)\r\n \r\n renderer.add(stream_actorNeg)\r\n renderer.add(bar)\r\n window.show(renderer, size=(1920, 1039), reset_camera=False)\r\n renderer.camera_info()\r\n \r\n \"\"\"Take a snapshot of the window and save it\r\n \"\"\"\r\n window.record(renderer, out_path = out_path, size=(1920, 1039))\r\n\r\ndef compute_dsc(estimated_tract, true_tract):\r\n \"\"\"Compute the overlap between the segmented tract and ground truth tract\r\n \"\"\"\r\n aff=np.array([[-1.25, 0, 0, 90],[0, 1.25, 0, -126],[0, 0, 1.25, -72],[0, 0, 0, 1]])\r\n #aff=utils.affine_for_trackvis(voxel_size=np.array([1.25,1.25,1.25]))\r\n voxel_list_estimated_tract = streamline_mapping(estimated_tract, affine=aff).keys()\r\n voxel_list_true_tract = streamline_mapping(true_tract, affine=aff).keys()\r\n TP = len(set(voxel_list_estimated_tract).intersection(set(voxel_list_true_tract)))\r\n vol_A = len(set(voxel_list_estimated_tract))\r\n vol_B = len(set(voxel_list_true_tract))\r\n DSC = 2.0 * float(TP) / float(vol_A + vol_B)\r\n return DSC \r\n \r\ndef load(filename):\r\n \"\"\"Load tractogram from TRK file \r\n \"\"\"\r\n wholeTract= nib.streamlines.load(filename) \r\n wholeTract = wholeTract.streamlines\r\n return wholeTract \r\ndef embedding(streamlines, no_of_points):\r\n \"\"\"Resample streamlines using 12 points and also flatten the streamlines\r\n \"\"\"\r\n return np.array([set_number_of_points(s, no_of_points).ravel() for s in streamlines]) \r\n \r\ndef create_train_data_set(train_subjectList,tract):\r\n \r\n train_data=[]\r\n for sub in train_subjectList: \r\n print (sub) \r\n T_filename=sub+tract\r\n wholeTract = load (T_filename) \r\n train_data=np.concatenate((train_data, wholeTract),axis=0) \r\n \r\n print (\"train data Shape\") \r\n resample_tract=embedding(train_data,no_of_points=no_of_points)\r\n \r\n return resample_tract, train_data\r\n \r\ndef create_test_data_set(testTarget_brain): \r\n \r\n print (\"Preparing Test Data\") \r\n t_filename=testTarget_brain #\"124422_af.left.trk\" \r\n test_data=load(t_filename) \r\n resample_tractogram=embedding(test_data,no_of_points=no_of_points)\r\n\r\n return resample_tractogram, test_data \r\n \r\nif __name__ == '__main__':\r\n \r\n train_subjectList =[ \"124422\", \"111312\", \"100408\", \"100307\", \"856766\"]\r\n tract = \"_cg.right.trk\"\r\n no_of_points=12 \r\n leafsize=10\r\n \r\n ################################ Train Data ######################################\r\n print (\"Preparing Train Data\")\r\n resample_tract_train, train_data= create_train_data_set(train_subjectList, tract) \r\n \r\n ###################### Test Data################################\r\n testTarget = \"161731\"\r\n testTarget_brain = \"full1M_\"+testTarget+\".trk\"\r\n t0=time.time()\r\n resample_tract_test, test_data= create_test_data_set(testTarget_brain)\r\n trueTract=load(testTarget + tract) \r\n t1=t0-time.time()\r\n \"\"\"########################### one class SVM Linear ######################\"\"\"\r\n gamma_value = 0.001\r\n clf = svm.OneClassSVM(nu=0.1, kernel=\"linear\", gamma=gamma_value)\r\n clf.fit(resample_tract_train)\r\n \"\"\" linear poly rbf \"\"\"\r\n \"\"\"#########################################\"\"\"\r\n \r\n x_pred_train = clf.predict(resample_tract_train.tolist())\r\n n_error_test = x_pred_train[x_pred_train==-1].size\r\n print('number of error for training =', n_error_test) \r\n \r\n \r\n x_pred_test=clf.predict(resample_tract_test.tolist()) \r\n n_error_test = x_pred_test[x_pred_test==-1].size\r\n print('number of error for testing=',n_error_test)\r\n \r\n \r\n ########################### visualize tract ######################\r\n test_data=np.array(test_data)\r\n segmented_tract_positive= test_data[np.where(x_pred_test==1)]\r\n segmented_tract_negative= test_data[np.where(x_pred_test==-1)]\r\n dsc=compute_dsc(segmented_tract_positive,trueTract)\r\n print(\"Accuracy for linear: \",dsc)\r\n \r\n \"\"\"########################### one class SVM Poly ######################\"\"\"\r\n gamma_value = 0.001\r\n clf = svm.OneClassSVM(nu=0.1, kernel=\"poly\", gamma=gamma_value)\r\n clf.fit(resample_tract_train)\r\n \"\"\" linear poly rbf \"\"\"\r\n \"\"\"#########################################\"\"\"\r\n \r\n x_pred_train = clf.predict(resample_tract_train.tolist())\r\n n_error_test = x_pred_train[x_pred_train==-1].size\r\n print('number of error for training =', n_error_test) \r\n \r\n \r\n x_pred_test=clf.predict(resample_tract_test.tolist()) \r\n n_error_test = x_pred_test[x_pred_test==-1].size\r\n print('number of error for testing=',n_error_test)\r\n \r\n \r\n ########################### visualize tract ######################\r\n test_data=np.array(test_data)\r\n segmented_tract_positive= test_data[np.where(x_pred_test==1)]\r\n segmented_tract_negative= test_data[np.where(x_pred_test==-1)]\r\n dsc=compute_dsc(segmented_tract_positive,trueTract)\r\n print(\"Accuracy for poly: \",dsc)\r\n \r\n \"\"\"########################### one class SVM RBF######################\"\"\"\r\n t2=time.time()\r\n gamma_value = 0.001\r\n clf = svm.OneClassSVM(nu=0.1, kernel=\"rbf\", gamma=gamma_value)\r\n clf.fit(resample_tract_train)\r\n \r\n x_pred_train = clf.predict(resample_tract_train.tolist())\r\n n_error_test = x_pred_train[x_pred_train==-1].size\r\n print('number of error for training =', n_error_test) \r\n \r\n \r\n x_pred_test=clf.predict(resample_tract_test.tolist()) \r\n n_error_test = x_pred_test[x_pred_test==-1].size\r\n print('number of error for testing=',n_error_test)\r\n \r\n \r\n ########################### visualize tract ######################\r\n test_data=np.array(test_data)\r\n segmented_tract_positive= test_data[np.where(x_pred_test==1)]\r\n segmented_tract_negative= test_data[np.where(x_pred_test==-1)]\r\n ###########################Calculating Dice Similarity Co-efficient########################### \r\n \r\n dsc=compute_dsc(segmented_tract_positive,trueTract)\r\n print(\"Accuracy for rbf: \",dsc)\r\n \r\n print(\"Total amount of time to compute svm is %f seconds\" % ((time.time()-t2)+t1)) \r\n \r\n print(\"Show the tract\")\r\n \r\n out_path=\"images\\\\svm\\\\\"+str(len(train_subjectList) )+\"_sub_\"+testTarget+\"_\"+tract+\"_SVMResult.png\" # Save image in this path\r\n color_positive= colors.green\r\n color_negative=colors.red\r\n show_tract(segmented_tract_positive, color_positive, segmented_tract_negative, color_negative, out_path)#,color_negative)#,segmented_tract_negative) \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": "One_Class_SVM.py", "file_name": "One_Class_SVM.py", "file_ext": "py", "file_size_in_byte": 7947, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "dipy.tracking.utils.affine_for_trackvis", "line_number": 19, "usage_type": "call"}, {"api_name": "dipy.tracking.utils", "line_number": 19, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "dipy.tracking.streamline.transform_streamlines", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 20, "usage_type": "attribute"}, {"api_name": "dipy.tracking.streamline.transform_streamlines", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 22, "usage_type": "attribute"}, {"api_name": "dipy.viz.window.Renderer", "line_number": 24, "usage_type": "call"}, {"api_name": "dipy.viz.window", "line_number": 24, "usage_type": "name"}, {"api_name": "dipy.viz.actor.line", "line_number": 25, "usage_type": "call"}, {"api_name": "dipy.viz.actor", "line_number": 25, "usage_type": "name"}, {"api_name": "dipy.viz.actor.line", "line_number": 28, "usage_type": "call"}, {"api_name": "dipy.viz.actor", "line_number": 28, "usage_type": "name"}, {"api_name": "dipy.viz.actor.scalar_bar", "line_number": 34, "usage_type": "call"}, {"api_name": "dipy.viz.actor", "line_number": 34, "usage_type": "name"}, {"api_name": "dipy.viz.window.show", "line_number": 39, "usage_type": "call"}, {"api_name": "dipy.viz.window", "line_number": 39, "usage_type": "name"}, {"api_name": "dipy.viz.window.record", "line_number": 44, "usage_type": "call"}, {"api_name": "dipy.viz.window", "line_number": 44, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 49, "usage_type": "call"}, {"api_name": "dipy.tracking.vox2track.streamline_mapping", "line_number": 51, "usage_type": "call"}, {"api_name": "dipy.tracking.vox2track.streamline_mapping", "line_number": 52, "usage_type": "call"}, {"api_name": "nibabel.streamlines.load", "line_number": 62, "usage_type": "call"}, {"api_name": "nibabel.streamlines", "line_number": 62, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 68, "usage_type": "call"}, {"api_name": "dipy.tracking.streamline.set_number_of_points", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 77, "usage_type": "call"}, {"api_name": "time.time", "line_number": 107, "usage_type": "call"}, {"api_name": "time.time", "line_number": 110, "usage_type": "call"}, {"api_name": "sklearn.svm.OneClassSVM", "line_number": 113, "usage_type": "call"}, {"api_name": "sklearn.svm", "line_number": 113, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 131, "usage_type": "call"}, {"api_name": "sklearn.svm.OneClassSVM", "line_number": 137, "usage_type": "call"}, {"api_name": "sklearn.svm", "line_number": 137, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 155, "usage_type": "call"}, {"api_name": "time.time", "line_number": 160, "usage_type": "call"}, {"api_name": "sklearn.svm.OneClassSVM", "line_number": 162, "usage_type": "call"}, {"api_name": "sklearn.svm", "line_number": 162, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 178, "usage_type": "call"}, {"api_name": "time.time", "line_number": 184, "usage_type": "call"}, {"api_name": "vtk.util.colors.green", "line_number": 189, "usage_type": "attribute"}, {"api_name": "vtk.util.colors", "line_number": 189, "usage_type": "name"}, {"api_name": "vtk.util.colors.red", "line_number": 190, "usage_type": "attribute"}, {"api_name": "vtk.util.colors", "line_number": 190, "usage_type": "name"}]} +{"seq_id": "572605016", "text": "# !/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nDefines unit tests for :mod:`colour.appearance.hunt` module.\n\"\"\"\n\nfrom __future__ import division, unicode_literals\n\nimport numpy as np\n\nfrom colour.appearance import Hunt_InductionFactors, XYZ_to_Hunt\nfrom colour.appearance.tests.common import ColourAppearanceModelTest\n\n__author__ = 'Colour Developers'\n__copyright__ = 'Copyright (C) 2013 - 2015 - Colour Developers'\n__license__ = 'New BSD License - http://opensource.org/licenses/BSD-3-Clause'\n__maintainer__ = 'Colour Developers'\n__email__ = 'colour-science@googlegroups.com'\n__status__ = 'Production'\n\n__all__ = ['TestHuntColourAppearanceModel']\n\n\nclass TestHuntColourAppearanceModel(ColourAppearanceModelTest):\n \"\"\"\n Defines :mod:`colour.appearance.hunt` module unit tests methods for\n Hunt colour appearance model.\n \"\"\"\n\n FIXTURE_BASENAME = 'hunt.csv'\n\n OUTPUT_ATTRIBUTES = {'J': 'J',\n 'C_94': 'C',\n 'h_S': 'h',\n 's': 's',\n 'Q': 'Q',\n 'M94': 'M'}\n\n def output_specification_from_data(self, data):\n \"\"\"\n Returns the Hunt colour appearance model output specification\n from given data.\n\n Parameters\n ----------\n data : list\n Fixture data.\n\n Returns\n -------\n Hunt_Specification\n Hunt colour appearance model specification.\n \"\"\"\n\n XYZ = np.array([data['X'], data['Y'], data['Z']])\n XYZ_w = np.array([data['X_w'], data['Y_w'], data['Z_w']])\n XYZ_b = np.array([data['X_w'], 0.2 * data['Y_w'], data['Z_w']])\n\n specification = XYZ_to_Hunt(XYZ,\n XYZ_w,\n XYZ_b,\n data['L_A'],\n Hunt_InductionFactors(\n data['N_c'],\n data['N_b']),\n CCT_w=data['T'])\n\n return specification\n", "sub_path": "colour/appearance/tests/test_hunt.py", "file_name": "test_hunt.py", "file_ext": "py", "file_size_in_byte": 2090, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "colour.appearance.tests.common.ColourAppearanceModelTest", "line_number": 25, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 58, "usage_type": "call"}, {"api_name": "colour.appearance.XYZ_to_Hunt", "line_number": 60, "usage_type": "call"}, {"api_name": "colour.appearance.Hunt_InductionFactors", "line_number": 64, "usage_type": "call"}]} +{"seq_id": "288924087", "text": "import requests\r\nimport json\r\nfrom datetime import date\r\nimport urllib.request\r\nimport random\r\n\r\ndef currencyroulettegame(diffuculty):\r\n \r\n #while True:\r\n #print (\"choose difficulty from 1 - 5: \")\r\n #difficulty = int(input())\r\n #if (difficulty > 5 or difficulty < 1):\r\n #print(\"iligal command. try again \")\r\n #continue\r\n #else:\r\n #break\r\n \r\n d = difficulty\r\n \r\n #generate numbers from 1-100\r\n rand = (random.randint(1,100))\r\n\r\n #varible t mean total money . its mean current USD convert to ILS and multiply in random number\r\n url = 'https://v6.exchangerate-api.com/v6/a7dba39e16dcd2a6e9c844af/pair/USD/ILS/'\r\n api_key = 'a7dba39e16dcd2a6e9c844af'\r\n response = requests.get(url).json()\r\n res = urllib.request.urlopen(url)\r\n r = json.loads(res.read())\r\n rate = r.get(\"conversion_rate\")\r\n t = rand * rate #total money in ILS\r\n print(\"its corrent rate from USD to ILS\" ,rate)\r\n\r\n #asking from user what the value he think will be in ILS\r\n print(\"hey user what you think will be result in USD?: \")\r\n get_guess_from_user = float(input())\r\n get_guess_from_user = get_guess_from_user * rate \r\n #get money interval\r\n get_money_interval_min = t - ( 5 - d)\r\n get_money_interval_max = t + ( 5 - d)\r\n play = True\r\n #check if the value stay in generate interval \r\n if( get_guess_from_user >= get_money_interval_min and get_guess_from_user <= get_money_interval_max):\r\n print (\"WOW its correct :) \")\r\n print(play)\r\n elif( get_guess_from_user < get_money_interval_min or get_guess_from_user > get_money_interval_max):\r\n print(\"its not correct. try again\")\r\n print(not play)\r\n \r\n \r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "sub_path": "CurrencyRouletteGame/CurrencyRouletteGame.py", "file_name": "CurrencyRouletteGame.py", "file_ext": "py", "file_size_in_byte": 1787, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "random.randint", "line_number": 21, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 26, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 27, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 27, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 27, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "569814542", "text": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n# Sigmoid = Logistic Function = trả về xác suất ( từ 0 đến 1 )\n# z = W0+W1.X+W2.Y\ndef sigmoid(data,weights):\n z = np.dot(data,weights)\n return 1.0/(1 + np.exp(-z))\n\ndef cost_function(data,RealOutput,weights):\n n = len(RealOutput)\n PredictedOutput = sigmoid(data,weights)\n\n #! Cost function \n cost_class1 = -(RealOutput)*(np.log(PredictedOutput))\n cost_class0 = -(1 - RealOutput)*(np.log(1-PredictedOutput))\n\n cost = np.sum((cost_class1 + cost_class0)) / n\n\n return cost\n\ndef train(data, RealOutput, weights, learning_rate, iteration):\n cost_history = []\n for i in range(iteration):\n # Update weight\n n = len(RealOutput)\n PredictedOutput = sigmoid(data,weights)\n #! Gradient Descent \n weights = weights - (np.dot(data.T,(PredictedOutput - RealOutput)) * learning_rate ) / n\n # Get Cost function\n cost = cost_function(data, RealOutput, weights)\n cost_history.append(cost)\n\n if cost < 0.25:\n return weights, cost_history\n\n return weights, cost_history\nif __name__ == \"__main__\":\n dataFromFile = pd.read_csv('data.csv', header = None)\n\n true_x = [] # Trục x của những giá trị là 1\n true_y = [] # Trục y của những giá trị là 1\n false_x = [] # Trục x của những giá trị là 0\n false_y = [] # Trục y của những giá trị là 0\n maxvalue1 = 0\n maxvalue2 = 0\n\n for item in dataFromFile.values:\n if item[2] == 1.:\n true_x.append(item[0])\n true_y.append(item[1])\n if item[0] > maxvalue1:\n maxvalue1 = item[0]\n else:\n false_x.append(item[0])\n false_y.append(item[1])\n if item[1] > maxvalue2:\n maxvalue2 = item[1]\n # RealOutput là giá trị rớt môn hay không ( 0 hoặc 1 ) đọc từ file CSV\n RealOutput = np.zeros((100,1))\n data = np.zeros((100,3))\n # Khởi tạo weight\n weight = [[1.0],[1.0],[1.0]]\n i = 0\n \n for item in dataFromFile.values:\n data[i][0] = 1\n data[i][1] = item[0]\n data[i][2] = item[1]\n RealOutput[i][0] = item[2]\n i = i + 1\n weight,cost_history = train(data,RealOutput,weight,0.001,350000) \n\n plt.scatter(true_x,true_y, marker = 'o', c = 'g',label = 'Passed')\n plt.scatter(false_x, false_y, marker = 's', c = 'r',label = 'Failed')\n\n plt.xlabel(\"Test1\")\n plt.ylabel(\"Test2\")\n\n # Test dữ liệu mới\n if(sigmoid([1,80,80],weight) > 0.5):\n print(\"1\")\n else:\n print(\"0\")\n\n x2 = (- weight[0] - weight[1]*maxvalue1) / weight[2]\n x2_1 = (-weight[0] - weight[1]*maxvalue2)/ weight[2]\n\n plt.plot([maxvalue1,x2],[x2_1,maxvalue2],label = 'Seperation')\n\n plt.legend()\n plt.show()\n", "sub_path": "logistic_regression.py", "file_name": "logistic_regression.py", "file_ext": "py", "file_size_in_byte": 2864, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "numpy.dot", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 30, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}]} +{"seq_id": "496744601", "text": "\"\"\"\nPython class example.\nThis module does some more consolidating...\n\"\"\"\nimport codecs\nimport io\n# importing the html_rendering code with a short name for easy typing.\nimport html_render as hr\n\n## writing the file out:\ndef render(page, filename):\n \"\"\"\n render the tree of elements\n\n This uses cSstringIO to renderto memory, then dump to console and\n write to file -- very handy!\n \"\"\"\n\n f = io.StringIO()\n\n page.render(f)\n\n print(f.getvalue())\n\n codecs.open(filename, 'w', encoding=\"utf-8\").write( f.getvalue() )\n \nclass Element(object):\n \"\"\"\n An element with optional attributes and multiple items in the content\n \"\"\"\n tag = \"\"\n indent = \" \"\n def __init__(self, content=None, **attributes):\n \"\"\"\n initialize an element with optional attributes, and any number of sub-elements and content\n :param content: content of the element: single string or another element.\n an empty string will be ignored\n :param [attributes]: optional attributes as keyword parameters.\n example:\n \"\"\"\n if not content:\n self.children = []\n else:\n self.children = [content]\n\n self.attributes = attributes\n\n def append(self, element):\n self.children.append(element)\n\n def render(self, file_out, ind = \"\"):\n \"\"\"\n an html rendering method for elements that have attributes and content\n \"\"\"\n file_out.write(\"\\n\")\n file_out.write(ind)\n file_out.write(\"<%s\"%self.tag)\n for key, value in self.attributes.items():\n file_out.write(' %s=\"%s\"'%(key, value) )\n file_out.write(\">\")\n for child in self.children:\n try:\n child.render(file_out, ind + self.indent)\n except AttributeError:\n file_out.write(\"\\n\")\n file_out.write(ind + self.indent)\n file_out.write(str(child))\n file_out.write(\"\\n\")\n file_out.write(ind)\n file_out.write(''%self.tag)\n\nclass Html(Element):\n tag = \"html\"\n\nclass Head(Element):\n tag = \"head\"\n\nclass Body(Element):\n tag = \"body\"\n\nclass P(Element):\n tag = \"p\"\n\nclass Ul(Element):\n \"\"\"\n element for an unordered list\n \"\"\"\n tag = \"ul\"\n\nclass Li(Element):\n \"\"\"\n element for the item in a list\n \"\"\"\n tag = \"li\"\n\nclass SelfClosingTag(Element):\n \"\"\"\n Element with a single tag -- no content, only attributes\n \"\"\"\n def __init__(self, **attributes):\n self.attributes = attributes\n\n def render(self, file_out, ind = \"\"):\n \"\"\"\n an html rendering method for self-closing elements:\n attributes, but no content a no closing tag\n \"\"\"\n file_out.write(\"\\n\")\n file_out.write(ind)\n file_out.write(\"<%s\"%self.tag)\n for key, value in self.attributes.items():\n file_out.write(' %s=\"%s\"'%(key, value) )\n file_out.write(\" />\")\n\nclass Hr(SelfClosingTag):\n tag = \"hr\"\n\nclass OneLineTag(Element):\n\n def render(self, file_out, ind = \"\"):\n \"\"\"\n an html rendering method for elements that have attributes and content\n \"\"\"\n file_out.write(\"\\n\")\n file_out.write(ind)\n file_out.write(\"<%s\"%self.tag)\n for key, value in self.attributes.items():\n file_out.write(' %s=\"%s\"'%(key, value) )\n file_out.write(\">\")\n for child in self.children:\n try:\n child.render(file_out)\n except AttributeError:\n file_out.write(str(child))\n file_out.write(''%self.tag)\n\nclass Title(OneLineTag):\n tag = \"title\"\n\nclass A(OneLineTag):\n \"\"\"\n element for a link ( tag )\n \"\"\"\n tag = \"a\"\n def __init__(self, link, content):\n OneLineTag.__init__(self, content, href=link)\n\nclass H(OneLineTag):\n \"\"\"\n class for header tags, the level is specified in a parameter\n \"\"\"\n def __init__(self, level, content, **attributes):\n OneLineTag.__init__(self, content, **attributes)\n\n self.tag = \"h%i\"%level\n\n\nif __name__ == \"__main__\":\n import sys\n page = Html()\n\n head = Head()\n head.append(Title(\"PythonClass = Revision 1087:\"))\n\n page.append(head)\n\n body = Body()\n\n body.append( H(2, \"PythonClass - Class 6 example\") )\n\n body.append(P(\"Here is a paragraph of text -- there could be more of them, but this is enough to show that we can do some text\",\n style=\"text-align: center; font-style: oblique;\"))\n\n body.append(Hr())\n\n list = Ul(id=\"TheList\", style=\"line-height:200%\")\n list.append( Li(\"The first item in a list\") )\n list.append( Li(\"This is the second item\", style=\"color: red\") )\n item = Li()\n item.append(\"And this is a \")\n item.append( A(\"http://google.com\", \"link\") )\n item.append(\"to google\")\n list.append(item)\n body.append(list)\n\n page.append(body)\n\n render(page, u\"test_html.html\")", "sub_path": "all_files/hw8/sample1.py", "file_name": "sample1.py", "file_ext": "py", "file_size_in_byte": 4956, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "io.StringIO", "line_number": 19, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "258159678", "text": "\"\"\"438. Find All Anagrams in a String\nhttps://leetcode.com/problems/find-all-anagrams-in-a-string/\n\nGiven a string s and a non-empty string p,\nfind all the start indices of p's anagrams in s.\n\nStrings consists of lowercase English letters only and the length of\nboth strings s and p will not be larger than 20,100.\n\nThe order of output does not matter.\n\nExample 1:\n\nInput:\ns: \"cbaebabacd\" p: \"abc\"\nOutput:\n[0, 6]\n\nExplanation:\nThe substring with start index = 0 is \"cba\", which is an anagram of \"abc\".\nThe substring with start index = 6 is \"bac\", which is an anagram of \"abc\".\n\nExample 2:\n\nInput:\ns: \"abab\" p: \"ab\"\nOutput:\n[0, 1, 2]\n\nExplanation:\nThe substring with start index = 0 is \"ab\", which is an anagram of \"ab\".\nThe substring with start index = 1 is \"ba\", which is an anagram of \"ab\".\nThe substring with start index = 2 is \"ab\", which is an anagram of \"ab\".\n\"\"\"\nfrom typing import List\n\n\nclass Solution:\n def find_anagrams(self, s: str, p: str) -> List[int]:\n ns, np = len(s), len(p)\n if ns < np:\n return []\n ans = []\n store = {}\n temp = {}\n for i in range(np):\n store[p[i]] = (store[p[i]] + 1) if p[i] in store else 1\n temp[s[i]] = (temp[s[i]] + 1) if s[i] in temp else 1\n i, j = 0, np - 1\n while j < ns:\n if store == temp:\n ans.append(i)\n if j == ns - 1:\n break\n if temp[s[i]] == 1:\n del temp[s[i]]\n else:\n temp[s[i]] = temp[s[i]] - 1\n temp[s[j + 1]] = (temp[s[j + 1]] + 1) if s[j + 1] in temp else 1\n i += 1\n j += 1\n\n return ans\n", "sub_path": "python-algorithm/leetcode/problem_438.py", "file_name": "problem_438.py", "file_ext": "py", "file_size_in_byte": 1676, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "typing.List", "line_number": 39, "usage_type": "name"}]} +{"seq_id": "378962174", "text": "import os.path\nimport re\nfrom numbers import Number\nfrom lmkp.models.database_objects import *\nfrom lmkp.models.meta import DBSession as Session\nfrom lmkp.views import shapefile\nfrom pyramid.view import view_config\nfrom sqlalchemy.sql.expression import and_\n\n@view_config(route_name='cambodia_read_stakeholders', renderer='json')\ndef cambodia_read_stakeholders2(request):\n\n filepath = \"%s/documents/cambodia/data/Government_Data_Complete_4326_points2\" % os.path.dirname(os.path.dirname(__file__))\n\n # This dictionary maps the attribute in the Shapefile to the mandatory and\n # optional fields\n attributeMap = {\n 10: 'Name',\n 8: 'Country of origin',\n 3: 'Address'\n }\n\n # Map the country names from the input file to the defined countries in the\n # database\n countriesMap = {\n \"American\": \"United States\",\n \"Cambodia\": \"Cambodia\",\n \"Cambodian\": \"Cambodia\",\n \"chinese\": \"China\",\n \"Chinese\": \"China\",\n \"India\": \"India\",\n \"Indian\": \"India\",\n \"Israeli\": \"Israel\",\n \"Korean\": \"South Korea\",\n \"Malaysian\": \"Malaysia\",\n \"Taiwanese\": \"Taiwan\",\n \"Thai\": \"Thailand\",\n \"Vietnam\": \"Vietnam\",\n \"Vietnamese\": \"Vietnam\"\n }\n\n\n shp = shapefile.Reader(filepath)\n records = shp.shapeRecords()\n\n\n # Main dict to output\n stakeholderDiffObject = {}\n stakeholderDiffObject['stakeholders'] = []\n\n # List of already considered stakeholders (name)\n knownStakeholders = []\n\n # Retreive every feature with its geometry and attributes\n for record in records:\n #if record.record[2].strip() != '' and record.record[2] is not None:\n # pass\n\n # A dict for the current stakeholder\n stakeholderObject = {}\n stakeholderObject['taggroups'] = []\n\n # Loop all attributes\n for i in [10, 8, 3]:\n\n tagGroup = {}\n tagGroup['tags'] = []\n\n # Write all attributes that are not empty or None.\n # It is necessary to add the op property!\n # Each attribute is written to a separate taggroup\n if record.record[i].strip() != \"\":\n\n # Get the value\n value = unicode(record.record[i])\n # Check if the value has to be looked up\n if attributeMap[i] == 'Name':\n value = regex_name(value)\n stakeholderName = value\n\n if attributeMap[i] == 'Country of origin':\n value = countriesMap[value]\n\n tagGroup['tags'].append({\"key\": attributeMap[i], \"value\": value, \"op\": \"add\"})\n tagGroup['op'] = 'add'\n tagGroup['main_tag'] = {\"key\": attributeMap[i], \"value\": value}\n\n stakeholderObject['taggroups'].append(tagGroup)\n\n\n if stakeholderName not in knownStakeholders:\n # Append the stakeholder to the main object\n stakeholderDiffObject['stakeholders'].append(stakeholderObject)\n knownStakeholders.append(stakeholderName)\n\n return stakeholderDiffObject\n\n@view_config(route_name='cambodia_read_activities', renderer='json')\ndef cambodia_read_activities2(request):\n filepath = \"%s/documents/cambodia/data/Government_Data_Complete_4326_points2\" % os.path.dirname(os.path.dirname(__file__))\n\n # This dictionary maps the attribute in the Shapefile to the mandatory and\n # optional fields\n attributeMap = {\n 0: 'Intended area (ha)',\n 5: 'Intention of Investment',\n# 7: 'Year of agreement',\n 10: 'Investor',\n 13: 'Negotiation Status',\n }\n\n shp = shapefile.Reader(filepath)\n records = shp.shapeRecords()\n\n # Main dict to output\n activityDiffObject = {}\n activityDiffObject['activities'] = []\n\n # List of already used stakeholders\n usedStakeholders = []\n\n # Retreive every feature with its geometry and attributes\n for record in records:\n\n # A dict for the current stakeholder\n activityObject = {}\n activityObject['taggroups'] = []\n stakeholdersObject = []\n\n # Loop all attributes\n for k in [0, 5, 7, 10, 13]:\n\n\n if k == 13:\n taggroup = {}\n taggroup['tags'] = []\n taggroup['op'] = \"add\"\n\n if record.record[k].strip() == '':\n taggroup['tags'].append({'key': attributeMap[k], 'op': \"add\", 'value': 'Contract signed'})\n taggroup['main_tag'] = {'key': attributeMap[k], 'value': record.record[k][0]}\n else:\n taggroup['tags'].append({'key': attributeMap[k], 'op': \"add\", 'value': 'Contract cancelled'})\n taggroup['main_tag'] = {'key': attributeMap[k], 'value': record.record[k][0]}\n\n activityObject['taggroups'].append(taggroup)\n\n\n# elif type(record.record[k]) == type(list()):\n# if k == 7:\n# taggroup = {}\n# taggroup['tags'] = []\n# taggroup['op'] = \"add\"\n# taggroup['tags'].append({'key': attributeMap[k], 'op': \"add\", 'value': record.record[k][0]})\n# taggroup['main_tag'] = {'key': attributeMap[k], 'value': record.record[k][0]}\n# activityObject['taggroups'].append(taggroup)\n\n\n # Write all attributes that are not empty or None.\n # It is necessary to add the op property!\n # Each attribute is written to a separate taggroup\n elif isinstance(record.record[k], basestring) and record.record[k].strip() != '':\n\n\n if k == 5:\n taggroup = {}\n taggroup['tags'] = []\n taggroup['op'] = \"add\"\n value = guess_intention(record.record[k])\n taggroup['tags'].append({'key': attributeMap[k], 'op': \"add\", 'value': value})\n taggroup['main_tag'] = {'key': attributeMap[k], 'value': value}\n activityObject['taggroups'].append(taggroup)\n\n# if k == 7:\n# taggroup = {}\n# taggroup['tags'] = []\n# taggroup['op'] = \"add\"\n# taggroup['tags'].append({'key': attributeMap[k], 'op': \"add\", 'value': int(record.record[k].split('/')[0])})\n# taggroup['main_tag'] = {'key': attributeMap[k], 'value': int(record.record[k].split('/')[0])}\n# activityObject['taggroups'].append(taggroup)\n\n if k == 10:\n investor_name = regex_name(record.record[k])\n\n sh = Session.query(Stakeholder).join(SH_Tag_Group).\\\n join(SH_Tag, SH_Tag_Group.id == SH_Tag.fk_sh_tag_group).\\\n join(SH_Key).\\\n join(SH_Value).filter(and_(SH_Key.key == 'Name', SH_Value.value == investor_name)).first()\n\n if sh is not None:\n previous_version = usedStakeholders.count(str(sh.stakeholder_identifier))\n\n # TODO: For the moment, add the same stakeholder only\n # once because it crashes otherwise (Changeset issue).\n if previous_version == 0:\n\n stakeholdersObject.append({\"id\": str(sh.stakeholder_identifier), \"op\": \"add\", \"role\": 6, \"version\": (previous_version + 1)})\n\n usedStakeholders.append(str(sh.stakeholder_identifier))\n\n\n elif isinstance(record.record[k], Number):\n\n taggroup = {}\n taggroup['tags'] = []\n taggroup['op'] = \"add\"\n\n taggroup['tags'].append({'key': attributeMap[k], 'op': \"add\", 'value': record.record[k]})\n taggroup['main_tag'] = {'key': attributeMap[k], 'value': record.record[k]}\n activityObject['taggroups'].append(taggroup)\n\n taggroup = {}\n taggroup['tags'] = []\n taggroup['op'] = \"add\"\n taggroup['tags'].append({'key': 'Country', 'op': \"add\", 'value': 'Cambodia'})\n taggroup['main_tag'] = {'key': 'Country', 'value': 'Cambodia'}\n activityObject['taggroups'].append(taggroup)\n\n taggroup = {}\n taggroup['tags'] = []\n taggroup['op'] = \"add\"\n taggroup['tags'].append({'key': 'Data source', 'op': \"add\", 'value': 'Government sources'})\n taggroup['main_tag'] = {'key': 'Data source', 'value': 'Government sources'}\n activityObject['taggroups'].append(taggroup)\n\n # Add the missing mandatory key: Spatial accuracy\n activityObject['taggroups'].append(create_taggroup_dict('Spatial Accuracy', '100m to 1km'))\n\n # Add the geometry\n\n activityObject['geometry'] = {'coordinates': [record.shape.points[0][0], record.shape.points[0][1]], 'type': 'Point'}\n\n activityObject['stakeholders'] = stakeholdersObject\n # Append the stakeholder to the main object\n activityDiffObject['activities'].append(activityObject)\n\n return activityDiffObject\n\ndef create_taggroup_dict(key, value):\n taggroup = {}\n taggroup['op'] = 'add'\n taggroup['tags'] = []\n taggroup['tags'].append({\"key\": key, \"value\": value, \"op\": \"add\"})\n taggroup['main_tag'] = {\"key\": key, \"value\": value}\n return taggroup\n\ndef regex_name(value):\n value = re.sub(r'\\ \\(Region [IV]*\\)$', '', value)\n value = re.sub(r'\\ Region \\([0-9]*\\)$', '', value)\n value = re.sub(r'\\ [IV]*$', '', value)\n\n return value\n\ndef guess_intention(value):\n\n \"\"\"\n - Agriculture\n - Forestry\n - Mining\n - Tourism\n - Industry\n - Conservation\n - Renewable energy\n - Other\n \"\"\"\n\n if re.match(r'.*oil.*|.*rubber.*|.*cassava.*|.*shrimp.*|.*tapioca.*|.*sugar.*|.*tree.*|.*plantation.*', value, re.I):\n return \"Agriculture\"\n if re.match(r'.*agro.*', value, re.I):\n return \"Agriculture\"\n if re.match(r'.*acacia.*', value, re.I):\n return \"Agriculture\"\n if re.match(r'.*corn.*', value, re.I):\n return \"Agriculture\"\n\n if re.match(r'.*tourism.*|.*turi.*', value, re.I):\n return \"Tourism\"\n\n if re.match(r'.*dam.*', value, re.I):\n return \"Renewable energy\"\n\n if re.match(r'.*port.*', value, re.I):\n return \"Other\"\n\n\n return \"Other\"", "sub_path": "lmkp/views/cambodia_import.py", "file_name": "cambodia_import.py", "file_ext": "py", "file_size_in_byte": 10299, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.path.path.dirname", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 13, "usage_type": "name"}, {"api_name": "lmkp.views.shapefile.Reader", "line_number": 43, "usage_type": "call"}, {"api_name": "lmkp.views.shapefile", "line_number": 43, "usage_type": "name"}, {"api_name": "pyramid.view.view_config", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.path.dirname", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 100, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 100, "usage_type": "name"}, {"api_name": "lmkp.views.shapefile.Reader", "line_number": 112, "usage_type": "call"}, {"api_name": "lmkp.views.shapefile", "line_number": 112, "usage_type": "name"}, {"api_name": "lmkp.models.meta.DBSession.query", "line_number": 185, "usage_type": "call"}, {"api_name": "lmkp.models.meta.DBSession", "line_number": 185, "usage_type": "name"}, {"api_name": "sqlalchemy.sql.expression.and_", "line_number": 188, "usage_type": "call"}, {"api_name": "numbers.Number", "line_number": 202, "usage_type": "argument"}, {"api_name": "pyramid.view.view_config", "line_number": 98, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 248, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 249, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 250, "usage_type": "call"}, {"api_name": "re.match", "line_number": 267, "usage_type": "call"}, {"api_name": "re.I", "line_number": 267, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 269, "usage_type": "call"}, {"api_name": "re.I", "line_number": 269, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 271, "usage_type": "call"}, {"api_name": "re.I", "line_number": 271, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 273, "usage_type": "call"}, {"api_name": "re.I", "line_number": 273, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 276, "usage_type": "call"}, {"api_name": "re.I", "line_number": 276, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 279, "usage_type": "call"}, {"api_name": "re.I", "line_number": 279, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 282, "usage_type": "call"}, {"api_name": "re.I", "line_number": 282, "usage_type": "attribute"}]} +{"seq_id": "231626454", "text": "import twitter\nimport logging\nimport sys\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.INFO)\nch = logging.StreamHandler(sys.stdout)\nch.setLevel(logging.INFO)\nlogger.addHandler(ch)\n\n\nclass twitter_feeder():\n def __init__(self):\n self.api = twitter.Api(consumer_key=\"mYh1sti2PMGKmiU0C8pPLbGGl\",\n consumer_secret='0Kf8DNw8HklsHnINLGOPRryZMo3EvSCJvIL0Edh9JKS6ShCfx1',\n access_token_key='810489185861824512-sa6J6p1RAaolOFcLh1TOPmuOP9XHmDM',\n access_token_secret='zczOpmqn2vpzIg68XuKhXQgXzE4nMhuJGTu1gGugyV6Mv',\n )\n\n #logger.info(self.api.VerifyCredentials())\n \n def get_user_timeline(self, screen_name,count=10):\n logger.info('number of tweets:%d', count)\n \n t = self.api.GetUserTimeline(screen_name=screen_name,count=count)\n tweets = [i.AsDict() for i in t]\n\n return tweets\n \n \n'''\nfor t in tweets:\n print(t['text'])\n'''\n", "sub_path": "bori/twitter_feed.py", "file_name": "twitter_feed.py", "file_ext": "py", "file_size_in_byte": 972, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "logging.getLogger", "line_number": 5, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 6, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 7, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 8, "usage_type": "attribute"}, {"api_name": "twitter.Api", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "650268863", "text": "from flask import Flask\r\nfrom flask_restful import Api, Resource, reqparse, abort, fields, marshal_with\r\nfrom flask_sqlalchemy import SQLAlchemy\r\nfrom flask_marshmallow import Marshmallow \r\nfrom flask import request, jsonify\r\nimport requests\r\nimport json\r\napp = Flask(__name__)\r\napi = Api(app)\r\n\r\nma = Marshmallow(app)\r\n\r\n\r\n\r\n\r\n\t\r\n \r\n \r\n\r\n\r\n\r\nclass front_end1(Resource):\r\n \r\n def get(self,book_topic):\r\n response = requests.get(\"http://192.168.1.215:6000/\" + \"search\", {\"topic\" :book_topic})\r\n if not response :\r\n return jsonify({'message' : 'topic does not exsit'})\r\n temppp=json.loads( str(response.content, 'utf-8'))\r\n return jsonify(temppp)\r\napi.add_resource(front_end1, \"/search/\")\r\n\r\n\r\n@app.route('/lookup/', methods=['GET']) \r\ndef get2(item_id):\r\n response = requests.get(\"http://192.168.1.215:6000/\" + \"lookup\", {\"item_id\" :item_id})\r\n if not response:\r\n return jsonify({'message' : 'book does not exsist with that id'})\r\n temppp=json.loads( str(response.content, 'utf-8'))\r\n\r\n\r\n return jsonify({\"titel\" :temppp['titel'] ,\"price\" :temppp['cost'] , \"quantity\": temppp['number_in_stock'] })\r\n\r\n\r\n@app.route('/buy/', methods=['put']) \r\ndef buy(item_id):\r\n response = requests.put(\"http://192.168.1.251:5000/\" + \"buy\", {\"item_id\" :item_id})\r\n temppp=json.loads( str(response.content, 'utf-8'))\r\n if temppp['order_id'] ==-1 :\r\n return jsonify({'message' : 'book does not exsist!'})\r\n if temppp['order_id'] == -2 :\r\n return jsonify({'message' : 'quantity in stock is zero!'}) \r\n else:\r\n return jsonify(temppp)\r\n\r\n\r\n\r\n\r\n \r\n\r\n \r\n@app.route('/increment_quantity_in_stock/', methods=['put']) \r\ndef inc(item_id):\r\n response = requests.post(\"http://192.168.1.215:6000/\" + \"book_incraese_in_stock\", {\"item_id\" :item_id})\r\n if not response:\r\n return jsonify({'message' : 'book does not exsist!'})\r\n temppp=json.loads( str(response.content, 'utf-8'))\r\n return jsonify(temppp)\r\n\r\n\r\n@app.route('/deccrement_quantity_in_stock/', methods=['put']) \r\ndef dec(item_id):\r\n response = requests.post(\"http://192.168.1.215:6000/\" + \"book_decraese_in_stock\", {\"item_id\" :item_id})\r\n temppp=json.loads( str(response.content, 'utf-8'))\r\n if temppp[0]['titel'] == None:\r\n return jsonify({'message' : 'book does not exsist!'})\r\n \r\n if temppp[0]['titel']==\"zero\" :\r\n return jsonify({'message' : 'zero quantity'})\r\n \r\n return jsonify(temppp[0])\r\n \r\n#,ssl_context= ('cert.pem', 'key.pem')\r\nif __name__ == \"__main__\":\r\n\tapp.run(host=\"0.0.0.0\", debug=True, port=\"8000\" )\r\n\r\n\r\n", "sub_path": "front_end_http.py", "file_name": "front_end_http.py", "file_ext": "py", "file_size_in_byte": 2691, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "flask_restful.Api", "line_number": 9, "usage_type": "call"}, {"api_name": "flask_marshmallow.Marshmallow", "line_number": 11, "usage_type": "call"}, {"api_name": "flask_restful.Resource", "line_number": 22, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 27, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 29, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 37, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 41, "usage_type": "call"}, {"api_name": "requests.put", "line_number": 46, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 47, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 49, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 53, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 65, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 66, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 67, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 72, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 75, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 80, "usage_type": "call"}]} +{"seq_id": "224076481", "text": "import sys\nimport json\n'''\nThe LIWC .dic format looks like this:\n%\n1 funct\n2 pronoun\n.\n.\n.\n..\n%\na 1 10\nabdomen* 146 147\nabout 1 16 17\n\ngive that file into this as command line argument, get a json trie on stdout\n'''\n\ncategories = {}\ntrie = {}\n\n\ndef addToTrie(key, categories):\n cursor = trie\n for letter in key:\n if letter == '*':\n cursor['*'] = categories\n break\n if letter not in cursor:\n cursor[letter] = {}\n cursor = cursor[letter]\n cursor['$'] = categories\n\n\ndictionary_file = sys.argv[1]\nwith open(dictionary_file, 'r') as fp:\n\tfor line in fp.readlines():\n\t\tif not line.startswith('%'):\n\t\t\tparts = line.strip().split('\\t')\n\t\t\tif parts[0].isdigit():\n\t\t\t\t# store category names\n\t\t\t\tcategories[parts[0]] = parts[1]\n\t\t\telse:\n\t\t\t\t# print parts[0], ':', parts[1:]\n\t\t\t\taddToTrie(parts[0], [categories[category_id] for category_id in parts[1:]])\n\nprint (json.dumps(trie, sort_keys=True))\n", "sub_path": "Bigdata_Final_Project/src/main/resources/scripts/DicToTrieConvertor.py", "file_name": "DicToTrieConvertor.py", "file_ext": "py", "file_size_in_byte": 965, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "sys.argv", "line_number": 36, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "547910894", "text": "from flask import Flask,render_template,url_for,request\nimport pandas as pd\nimport pickle\nimport json\nimport os\n#import urllib.request\nimport urllib2\nimport json\n\n\napp= Flask(__name__,static_folder=\"static\")\n\n\n#Define a view function named 'home', which renders the html page 'home.html'\n#Ensure that the view function 'home' is routed when a user access the URL '/' .\n\n@app.route('/')\ndef home():\n\treturn render_template('index.html')\n\n\n\n#Define a view function named 'predict', which does the function of getting the text entered by user in home.html and predicts if it is spam or not and renders the result in result.html\n#Ensure that the view function 'predict' is routed when a user access the URL '/predict' .\n\n\n\n\n@app.route('/predict',methods=['POST'])\ndef predict():\n\t\n\t\n\tdata = {\n \"Inputs\": {\n \"input1\":\n [\n {\n 'text': request.form['message'], \n \n }\n ],\n },\n\t\t\"GlobalParameters\": {\n\t\t\t\t\t\t\t}\n\t}\n\n\tbody = str.encode(json.dumps(data))\n\tprint(data)\n\turl = 'https://ussouthcentral.services.azureml.net/workspaces/cb42094b240f4bdd94e35c93616c410e/services/b35a822d95804870adbd49da925f6f74/execute?api-version=2.0&format=swagger'\n\tapi_key = 'ljmUH0k0X4Yu8ooYEI10+09JiUwzi569UET1pDKnjsefl+dxt4XCaBoVEdeagK3y2m0Q8Vp3Z4IUaUqTRVvDBQ==' # Replace this with the API key for the web service\n\theaders = {'Content-Type':'application/json', 'Authorization':('Bearer '+ api_key)}\n\treq = urllib2.Request(url, body, headers)\n\tresponse = urllib2.urlopen(req)\n\t#req = urllib.request.Request(url, body, headers)\n\t#response = urllib.request.urlopen(req)\n\t#result = response.read().decode('utf-8')\n\tresult = response.read()\n\tprint(result)\n\ty = json.loads(result)\n\tprint(y['Results']['output1'][0]['Scored Labels'])\n\ty = json.loads(result)\n\tprediction_value=y['Results']['output1'][0]['Scored Labels']\n\tif prediction_value==\"1\":\n\t\tprediction_text=\"Negative\"\n\telif prediction_value==\"3\":\n\t\tprediction_text=\"Moderate\"\n\telif prediction_value==\"5\":\n\t\tprediction_text=\"Positive\"\n\telse:\n\t\tprediction_text=\"Irrelevant Tweet\"\n\t\n\t\n\tprint(prediction_text)\n\t\t\n\t\n\t\t\n\treturn render_template('result.html',prediction = prediction_text)\n\n\n\n\t\n\t\t\n\n\n\n\n\n\n# make your app run in 0.0.0.0 host and port 8000\nif __name__ == '__main__':\n\tapp.run(debug=True)\n\t#app.run('0.0.0.0',port=8000,debug=True)\n", "sub_path": "app/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 2413, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "flask.Flask", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 38, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 47, "usage_type": "call"}, {"api_name": "urllib2.Request", "line_number": 52, "usage_type": "call"}, {"api_name": "urllib2.urlopen", "line_number": 53, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 59, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 77, "usage_type": "call"}]} +{"seq_id": "305967565", "text": "import gym\nfrom gym.envs.registration import register\n\nimport ray\nimport ray.rllib.agents.ppo.ppo as ppo\nfrom ray.tune.registry import register_env\nfrom ray.tune import run_experiments, grid_search\n\nenv_name = \"CPGenLQREnv\" \nenv_version_num=0\nenv_name = env_name + '-v' + str(env_version_num)\n\ndef pass_params_to_gym(env_name):\n register(\n id=env_name,\n entry_point=(\"CPGenLQREnv:CPGenLQREnv\"),\n max_episode_steps=env_params[\"horizon\"],\n kwargs={\"env_params\":env_params}\n )\n\n\ndef create_env(env_config):\n pass_params_to_gym(env_name) \n env = gym.envs.make(env_name)\n return env\n\nif __name__ == '__main__':\n #horizon, exp_length upper bounds\n env_params = {\"horizon\":120, \"exp_length\":6, \"reward_threshold\":-10, \"eigv_low\": 0.5,\n \"eigv_high\": 2, \"q_scaling\":[0,2], \"r_scaling\":[0,2], \"dim\": 3}\n register_env(env_name, lambda env_config: create_env(env_config))\n num_cpus = 15\n ray.init(redirect_output=False)\n config = ppo.DEFAULT_CONFIG.copy()\n config[\"train_batch_size\"] = 30000\n config[\"num_sgd_iter\"]=10\n config[\"num_workers\"]=num_cpus\n config[\"gamma\"] = 0.95\n config[\"horizon\"] = env_params[\"horizon\"]\n config[\"use_gae\"] = True\n config[\"lambda\"] = 0.1\n config[\"lr\"] = grid_search([5e-6])\n config[\"sgd_minibatch_size\"] = 64\n config[\"model\"].update({\"fcnet_hiddens\": [256, 256, 256]}) # number of hidden layers in NN\n\n\n trials = run_experiments({\n \"GenLQR_tests\": {\n \"run\": \"PPO\", # name of algorithm\n \"env\": \"CPGenLQREnv-v0\", # name of env\n \"config\": config,\n \"checkpoint_freq\": 20, # how often to save model params\n #\"max_failures\": 999 # Not worth changing\n \"stop\": {\"training_iteration\": 2000}\n #\"upload_dir\": \"test_upload_dir\"\n }\n })\n #agent = ppo.PPOAgent(config=config, env=env_name)\n #filename = \"reward_means_{}_{}.txt\".format(env_params[\"horizon\"], str(env_params[\"eigv_high\"]).replace('.','-'))\n\n #for i in range(1000):\n #result = agent.train()\n #print('-'*60)\n #print(\"Epoch:\" + str(i))\n #print(result[\"episode_reward_mean\"])\n #print('-'*60)\n #with open(filename, 'a') as f:\n #f.write(str(result[\"episode_reward_mean\"])+'\\n')\n", "sub_path": "cpremote_run/cpgen_script.py", "file_name": "cpgen_script.py", "file_ext": "py", "file_size_in_byte": 2339, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "gym.envs.registration.register", "line_number": 14, "usage_type": "call"}, {"api_name": "gym.envs.make", "line_number": 24, "usage_type": "call"}, {"api_name": "gym.envs", "line_number": 24, "usage_type": "attribute"}, {"api_name": "ray.tune.registry.register_env", "line_number": 31, "usage_type": "call"}, {"api_name": "ray.init", "line_number": 33, "usage_type": "call"}, {"api_name": "ray.rllib.agents.ppo.ppo.DEFAULT_CONFIG.copy", "line_number": 34, "usage_type": "call"}, {"api_name": "ray.rllib.agents.ppo.ppo.DEFAULT_CONFIG", "line_number": 34, "usage_type": "attribute"}, {"api_name": "ray.rllib.agents.ppo.ppo", "line_number": 34, "usage_type": "name"}, {"api_name": "ray.tune.grid_search", "line_number": 42, "usage_type": "call"}, {"api_name": "ray.tune.run_experiments", "line_number": 47, "usage_type": "call"}]} +{"seq_id": "604845042", "text": "from __future__ import unicode_literals\n\nimport collections\nimport requests\n\nfrom urlparse import urljoin\n\nBASE_URL = 'http://archive.org/'\n\n\nclass InternetArchiveClient(object):\n\n def __init__(self, base_url=BASE_URL, timeout=None):\n self._search_url = urljoin(base_url, '/advancedsearch.php')\n self._metadata_url = urljoin(base_url, '/metadata/')\n self._download_url = urljoin(base_url, '/download/')\n self._bookmarks_url = urljoin(base_url, '/bookmarks/')\n self._session = requests.Session()\n self._timeout = timeout\n\n def search(self, query, fields=None, sort=None, rows=None, start=None):\n response = self._session.get(self._search_url, params={\n 'q': query,\n 'fl[]': fields,\n 'sort[]': sort,\n 'rows': rows,\n 'start': start,\n 'output': 'json'\n }, timeout=self._timeout)\n if not response.content:\n raise self.SearchError(response.url)\n return self.SearchResult(response.json())\n\n def metadata(self, path):\n url = urljoin(self._metadata_url, path.lstrip('/'))\n response = self._session.get(url, timeout=self._timeout)\n data = response.json()\n\n if not data:\n raise LookupError('Internet Archive item %s not found' % path)\n elif 'error' in data:\n raise LookupError(data['error'])\n elif 'result' in data:\n return data['result']\n else:\n return data\n\n def bookmarks(self, username):\n url = urljoin(self._bookmarks_url, username + '?output=json')\n response = self._session.get(url, timeout=self._timeout)\n # requests for non-existant users yield text/xml response\n if response.headers['Content-Type'] != 'application/json':\n raise LookupError('Internet Archive user %s not found' % username)\n return response.json()\n\n def geturl(self, identifier, filename=None):\n if filename:\n return urljoin(self._download_url, identifier + '/' + filename)\n else:\n return urljoin(self._download_url, identifier + '/')\n\n class SearchResult(collections.Sequence):\n\n def __init__(self, result):\n self.query = result['responseHeader']['params']['q']\n self.rowcount = result['response']['numFound']\n self.docs = result['response']['docs']\n\n def __getitem__(self, key):\n return self.docs[key]\n\n def __len__(self):\n return len(self.docs)\n\n def __iter__(self):\n return iter(self.docs)\n\n class SearchError(Exception):\n\n pass\n", "sub_path": "mopidy_internetarchive/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 2642, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "urlparse.urljoin", "line_number": 14, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 15, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 16, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 17, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 18, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 35, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 49, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 58, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 60, "usage_type": "call"}, {"api_name": "collections.Sequence", "line_number": 62, "usage_type": "attribute"}]} +{"seq_id": "434791106", "text": "\n# Dakota Abernathy\n# Neha Saini\n# ENPM 661\n# Project3-phase3\n\n\nimport math\nimport random\nfrom queue import PriorityQueue\nimport pygame\nimport time\nfrom random import randint\n\nGRAIN = 30\nHEIGHT = 10 * GRAIN\nWIDTH = 10 * GRAIN\nSCALE = 2\n\nboard = None\nstart = None\ntarget = None\nreal_time = False\n\nWHITE = (255, 255, 255)\nBLACK = (0, 0, 0, 255)\nGREEN = (0, 255, 0)\nRED = (255, 0, 0)\nCYAN = (0, 255, 255)\nMAGENTA = (255, 0, 255)\nYELLOW = (255, 255, 0)\n\nBOT_RADIUS = 1.05 # 105mm\nOBSTACLE_CLEARANCE = 5\nCLEARANCE = BOT_RADIUS + OBSTACLE_CLEARANCE\nTHRESHOLD = 6\nnodes_visited = []\nactions = [40, 42, 0]\npath = []\nSQRT2 = math.sqrt(2)\nnodes = None\nfound_path = True\n\nr = 0.038\nL = 0.354\ndt = 0.1\nstop_time = 6\nSCALAR = GRAIN * SCALE\n\n\n# distance between two points\ndef distance(x1, y1, x2, y2):\n return math.sqrt(pow((x2-x1), 2)+pow((y2-y1), 2))\n\n\ndef round_to_n(num, n=3):\n return n * round(num / n)\n\n\n# class to keep track of each place visited\nclass Node:\n def __init__(self, x, y, theta, end_x=0, end_y=0, parent=None, dist=None, ul=None, ur=None, end_theta=0):\n self.x = int(x)\n self.y = int(y)\n self.end_x = int(end_x)\n self.end_y = int(end_y)\n self.parent = parent\n self.theta = int(theta + .5) % 360\n self.end_theta = int(end_theta + .5) % 360\n if parent:\n self.path_length = parent.path_length + dist\n self.L = ul\n self.R = ur\n else:\n self.path_length = 0\n self.L = 0\n self.R = 0\n if target:\n self.h = self.heuristic()\n else:\n self.h = 0\n\n def heuristic(self): # a* heuristic\n return math.sqrt(math.pow(target.x - self.end_x, 2) + math.pow(target.y - self.end_y, 2)) + self.path_length / 1\n\n def __eq__(self, other):\n return self.x == other.x and self.y == other.y\n\n def __str__(self):\n return \"[\" + str(round_to_n(self.end_x)) + \", \" + str(round_to_n(self.end_y)) + \", \" \\\n + str(round_to_n(self.end_theta, 10))\n\n def __lt__(self, other):\n return self.path_length < other.path_length\n\n\ndef draw_node_diff(node, color=CYAN):\n plot_curve(node.x, node.y, node.theta, node.L, node.R, color)\n\n\ndef get_neighbors_rigid_diff(node):\n neighbors = []\n for left in actions:\n for right in actions:\n if right == left == 0:\n continue\n xn, yn, tn, dist = move_curve(node.end_x, node.end_y, node.end_theta, left, right)\n if point_valid(xn, yn, False):\n neighbors.append(Node(node.end_x, node.end_y, node.end_theta, xn, yn, node, dist, left, right, tn))\n return neighbors\n\n\n# returns a randomly-generated node\ndef random_node():\n point = random_point()\n node = Node(point[0], point[1], 0, point[0], point[1])\n new_nodes = get_neighbors_rigid_diff(node)\n random.shuffle(new_nodes)\n if new_nodes:\n return new_nodes[0]\n\n\n# draws a single point with a threshold area around it\ndef draw_point_with_threshold(point, color=GREEN):\n pygame.draw.circle(board, color, [point.x * SCALE, (HEIGHT - point.y) * SCALE], THRESHOLD * SCALE)\n\n\n# makes default board\ndef make_board():\n global board\n pygame.init()\n board = pygame.display.set_mode((int(WIDTH * SCALE), int(HEIGHT * SCALE)))\n pygame.display.set_caption(\"Path finding algorithm\")\n board.fill(WHITE)\n\n # easy\n pygame.draw.circle(board, BLACK, [2 * SCALAR, (HEIGHT - 2 * GRAIN) * SCALE], 1 * SCALAR)\n pygame.draw.circle(board, BLACK, [2 * SCALAR, (HEIGHT - 8 * GRAIN) * SCALE], 1 * SCALAR)\n pygame.draw.rect(board, BLACK, pygame.Rect(\n .25 * SCALAR, (HEIGHT - 5.75 * GRAIN) * SCALE, 1.5 * SCALAR, 1.5 * SCALAR))\n pygame.draw.rect(board, BLACK, pygame.Rect(\n 3.75 * SCALAR, (HEIGHT - 5.75 * GRAIN) * SCALE, 2.5 * SCALAR, 1.5 * SCALAR))\n pygame.draw.rect(board, BLACK, pygame.Rect(\n 7.25 * SCALAR, (HEIGHT - 4 * GRAIN) * SCALE, 1.5 * SCALAR, 2 * SCALAR))\n\n\ndef in_circle(x, y): # check if point in lower circle\n if math.pow(x - 2 * GRAIN, 2) + math.pow(y - (HEIGHT - 2 * GRAIN), 2) >= math.pow(1 * GRAIN + CLEARANCE, 2):\n return False\n return True\n\n\ndef in_circle_2(x, y): # check if point in upper circle\n if math.pow(x - 2 * GRAIN, 2) + math.pow(y - (HEIGHT - 8 * GRAIN) , 2) >= math.pow(1 * GRAIN + CLEARANCE, 2):\n return False\n return True\n\n\ndef in_rect(x, y): # check if point in rectangle\n if .25 * GRAIN - CLEARANCE <= x <= 1.75 * GRAIN + CLEARANCE and \\\n 5.75 * GRAIN + CLEARANCE >= y >= 4.25 * GRAIN - CLEARANCE:\n return True\n return False\n\n\ndef in_rect_2(x, y):\n if 3.75 * GRAIN - CLEARANCE <= x <= 6.25 * GRAIN + CLEARANCE and \\\n 5.75 * GRAIN + CLEARANCE >= y >= 4.25 * GRAIN - CLEARANCE:\n return True\n return False\n\n\ndef in_rect_3(x, y):\n if 7.25 * GRAIN - CLEARANCE <= x <= 8.75 * GRAIN + CLEARANCE and \\\n 4 * GRAIN + CLEARANCE >= y >= 2 * GRAIN - CLEARANCE:\n return True\n return False\n\n\n# check if point is in any obstacle\ndef in_obstacle(x, y):\n if in_circle(x, y) or in_circle_2(x, y) or in_rect(x, y) or in_rect_2(x, y) or in_rect_3(x, y):\n return True\n return False\n\n\ndef is_close(x, y, x_target, y_target):\n return distance(x, y, x_target, y_target) <= THRESHOLD\n\n\n# check if point inside boundaries and not in any obstacle\ndef point_valid(x, y, talk=True):\n if x < 0 or x >= WIDTH:\n if talk:\n print(\"X is outside of boundary [0,\", WIDTH, \"]\")\n return False\n if y < 0 or y > HEIGHT:\n if talk:\n print(\"Y is outside of boundary [0,\", HEIGHT, \"]\")\n return False\n if in_obstacle(x, y):\n if talk:\n print(\"Point is inside an obstacle\")\n return False\n return True\n\n\n# gets single valid point from user\ndef get_point_from_user(word):\n valid = False\n while not valid:\n try:\n x, y = input(\"Enter the coordinates of the \" + word + \" point: \").split()\n x = int(x)\n y = int(y)\n valid = point_valid(x, y, True)\n except:\n print(\"Enter point as X/Y coordinate pair separated by a space\")\n return x, y\n\n\n# get single valid random point\ndef random_point():\n valid = False\n while not valid:\n x = randint(0, WIDTH)\n y = randint(0, HEIGHT)\n valid = point_valid(x, y, False)\n return x, y\n\n\n# gets valid start and target point\ndef get_initial_conditions(human=True):\n global OBSTACLE_CLEARANCE\n if human:\n x1, y1 = get_point_from_user(\"start\")\n x2, y2 = get_point_from_user(\"target\")\n theta = int(input(\"Input starting theta in degrees: \")) % 360\n get_diff()\n OBSTACLE_CLEARANCE = int(input(\"Enter clearance for obstacles\"))\n else:\n x1, y1 = random_point()\n x2, y2 = random_point()\n theta = random.randint(0, 11) * 30 # 30, 60, 90, etc\n return Node(x1, y1, theta, x1, y1), Node(x2, y2, 0, x2, y2)\n\n\n# a* search\ndef turtle_a_star():\n global found_path\n open_list = PriorityQueue()\n open_list.put((0, start))\n open_check = {str(start): 1}\n closed = {str(start): 1}\n itt = 0\n while open_list.qsize():\n next_node = open_list.get()[1]\n if real_time: # plot in real time\n itt = itt + 1\n draw_node_diff(next_node, CYAN)\n if itt % 50 == 0:\n pygame.display.update()\n pygame.event.get()\n\n closed[str(next_node)] = 1\n\n nodes_visited.append(next_node)\n if is_close(next_node.end_x, next_node.end_y, target.x, target.y): # check if done\n target.parent = next_node\n found_path = True\n return True\n\n neighbors = get_neighbors_rigid_diff(next_node)\n\n for neighbor in neighbors: # get neighbors and check if they have been checked yet\n if str(neighbor) in closed or str(neighbor) in open_check:\n continue\n open_list.put((neighbor.heuristic(), neighbor))\n open_check[str(neighbor)] = 1\n\n found_path = False\n return False\n\n\n# work back from target to get path to start\ndef back_track():\n n = target\n while n:\n path.append(n)\n n = n.parent\n path.reverse()\n\n\n# adds all visited nodes, the path, start and end points to board\ndef add_points():\n print(\"Visited: \", len(nodes_visited))\n draw_point_with_threshold(start, GREEN)\n draw_point_with_threshold(target, RED)\n pygame.display.update()\n clock = pygame.time.Clock()\n itt = 0\n time.sleep(.5)\n\n if len(nodes_visited) > 100000:\n rate = 0\n elif len(nodes_visited) > 1000:\n rate = len(nodes_visited) / 4\n else:\n rate = 200\n\n for point in nodes_visited:\n draw_node_diff(point)\n if itt % 10 == 0:\n draw_point_with_threshold(start)\n draw_point_with_threshold(target, RED)\n pygame.display.update()\n pygame.event.get()\n clock.tick(rate)\n itt = itt + 1\n\n pygame.display.update()\n draw_point_with_threshold(start)\n draw_point_with_threshold(target, RED)\n\n print(\"Path: \", len(path))\n\n for point in path:\n draw_node_diff(point, MAGENTA)\n pygame.display.update()\n pygame.event.get()\n clock.tick(12)\n pygame.display.update()\n if path:\n print(\"Path length: \", path[-2].path_length)\n\n\ndef move_curve(x_i, y_i, theta_i, ul, ur):\n t = 0\n x_n = x_i\n y_n = y_i\n theta_n = 3.14 * theta_i / 180\n dist = 0\n while t < stop_time:\n t = t + dt\n x_s = x_n\n y_s = y_n\n x_n += (0.5 * r * (ul + ur) * math.cos(theta_n) * dt)\n y_n += (0.5 * r * (ul + ur) * math.sin(theta_n) * dt)\n theta_n += (r / L) * (ur - ul) * dt\n dist += distance(x_s, y_s, x_n, y_n)\n theta_n = 180 * theta_n / 3.14\n return x_n, y_n, theta_n, dist\n\n\ndef plot_curve(x_i, y_i, theta_i, ul, ur, color = CYAN):\n t = 0\n x_n = x_i\n y_n = y_i\n theta_n = 3.14 * theta_i / 180\n dist = 0\n while t < stop_time:\n t = t + dt\n x_s = x_n\n y_s = y_n\n x_n += (0.5 * r * (ul + ur) * math.cos(theta_n) * dt)\n y_n += (0.5 * r * (ul + ur) * math.sin(theta_n) * dt)\n theta_n += (r / L) * (ur - ul) * dt\n dist += distance(x_s, y_s, x_n, y_n)\n pygame.draw.line(board, color, [x_s * SCALE, (HEIGHT - y_s) * SCALE], [x_n * SCALE, (HEIGHT - y_n) * SCALE])\n return\n\n\ndef get_diff():\n print(\"This system works best with values in the range of 30-60\")\n print(\"The values should not differ more than 5, and work best with a difference of 2 or 3\")\n actions[0] = int(input(\"Enter the smaller value: \"))\n actions[1] = int(input(\"Enter the larger value: \"))\n\n\ndef sanity_check():\n for i in range(100000):\n x = randint(0, WIDTH)\n y = randint(0, HEIGHT)\n if point_valid(x, y):\n pygame.draw.circle(board, RED, [x * SCALE, (HEIGHT - y) * SCALE], 1 * SCALE)\n else:\n pygame.draw.circle(board, GREEN, [x * SCALE, (HEIGHT - y) * SCALE], 1 * SCALE)\n pygame.display.update()\n pygame.event.get()\n\n\nif __name__ == \"__main__\":\n mode = 1\n start, target = get_initial_conditions(True)\n print(\"Finding path...\")\n real_time = True\n\n if real_time:\n make_board()\n add_points()\n\n if turtle_a_star():\n print(\"Path found\")\n else:\n print(\"No path found\")\n\n if mode and found_path:\n make_board()\n back_track()\n add_points()\n pygame.display.update()\n print(\"Done\")\n\n for i in range(501):\n time.sleep(.1)\n events = pygame.event.get()\n for event in events:\n if event.type == pygame.QUIT:\n raise SystemExit\n raise SystemExit\n", "sub_path": "Turtle.py", "file_name": "Turtle.py", "file_ext": "py", "file_size_in_byte": 11839, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "math.sqrt", "line_number": 40, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 53, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 84, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 84, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 118, "usage_type": "call"}, {"api_name": "pygame.draw.circle", "line_number": 125, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 125, "usage_type": "attribute"}, {"api_name": "pygame.init", "line_number": 131, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 132, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 132, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 133, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 133, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 137, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 137, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 138, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 138, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 139, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 139, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 139, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 141, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 141, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 141, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 143, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 143, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 143, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 148, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 154, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 226, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 227, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 244, "usage_type": "call"}, {"api_name": "queue.PriorityQueue", "line_number": 251, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 262, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 262, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 263, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 263, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 299, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 299, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 300, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 300, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 302, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 316, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 316, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 317, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 317, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 321, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 321, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 329, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 329, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 330, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 330, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 332, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 332, "usage_type": "attribute"}, {"api_name": "math.cos", "line_number": 347, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 348, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 365, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 366, "usage_type": "call"}, {"api_name": "pygame.draw.line", "line_number": 369, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 369, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 382, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 383, "usage_type": "call"}, {"api_name": "pygame.draw.circle", "line_number": 385, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 385, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 387, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 387, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 388, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 388, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 389, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 389, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 411, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 411, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 415, "usage_type": "call"}, {"api_name": "pygame.event.get", "line_number": 416, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 416, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 418, "usage_type": "attribute"}]} +{"seq_id": "164482965", "text": "import requests # module to make http/htpps \nimport html5lib # small parser package for html-5\nfrom bs4 import BeautifulSoup\n\namazon_url = \"https://www.amazon.com/HP-Touchscreen-Computer-Quard-Core-802-11ac/dp/B082PZVZB7/ref=sr_1_1_sspa?crid=23S4A884JAUMR&keywords=laptop&qid=1579030350&sprefix=lapt%2Caps%2C139&sr=8-1-spons&psc=1&spLa=ZW5jcnlwdGVkUXVhbGlmaWVyPUE4WlI5OUYxNVdWRlEmZW5jcnlwdGVkSWQ9QTAzNjIwMzUxTVZPSzFJTlZFTERKJmVuY3J5cHRlZEFkSWQ9QTA1MDAxMDAxRkMyWkZMM0lWMzhNJndpZGdldE5hbWU9c3BfYXRmJmFjdGlvbj1jbGlja1JlZGlyZWN0JmRvTm90TG9nQ2xpY2s9dHJ1ZQ==\"\n\nagent = \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.18362\"\n\nagent_header = {\n \"User_Agent\": agent\n\n}\namazon_page = requests.get(amazon_url, headers=agent_header)\n\nprint(type(amazon_page.content))\nsoup = BeautifulSoup(amazon_page.content, \"html5lib\")\n#print(soup.prettify())\n#search a specific id\nprice_span= str(soup.find(id=\"priceblock_ourprice\"))\nprint(price_span)\n\nprice = \"\"\nfor char in price_span:\n if char.isdgit() is True:\n price += char\n if char == \".\":\n price += char\nprint(price) \n\nprice = float(price)\nmax_price = 800\nif price <= max_price:\n print(\"its all yours.\")\nelse:\n print(\"oups, Anna got it.\")", "sub_path": "week_1/Day 4/amazonwebcrap.py", "file_name": "amazonwebcrap.py", "file_ext": "py", "file_size_in_byte": 1283, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "requests.get", "line_number": 13, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "259931061", "text": "from hyp3_sdk import HyP3, Batch, Job\nfrom .cmd_parser import composite_options\nfrom datetime import datetime\nfrom .log import logger\n\n\ndef run_rtc_gamma(granules):\n hyp3 = HyP3(username=composite_options.username, password=composite_options.password)\n logger.info(\"preparing gamma jobs\")\n rtc_gamma_jobs = [hyp3.prepare_rtc_job(granule=granule, include_scattering_area=True) for granule in granules]\n logger.info(\"jobs prepared\")\n logger.info(rtc_gamma_jobs)\n rtc_gamma_batch = hyp3.submit_prepared_jobs(rtc_gamma_jobs)\n logger.info(\"jobs submitted\")\n rtc_gamma_batch = hyp3.watch(rtc_gamma_batch)\n logger.info(\"jobs done\")\n location = \"{now}_min:{lon_lat_min}_max:{lon_lat_max}_granules:{granule_count}\".format(\n now=datetime.now().isoformat(),\n lon_lat_min=composite_options.lon_lat_minimum,\n lon_lat_max=composite_options.lon_lat_maximum,\n granule_count=len(granules)\n )\n rtc_gamma_batch.download_files(location=location)\n logger.info(\"jobs downloaded\")\n return location\n", "sub_path": "comp_wrapper/gamma_manager.py", "file_name": "gamma_manager.py", "file_ext": "py", "file_size_in_byte": 1045, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "hyp3_sdk.HyP3", "line_number": 8, "usage_type": "call"}, {"api_name": "cmd_parser.composite_options.username", "line_number": 8, "usage_type": "attribute"}, {"api_name": "cmd_parser.composite_options", "line_number": 8, "usage_type": "name"}, {"api_name": "cmd_parser.composite_options.password", "line_number": 8, "usage_type": "attribute"}, {"api_name": "log.logger.info", "line_number": 9, "usage_type": "call"}, {"api_name": "log.logger", "line_number": 9, "usage_type": "name"}, {"api_name": "log.logger.info", "line_number": 11, "usage_type": "call"}, {"api_name": "log.logger", "line_number": 11, "usage_type": "name"}, {"api_name": "log.logger.info", "line_number": 12, "usage_type": "call"}, {"api_name": "log.logger", "line_number": 12, "usage_type": "name"}, {"api_name": "log.logger.info", "line_number": 14, "usage_type": "call"}, {"api_name": "log.logger", "line_number": 14, "usage_type": "name"}, {"api_name": "log.logger.info", "line_number": 16, "usage_type": "call"}, {"api_name": "log.logger", "line_number": 16, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 18, "usage_type": "name"}, {"api_name": "cmd_parser.composite_options.lon_lat_minimum", "line_number": 19, "usage_type": "attribute"}, {"api_name": "cmd_parser.composite_options", "line_number": 19, "usage_type": "name"}, {"api_name": "cmd_parser.composite_options.lon_lat_maximum", "line_number": 20, "usage_type": "attribute"}, {"api_name": "cmd_parser.composite_options", "line_number": 20, "usage_type": "name"}, {"api_name": "log.logger.info", "line_number": 24, "usage_type": "call"}, {"api_name": "log.logger", "line_number": 24, "usage_type": "name"}]} +{"seq_id": "272298389", "text": "#!/usr/bin/env python\n# -*- coding: utf8 -*-\n\nfrom LOP.Models.model_lop import Model_lop\n\n# Tensorflow\nimport tensorflow as tf\n\n# Keras\nimport keras\nfrom keras.layers import Dense, Dropout, BatchNormalization, GRU\n\n# Hyperopt\nfrom LOP.Utils import hopt_wrapper\nfrom math import log\nfrom hyperopt import hp\n\nfrom LOP.Models.Utils.weight_summary import keras_layer_summary\nfrom LOP.Models.Utils.stacked_rnn import stacked_rnn\n\n\nclass LSTM_affine_PianoCond(Model_lop):\n def __init__(self, model_param, dimensions):\n\n Model_lop.__init__(self, model_param, dimensions)\n\n return\n\n @staticmethod\n def name():\n return \"LSTM_affine_PianoCond\"\n @staticmethod\n def binarize_piano():\n return True\n @staticmethod\n def binarize_orchestra():\n return True\n @staticmethod\n def is_keras():\n return True\n @staticmethod\n def optimize():\n return True\n @staticmethod\n def trainer():\n return \"standard_trainer\"\n @staticmethod\n def get_hp_space():\n super_space = Model_lop.get_hp_space()\n\n space = {}\n\n space.update(super_space)\n return space\n\n def init_weights(self):\n self.weights = {}\n\n self.FilM_dim_0 = 2000\n self.FilM_dim_1 = 2000\n\n self.weights[\"FiLM_generator\"] = [Dense(self.FilM_dim_0*2 + self.FilM_dim_1*2, activation='relu')]\n\n self.weights[\"\"] = stacked_rnn([2000,2000], 'relu', self.dropout_probability)\n\n self.weights[\"first_layer\"] = Dense(2000, activation='relu')\n\n self.weights[\"block_0\"] = [\n Dense(2000, activation='relu'),\n Dense(self.FilM_dim_0, activation='linear'),\n BatchNormalization()\n ]\n\n self.weights[\"block_1\"] = [\n Dense(2000, activation='relu'),\n Dense(self.FilM_dim_1, activation='linear'),\n BatchNormalization()\n ]\n\n self.weights[\"last_layer\"] = Dense(self.orch_dim, activation='softmax')\n\n return\n\n def FiLM_generator(self, x, layers):\n for gru_layer in layers:\n x = gru_layer(x)\n keras_layer_summary(gru_layer, collections=[\"weights\"])\n return x\n\n def residual_FiLM(self, x, gamma, beta, layers):\n original = x\n # Stacked MLPs\n for layer in layers:\n x = layer(x)\n keras_layer_summary(layer, collections=[\"weights\"])\n # FiLM\n x = tf.multiply(gamma, x) + beta\n # Relu\n x = keras.activations.relu(x)\n return x\n\n def predict(self, inputs_ph):\n\n piano_t, _, _, orch_past, _ = inputs_ph\n\n FiLM_Coeff = self.FiLM_generator(orch_past, self.weights['FiLM_generator'])\n\n # Unpack\n ind_start = 0\n ind_end = ind_start+self.FilM_dim_0\n beta_0 = FiLM_Coeff[:, ind_start:ind_end]\n ind_start = ind_end\n ind_end = ind_start+self.FilM_dim_0\n gamma_0 = FiLM_Coeff[:, ind_start:ind_end]\n ind_start = ind_end\n ind_end = ind_start+self.FilM_dim_1\n beta_1 = FiLM_Coeff[:, ind_start:ind_end]\n ind_start = ind_end\n ind_end = ind_start+self.FilM_dim_1\n gamma_1 = FiLM_Coeff[:, ind_start:ind_end]\n\n # Adapt input dimension\n first_layer = self.weights[\"first_layer\"]\n x = first_layer(piano_t)\n keras_layer_summary(first_layer, collections=[\"weights\"])\n\n x = self.residual_FiLM(x, gamma_0, beta_0, self.weights[\"block_0\"])\n\n x = self.residual_FiLM(x, gamma_1, beta_1, self.weights[\"block_1\"])\n\n last_layer = self.weights[\"last_layer\"]\n orch_prediction = last_layer(x)\n keras_layer_summary(last_layer, collections=[\"weights\"])\n\n return orch_prediction, orch_prediction", "sub_path": "Source/LOP/Models/Real_time/Affine/LSTM_affine_PianoCond.py", "file_name": "LSTM_affine_PianoCond.py", "file_ext": "py", "file_size_in_byte": 3731, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "LOP.Models.model_lop.Model_lop", "line_number": 22, "usage_type": "name"}, {"api_name": "LOP.Models.model_lop.Model_lop.__init__", "line_number": 25, "usage_type": "call"}, {"api_name": "LOP.Models.model_lop.Model_lop", "line_number": 25, "usage_type": "name"}, {"api_name": "LOP.Models.model_lop.Model_lop.get_hp_space", "line_number": 49, "usage_type": "call"}, {"api_name": "LOP.Models.model_lop.Model_lop", "line_number": 49, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 62, "usage_type": "call"}, {"api_name": "LOP.Models.Utils.stacked_rnn.stacked_rnn", "line_number": 64, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 66, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 69, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 70, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 71, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 75, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 76, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 77, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 80, "usage_type": "call"}, {"api_name": "LOP.Models.Utils.weight_summary.keras_layer_summary", "line_number": 87, "usage_type": "call"}, {"api_name": "LOP.Models.Utils.weight_summary.keras_layer_summary", "line_number": 95, "usage_type": "call"}, {"api_name": "tensorflow.multiply", "line_number": 97, "usage_type": "call"}, {"api_name": "keras.activations.relu", "line_number": 99, "usage_type": "call"}, {"api_name": "keras.activations", "line_number": 99, "usage_type": "attribute"}, {"api_name": "LOP.Models.Utils.weight_summary.keras_layer_summary", "line_number": 125, "usage_type": "call"}, {"api_name": "LOP.Models.Utils.weight_summary.keras_layer_summary", "line_number": 133, "usage_type": "call"}]} +{"seq_id": "13572104", "text": "\"\"\"\ni2c Experiment Runner\n\"\"\"\n\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import absolute_import\n\nimport os\nimport sys\nimport argparse\nimport importlib\nimport logging\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom progress.bar import Bar\nfrom shutil import copyfile\n\nDIR_NAME = os.path.dirname(__file__)\n\ntry:\n import pi2c\nexcept ImportError:\n print(\"pi2c not installed, using local version\")\n top_path = os.path.join(DIR_NAME, '..')\n sys.path.append(os.path.abspath(top_path))\n\nfrom pi2c.i2c import I2cGraph\nfrom pi2c.env import make_env\nfrom pi2c.model import make_env_model\nfrom pi2c.policy.linear import TimeIndexedLinearGaussianPolicy\nfrom pi2c.utils import converged_list, TrajectoryData, profile, make_results_folder, configure_plots, TrajectoryEvaluator\n\nPROFILING = False\n\ndef run(experiment, res_dir, weight_path):\n\n env = make_env(experiment)\n model = make_env_model(experiment.ENVIRONMENT,\n experiment.MODEL)\n i2c = I2cGraph(\n model,\n experiment.N_DURATION,\n experiment.INFERENCE.Q,\n experiment.INFERENCE.R,\n experiment.INFERENCE.ALPHA,\n experiment.INFERENCE.alpha_update_tol,\n experiment.INFERENCE.SIG_U,\n experiment.INFERENCE.msg_iter,\n experiment.INFERENCE.msg_tol,\n experiment.INFERENCE.em_tol,\n experiment.INFERENCE.backwards_contraction,\n res_dir)\n\n if weight_path is not None:\n print(\"Loading i2c model with {}\".format(weight_path))\n i2c.sys.model.load(weight_path)\n\n policy = TimeIndexedLinearGaussianPolicy(experiment.POLICY_COVAR,\n experiment.N_DURATION, i2c.sys.dim_u, i2c.sys.dim_x)\n\n # collection of data\n traj_data = TrajectoryData(\n model.x_noise, model.y_noise, experiment.N_AUG)\n s_est = np.zeros((experiment.N_DURATION, i2c.sys.dim_xt))\n traj_eval = TrajectoryEvaluator(experiment.N_DURATION, i2c.QR, i2c.sg)\n traj_eval_iter = TrajectoryEvaluator(experiment.N_DURATION, i2c.QR, i2c.sg)\n\n # get stationary data for initial training data\n for _ in range(experiment.N_STARTING):\n x, y, _ = env.run(policy)\n traj_data.add(x, y)\n\n # MBRL training loop\n for j in range(experiment.N_EPISODE):\n print(\"Ep. {}\".format(j))\n\n # run policy on env, get data\n with profile(\"Simulation\", PROFILING):\n x, y, _ = env.run(policy)\n x_test, y_test, _ = env.run(policy)\n env.plot_sim(x, s_est, \"training {}\".format(j), res_dir)\n\n # fit model\n x_train, y_train = traj_data.add(x, y)\n\n # inference\n bar = Bar('Learning', max=experiment.N_INFERENCE)\n with profile(\"Inference\", PROFILING):\n i2c.reset_metrics()\n for i in range(experiment.N_INFERENCE):\n i2c.learn_msgs()\n # eval policy\n policy.K, policy.k, _ = i2c.get_local_linear_policy()\n policy.sigk = np.zeros(policy.sigk.shape)\n x, y, z = env.run(policy)\n z_est = i2c.get_marginal_observed_trajectory()\n traj_eval_iter.eval(z, z_est)\n\n if i % experiment.N_ITERS_PER_PLOT == 0: #\n i2c.plot_traj(i, dir_name=res_dir, filename=\"iter_{}_{}\".format(j, i))\n i2c.plot_observed_traj(dir_name=res_dir, filename=\"iter_{}_{}\".format(j, i))\n i2c.plot_system_dynamics(dir_name=res_dir, filename=\"iter_{}_{}\".format(j, i))\n i2c.plot_uncertainty(dir_name=res_dir, filename=\"iter_{}_{}\".format(j, i))\n i2c.plot_controller(dir_name=res_dir, filename=\"{}_{}\".format(j, i))\n i2c.plot_cost(res_dir, j)\n i2c.plot_alphas(dir_name=res_dir, filename=j)\n i2c.plot_policy_entropy(dir_name=res_dir, filename=j)\n policy.K, policy.k, policy.sigk = i2c.get_local_linear_policy()\n s_est = i2c.get_marginal_trajectory()\n env.plot_sim(x, s_est, \"{} {}\".format(j, i), res_dir)\n i2c.plot_gap(res_dir, j)\n i2c.plot_em_cost(res_dir, j)\n traj_eval_iter.plot(\"over_iterations_{}\".format(j), res_dir)\n i2c.save(res_dir, \"{}_{}\".format(j, i))\n bar.next()\n bar.finish()\n\n i2c.plot_traj(i, dir_name=res_dir, filename=\"iter_{}_{}\".format(j, i))\n i2c.plot_cost_all(res_dir, j)\n i2c.plot_uncertainty(dir_name=res_dir, filename=j)\n i2c.plot_observed_traj(dir_name=res_dir, filename=\"iter_{}_{}\".format(j, i))\n i2c.plot_controller(dir_name=res_dir, filename=j)\n\n # update policy\n policy.K, policy.k, policy.sigk = i2c.get_local_linear_policy()\n z_est = i2c.get_marginal_observed_trajectory()\n x, y, z = env.run(policy)\n s_est = i2c.get_marginal_trajectory()\n env.plot_sim(x, s_est, \"evaluation {}\".format(j), res_dir)\n traj_eval.eval(z, z_est)\n traj_eval.plot(\"over_episodes\", res_dir)\n traj_eval_iter.plot(\"over_iterations_{}\".format(j), res_dir)\n\n i2c.plot_alphas(res_dir, \"final\")\n i2c.plot_cost(res_dir, \"cost_final\")\n\n x_final, _, _ = env.run(policy)\n s_est = i2c.get_marginal_trajectory()\n env.plot_sim(x_final, s_est, \"Final\", res_dir)\n\n # compare against pure feedforward\n policy.zero()\n policy.k = i2c.get_marginal_input()\n x_ff, _, _ = env.run(policy)\n env.plot_sim(x_ff, s_est, \"Final Feedforward\", res_dir)\n\n # save model and data\n i2c.sys.save(res_dir)\n save_trajectories(x_final, i2c, res_dir)\n traj_eval.save(\"episodic\", res_dir)\n traj_eval_iter.save(\"iter\", res_dir)\n i2c.save(res_dir, \"{}_{}\".format(j, i))\n\n env.close()\n\n\ndef save_trajectories(s, inference, res_dir):\n if res_dir:\n x_real = s[:, :inference.sys.dim_x]\n u_real = s[:, inference.sys.dim_x:]\n np.save(os.path.join(res_dir, \"xu_real.npy\"), s)\n np.save(os.path.join(res_dir, \"x_real.npy\"), x_real)\n np.save(os.path.join(res_dir, \"u_real.npy\"), u_real)\n inference.save_traj(res_dir)\n\nif __name__ == \"__main__\":\n import git\n from glob import glob\n from os.path import basename, join, splitext\n\n def write_commit(res_dir):\n repo = git.Repo(search_parent_directories=True)\n sha = repo.head.object.hexsha\n with open(os.path.join(res_dir, \"git_commit.txt\"), 'w+') as f:\n f.write(sha)\n\n configure_plots()\n\n exps = [splitext(basename(e))[0] for e in\n glob(os.path.join(DIR_NAME, \"experiments\", \"*.py\"))\n if \"__init__\" not in e]\n\n parser = argparse.ArgumentParser(description=\"Run Experiment\")\n parser.add_argument(\"config\",\n help=\"file with hyperparameters\",\n choices=exps)\n parser.add_argument(\"-n\", \"--name\", help=\"folder\", default=\"\")\n parser.add_argument(\"-w\", \"--weights\", help=\"path to model weights\")\n parser.add_argument(\"-r\", \"--random-seed\", help=\"random seed\", default=0)\n args = parser.parse_args()\n\n np.random.seed(args.random_seed)\n\n experiment = importlib.import_module(\n \"experiments.{}\".format(args.config))\n exp_filename = \"{}.py\".format(args.config)\n exp_file = os.path.join(os.path.join(DIR_NAME,\n os.path.join(\"experiments\", exp_filename)))\n res_dir = make_results_folder(args.config, args.random_seed, args.name)\n # copy experiment config and git commit to results\n copyfile(exp_file, os.path.join(res_dir, exp_filename))\n write_commit(res_dir)\n\n run(experiment, res_dir, args.weights)\n plt.show()\n", "sub_path": "scripts/run.py", "file_name": "run.py", "file_ext": "py", "file_size_in_byte": 7678, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "os.path.dirname", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 26, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pi2c.env.make_env", "line_number": 38, "usage_type": "call"}, {"api_name": "pi2c.model.make_env_model", "line_number": 39, "usage_type": "call"}, {"api_name": "pi2c.i2c.I2cGraph", "line_number": 41, "usage_type": "call"}, {"api_name": "pi2c.policy.linear.TimeIndexedLinearGaussianPolicy", "line_number": 59, "usage_type": "call"}, {"api_name": "pi2c.utils.TrajectoryData", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 65, "usage_type": "call"}, {"api_name": "pi2c.utils.TrajectoryEvaluator", "line_number": 66, "usage_type": "call"}, {"api_name": "pi2c.utils.TrajectoryEvaluator", "line_number": 67, "usage_type": "call"}, {"api_name": "pi2c.utils.profile", "line_number": 79, "usage_type": "call"}, {"api_name": "progress.bar.Bar", "line_number": 88, "usage_type": "call"}, {"api_name": "pi2c.utils.profile", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 162, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 162, "usage_type": "call"}, {"api_name": "os.path", "line_number": 162, "usage_type": "attribute"}, {"api_name": "numpy.save", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path", "line_number": 163, "usage_type": "attribute"}, {"api_name": "numpy.save", "line_number": 164, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 164, "usage_type": "call"}, {"api_name": "os.path", "line_number": 164, "usage_type": "attribute"}, {"api_name": "git.Repo", "line_number": 173, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 175, "usage_type": "call"}, {"api_name": "os.path", "line_number": 175, "usage_type": "attribute"}, {"api_name": "pi2c.utils.configure_plots", "line_number": 178, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 180, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 180, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 181, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 181, "usage_type": "call"}, {"api_name": "os.path", "line_number": 181, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 193, "usage_type": "attribute"}, {"api_name": "importlib.import_module", "line_number": 195, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 198, "usage_type": "call"}, {"api_name": "os.path", "line_number": 198, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 199, "usage_type": "call"}, {"api_name": "os.path", "line_number": 199, "usage_type": "attribute"}, {"api_name": "pi2c.utils.make_results_folder", "line_number": 200, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 202, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 202, "usage_type": "call"}, {"api_name": "os.path", "line_number": 202, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 206, "usage_type": "name"}]} +{"seq_id": "466314139", "text": "import pygame\nfrom Physics.Particle import Particle\nfrom Physics.Vector import Vector\nfrom Physics.CoordinateSystem import CoordinateSystem\nfrom Physics.Force import Force, CREATEFORCE, NONE, CONSTANTFORCE, EARTHGRAVITY, SQUARELAW, CUBELAW, GRAVITATIONALFORCE, ELECTRICFORCE\n\nclass Electron(Particle):\n def __init__(self, coordinateSystem = None, position_vector = Vector(2,[0,0]), velocity = Vector(2,[0,0])):\n if(coordinateSystem is None):\n self.coordinateSystem = CoordinateSystem([pygame.display.get_surface().get_size()[0] / 2, pygame.display.get_surface().get_size()[1] / 2], 100,\"PPM\", pygame.display.get_surface().get_size())\n else:\n self.coordinateSystem = coordinateSystem\n self.timer = 0\n self.pathPoints = []\n self.position_vector = position_vector\n self.velocity = velocity\n self.mass = float(9.1093837015e-31)\n self.charge = (-1) * float(1.60217662e-19)\n self.netForce = Vector(2, [0, 0])\n self.acceleration = self.netForce * (1/self.mass)\n pygame.sprite.Sprite.__init__(self)\n self.image = pygame.image.load(\"Electron.png\")\n self.center_pixel_displacement_vector = Vector(2,self.image.get_rect().topleft) - Vector(2, self.image.get_rect().center)", "sub_path": "Physics/Electron.py", "file_name": "Electron.py", "file_ext": "py", "file_size_in_byte": 1279, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "Physics.Particle.Particle", "line_number": 7, "usage_type": "name"}, {"api_name": "Physics.Vector.Vector", "line_number": 8, "usage_type": "call"}, {"api_name": "Physics.CoordinateSystem.CoordinateSystem", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.display.get_surface", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 10, "usage_type": "attribute"}, {"api_name": "Physics.Vector.Vector", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.sprite.Sprite.__init__", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 22, "usage_type": "attribute"}, {"api_name": "Physics.Vector.Vector", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "248578420", "text": "# import necessary libraries\r\nimport numpy as np\r\nimport datetime\r\nimport sqlalchemy\r\nimport json\r\nfrom sqlalchemy.ext.automap import automap_base\r\nfrom sqlalchemy.orm import Session\r\nfrom sqlalchemy import create_engine, func, Column, String\r\nfrom sqlalchemy.ext.declarative import declarative_base\r\nfrom flask import Flask, jsonify, request, render_template\r\n\r\nengine = create_engine(\"postgresql+psycopg2://postgres:postgres@localhost:5432/malaria_db\")\r\n\r\n# reflect an existing database into a new model\r\nBase = automap_base()\r\n\r\nBase.prepare(engine, reflect=True)\r\n\r\n# references to each table\r\nTable_000817 = Base.classes.table_000817\r\n\r\n# create instance of Flask app\r\napp = Flask(__name__)\r\n\r\n# create route that renders index.html template\r\n@app.route(\"/index\")\r\ndef index():\r\n return render_template(\"index.html\")\r\n\r\n# create route that renders index.html template\r\n@app.route(\"/main\")\r\ndef main():\r\n return render_template(\"main.html\")\r\n\r\n@app.route(\"/main2\")\r\ndef main2():\r\n return render_template(\"main2.html\")\r\n\r\n# create route that renders index.html template\r\n@app.route(\"/plots\")\r\ndef plots():\r\n return render_template(\"plots.html\")\r\n\r\n#create a route for the data\r\n@app.route(\"/api\")\r\ndef api():\r\n # Create our session (link) from Python to the DB\r\n session = Session(engine)\r\n\r\n \"\"\"Return a list of all results\"\"\"\r\n # Query all malaria\r\n results = session.query(\r\n Table_000817.country,\r\n Table_000817.code,\r\n Table_000817.year,\r\n Table_000817.under_5,\r\n Table_000817.age_5_14,\r\n Table_000817.age_15_49,\r\n Table_000817.age_50_69,\r\n Table_000817.over_70,\r\n Table_000817.total_deaths,\r\n Table_000817.population,\r\n Table_000817.perc_of_pop_w_malaria,\r\n Table_000817.gdp_per_capita).all()\r\n\r\n session.close()\r\n\r\n # Create a dictionary from the row data and append to a list\r\n malaria_db = []\r\n for country, code, year, under_5, age_5_14, age_15_49, age_50_69, over_70, total_deaths, population, perc_of_pop_w_malaria, gdp_per_capita in results:\r\n malaria_dict = {}\r\n malaria_dict[\"country\"] = country\r\n malaria_dict[\"code\"] = code\r\n malaria_dict[\"year\"] = year\r\n malaria_dict[\"under_5\"] = under_5\r\n malaria_dict[\"age_5_14\"] = age_5_14\r\n malaria_dict[\"age_15_49\"] = age_15_49\r\n malaria_dict[\"age_50_69\"] = age_50_69\r\n malaria_dict[\"over_70\"] = over_70\r\n malaria_dict[\"total_deaths\"] = total_deaths\r\n malaria_dict[\"population\"] = population\r\n malaria_dict[\"perc_of_pop_w_malaria\"] = perc_of_pop_w_malaria\r\n malaria_dict[\"gdp_per_capita\"] = gdp_per_capita\r\n malaria_db.append(malaria_dict)\r\n return jsonify(malaria_db)\r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n app.run(debug=True)\r\n", "sub_path": "CleanedFinal/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 2813, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "sqlalchemy.create_engine", "line_number": 12, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.automap.automap_base", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 37, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 42, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 85, "usage_type": "call"}]} +{"seq_id": "371066562", "text": "import os\nfrom deepdiff import DeepDiff\nfrom django.core.management.base import BaseCommand, CommandError\nfrom file_system.repository.file_repository import FileRepository\nfrom django.conf import settings\n\n\nclass Command(BaseCommand):\n help = \"Create diff files for updated files\"\n\n def handle(self, *args, **options):\n jira_directories = os.listdir(settings.NOTIFIER_STORAGE_DIR)\n for dir in jira_directories:\n print(dir)\n files = os.listdir(os.path.join(settings.NOTIFIER_STORAGE_DIR, dir))\n for f in files:\n self._create_metadata_versioned_files(os.path.join(settings.NOTIFIER_STORAGE_DIR, dir, f))\n\n def _create_metadata_versioned_files(self, filepath):\n print(filepath)\n filename = os.path.basename(filepath)\n print(filename)\n filename = filename.replace(\"_metadata_update.json\", \"\")\n f = FileRepository.filter(file_name=filename).first()\n print(f)\n metadata = f.file.filemetadata_set.order_by(\"-created_date\").all()\n print(len(metadata))\n for i in range(len(metadata) - 1):\n ddiff = DeepDiff(metadata[i + 1].metadata, metadata[i].metadata, ignore_order=True)\n print(ddiff)\n diff_file_name = \"%s_metadata_update_%s.json\" % (f.file.file_name, metadata[i].version)\n new_file = os.path.join(os.path.dirname(filepath), diff_file_name)\n print(\"New file name\")\n print(new_file)\n with open(new_file, \"w\") as fh:\n fh.write(str(ddiff))\n os.remove(filepath)\n", "sub_path": "file_system/management/commands/version_file_metadata.py", "file_name": "version_file_metadata.py", "file_ext": "py", "file_size_in_byte": 1586, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.core.management.base.BaseCommand", "line_number": 8, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.settings.NOTIFIER_STORAGE_DIR", "line_number": 12, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 12, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.conf.settings.NOTIFIER_STORAGE_DIR", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 15, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "django.conf.settings.NOTIFIER_STORAGE_DIR", "line_number": 17, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 17, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "file_system.repository.file_repository.FileRepository.filter", "line_number": 24, "usage_type": "call"}, {"api_name": "file_system.repository.file_repository.FileRepository", "line_number": 24, "usage_type": "name"}, {"api_name": "deepdiff.DeepDiff", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 32, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "422792990", "text": "import pygame\nfrom pygame.locals import *\nimport sys\n\nfrom Pong import Paddle\nfrom Pong import Ball\n\nDEBUG_PAUSE = False\n\nHEIGHT = 500\nWIDTH = 500\nBLACK = (0, 0, 0)\n\nPLAYER1_SCORE = 0\nPLAYER2_SCORE = 0\n\n\ndef updatePaddles(event, paddleL, paddleR, canMoveAble):\n if event.key == pygame.K_w:\n paddleL.setMoveUpAble(canMoveAble)\n elif event.key == pygame.K_s:\n paddleL.setMoveDownAble(canMoveAble)\n\n elif event.key == pygame.K_UP:\n paddleR.setMoveUpAble(canMoveAble)\n elif event.key == pygame.K_DOWN:\n paddleR.setMoveDownAble(canMoveAble)\n\n\ndef updatePos(paddleL, paddleR, ball):\n paddleL.moveUp()\n paddleL.moveDown()\n paddleR.moveUp()\n paddleR.moveDown()\n\n ball.moveX()\n ball.moveY()\n\n if ball.lessThan() or ball.greaterThan():\n if ball.lessThan():\n global PLAYER2_SCORE\n PLAYER2_SCORE += 1\n else:\n global PLAYER1_SCORE\n PLAYER1_SCORE += 1\n # point goes to player 2\n\n\n ball.moveOtherWayDX()\n ball.setX(100)\n\n ball.setY(50)\n ball.setDX(10)\n ball.setDY(0)\n\n # collison\n w, h = ball.getSize()\n ballRect = pygame.Rect(ball.getX(), ball.getY(), w, h)\n paddleRRect = pygame.Rect(paddleR.getX(), paddleR.getY(), paddleR.getWidth(), paddleR.getHeight())\n paddleLRect = pygame.Rect(paddleL.getX(), paddleL.getY(), paddleL.getWidth(), paddleL.getHeight())\n\n if ballRect.colliderect(paddleRRect):\n ball.moveOtherWayDX()\n ball.moveOtherWayDY()\n\n if paddleR.isMoveUpAble():\n ball.setDY(-1)\n else:\n ball.setDY(1)\n\n if ballRect.colliderect(paddleLRect):\n ball.moveOtherWayDX()\n ball.moveOtherWayDY()\n\n if paddleL.isMoveUpAble():\n ball.setDY(-1)\n else:\n ball.setDY(1)\n\n\ndef main():\n pygame.init()\n\n screen = pygame.display.set_mode((640, 480), pygame.FULLSCREEN)\n screen.fill(BLACK)\n\n width, height = screen.get_size()\n myfont = pygame.font.SysFont(\"Times\", 15)\n\n paddleL = Paddle(screen, 10, 0) # (x, y)\n paddleR = Paddle(screen, width - 20, 0)\n ball = Ball(screen)\n\n w, h = screen.get_size()\n\n while True:\n screen.fill(BLACK)\n paddleL.drawPaddle()\n paddleR.drawPaddle()\n ball.drawBall()\n label = myfont.render(str(PLAYER1_SCORE) + \"|\" + str(PLAYER2_SCORE), 1, (255, 255, 255))\n screen.blit(label, (h / 2, 10))\n\n for event in pygame.event.get():\n if event.type == QUIT:\n pygame.quit()\n sys.exit(0)\n elif event.type == pygame.KEYDOWN: # if keydown, let it move\n if event.key == pygame.K_ESCAPE:\n screen = pygame.display.set_mode((WIDTH, HEIGHT), 0, 32)\n width, height = screen.get_size()\n paddleL = Paddle(screen, 10, 0) # (x, y)\n paddleR = Paddle(screen, width - 20, 0)\n\n updatePaddles(event, paddleL, paddleR, True)\n elif event.type == pygame.KEYUP:\n updatePaddles(event, paddleL, paddleR, False)\n\n updatePos(paddleL, paddleR, ball)\n\n if not DEBUG_PAUSE:\n pygame.display.flip()\n\n\n\n # pygame.display.update() # update screen\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3334, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pygame.K_w", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pygame.K_s", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pygame.K_DOWN", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 58, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 59, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 60, "usage_type": "call"}, {"api_name": "pygame.init", "line_number": 82, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 84, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 84, "usage_type": "attribute"}, {"api_name": "pygame.FULLSCREEN", "line_number": 84, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 88, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 88, "usage_type": "attribute"}, {"api_name": "Pong.Paddle", "line_number": 90, "usage_type": "call"}, {"api_name": "Pong.Paddle", "line_number": 91, "usage_type": "call"}, {"api_name": "Pong.Ball", "line_number": 92, "usage_type": "call"}, {"api_name": "pygame.event.get", "line_number": 104, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 104, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 106, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 107, "usage_type": "call"}, {"api_name": "pygame.KEYDOWN", "line_number": 108, "usage_type": "attribute"}, {"api_name": "pygame.K_ESCAPE", "line_number": 109, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 110, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 110, "usage_type": "attribute"}, {"api_name": "Pong.Paddle", "line_number": 112, "usage_type": "call"}, {"api_name": "Pong.Paddle", "line_number": 113, "usage_type": "call"}, {"api_name": "pygame.KEYUP", "line_number": 116, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 122, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 122, "usage_type": "attribute"}]} +{"seq_id": "119301686", "text": "\nfrom pathlib import Path\n\n\n# Some path utilities\ndef ls(path):\n return [p for p in path.iterdir()]\n\n\ndef find_files(path, pattern=None):\n \"\"\" Recursively find all files \"\"\"\n all_files = list()\n if path.is_dir():\n all_files.extend([f for p in ls(path) for f in find_files(p)])\n else:\n all_files.append(path)\n if pattern is not None:\n all_files = [f for f in all_files if f.match(pattern)]\n return all_files\n\n\n# OK, let's do some duck-typing\nPath.ls = ls\nPath.find_files = find_files\n", "sub_path": "mlgen/tcga/util.py", "file_name": "util.py", "file_ext": "py", "file_size_in_byte": 525, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "pathlib.Path.ls", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 23, "usage_type": "name"}, {"api_name": "pathlib.Path.find_files", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 24, "usage_type": "name"}]} +{"seq_id": "486288503", "text": "# -*- coding: utf-8 -*-\n\"\"\"This file is a EmploiSoignant spider created on top of the ATSSpider\nscrapy crawl emploisoignant -a mining_job_id=9999 -a iteration=1 -a extract=1 -a url=\"http://www.emploisoignant.com/recherche-offres\"\n\nsample url:\n http://www.emploisoignant.com/recherche-offres\n\"\"\"\n\nfrom urlparse import urljoin\nfrom re import compile\n\nfrom scrapy.http import Request\nfrom scrapy.selector import Selector\n\nfrom brightcorp.base.atsspiders import ATSSpider\nfrom brightcorp.items import BrightcorpItemLoader\nfrom brightcorp.processors import Prefix, ConvertDateString, Replace\n\n\nclass EmploiSoignant(ATSSpider):\n\n name = 'emploisoignant'\n zip_code_re = compile(r\"\\/ (\\d+)\")\n replace_re = compile(r\"([(\\d+)])\")\n ref_re = compile(r\"detail\\/(\\d+)\")\n\n def parse(self, response):\n sel = Selector(response)\n if not self.expected_job_count_set:\n count = sel.xpath(\n '//p[@class=\"results-job-search\"]/text()'\n ).extract()\n if count:\n self.expected_job_count = count\n\n jobs = sel.xpath('//section[@class=\"col-center\"]/article')\n for job in jobs:\n job_url = job.xpath('./a/@href').extract()\n if job_url:\n job_url = urljoin(response.url, job_url[0])\n req = Request(job_url, callback=self.parse_job_callback())\n req.meta['title'] = job.xpath('.//h2[@itemprop=\"title\"]/text()').extract()\n req.meta['comp'] = job.xpath('.//h3[@itemprop=\"hiringOrganization\"]/span/text()').extract()\n req.meta['logo'] = job.xpath('./a/img/@src').extract()\n req.meta['loc'] = job.xpath('.//li[@itemprop=\"jobLocation\"]//text()').extract()\n req.meta['type'] = job.xpath('.//li[@itemprop=\"employmentType\"]//text()').extract()\n req.meta['date'] = job.xpath('.//li[@itemprop=\"datePosted\"]/text()').extract()\n yield req\n\n next_url = sel.xpath('//li[@class=\"next\"]/a/@href').extract()\n if next_url:\n next_url = urljoin(response.url, next_url[0])\n yield Request(next_url, callback=self.parse)\n\n def parse_job(self, response):\n loader = BrightcorpItemLoader(response=response)\n loader.add_value('url', response.url)\n loader.add_value('title', response.meta.get('title'))\n loader.add_value('company', response.meta.get('comp'))\n loader.add_value('logo_url', response.meta.get('logo'))\n loader.add_value('jobtype', response.meta.get('type'))\n loader.add_value(\n 'date', response.meta.get('date'), ConvertDateString('%d/%m/%Y')\n )\n loader.add_value(\n 'location', response.meta.get('loc'),\n Replace(self.zip_code_re), Replace('\\/'), Replace(self.replace_re)\n )\n loader.add_value(\n 'zip_code', response.meta.get('loc'), re=self.zip_code_re\n )\n loader.add_value(\n 'referencenumber', response.url,\n Prefix('%s-' % self.name), re=self.ref_re\n )\n loader.add_xpath('description', '//div[@itemprop=\"description\"]')\n yield loader.load_item()\n", "sub_path": "brightcorp/brightcorp/spiders/emploisoignant.py", "file_name": "emploisoignant.py", "file_ext": "py", "file_size_in_byte": 3201, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "brightcorp.base.atsspiders.ATSSpider", "line_number": 20, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 23, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 24, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 25, "usage_type": "call"}, {"api_name": "scrapy.selector.Selector", "line_number": 28, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 40, "usage_type": "call"}, {"api_name": "scrapy.http.Request", "line_number": 41, "usage_type": "call"}, {"api_name": "urlparse.urljoin", "line_number": 52, "usage_type": "call"}, {"api_name": "scrapy.http.Request", "line_number": 53, "usage_type": "call"}, {"api_name": "brightcorp.items.BrightcorpItemLoader", "line_number": 56, "usage_type": "call"}, {"api_name": "brightcorp.processors.ConvertDateString", "line_number": 63, "usage_type": "call"}, {"api_name": "brightcorp.processors.Replace", "line_number": 67, "usage_type": "call"}, {"api_name": "brightcorp.processors.Prefix", "line_number": 74, "usage_type": "call"}]} +{"seq_id": "597646249", "text": "import json\nfrom time import sleep\nimport pytest\nimport requests\nfrom httptest_helper import url, creatData\nfrom other import clear\n\n\ndef test_clear(url):\n clear()\n test_data = creatData(url)\n boyu_dict = test_data.register_boyu()\n channel_1 = test_data.creat_channel(boyu_dict['token'], 'team_1', True)\n resp_message = requests.post(url + 'message/send', json={\n 'token': boyu_dict['token'],\n 'channel_id': channel_1['channel_id'],\n 'message': \"hello1\"\n })\n json.loads(resp_message.text)\n #claer\n resp_clear = requests.delete(url + 'clear', params={})\n users_dict = json.loads(resp_clear.text)\n # test user data\n test_data = creatData(url)\n boyu_dict = test_data.register_boyu()\n wenyao_dict = test_data.register_wenyao()\n channel_1 = test_data.creat_channel(boyu_dict['token'], 'team_1', True)\n # add one message to 1 channel\n resp_message1 = requests.post(url + 'message/send', json={\n 'token': boyu_dict['token'],\n 'channel_id': channel_1['channel_id'],\n 'message': \"hello!!!\"\n })\n id_1 = json.loads(resp_message1.text)\n # check details in users_dict\n resp_users = requests.get(url + 'users/all', params={'token': boyu_dict['token']})\n users_dict = json.loads(resp_users.text)\n assert len(users_dict['users']) == 2\n assert len(users_dict['users'][0].keys()) == 6\n assert users_dict['users'][0]['u_id'] == boyu_dict['u_id']\n assert users_dict['users'][0]['email'] == '123@gmail.com'\n assert users_dict['users'][0]['name_first'] == 'Boyu'\n assert users_dict['users'][0]['name_last'] == 'Cai'\n assert len(users_dict['users'][0]['handle_str']) <= 20\n #test channels data\n requests.post(url + 'channels/create', json={\n 'token': boyu_dict['token'],\n 'name': 'name1',\n 'is_public': False\n })\n channels_listall_re_1 = requests.get(url + 'channels/listall', params={\n 'token': wenyao_dict['token']\n })\n listall_dict_1 = json.loads(channels_listall_re_1.text)\n channels_listall_re_2 = requests.get(url + 'channels/listall', params={\n 'token': boyu_dict['token']\n })\n listall_dict_2 = json.loads(channels_listall_re_2.text)\n assert listall_dict_1 == listall_dict_2\n #test messages data\n resp_search = requests.get(url + 'search', params={\n 'token': boyu_dict['token'],\n 'query_str': \"hello\"\n })\n search_result = json.loads(resp_search.text)\n assert len(search_result['messages']) == 1\n assert search_result['messages'][0][\"message\"] == 'hello!!!'\n assert search_result['messages'][0][\"u_id\"] == boyu_dict['u_id']\n assert search_result['messages'][0][\"message_id\"] == id_1['message_id']\n\n\n# test invalid token in users all\ndef test_users_all_token_invalid(url):\n clear()\n test_data = creatData(url)\n test_data.register_user('123@gmail.com', 'boyupass', 'Boyu',\n 'Caiiiiiiiiiiiiiiiii')\n with pytest.raises(requests.exceptions.HTTPError):\n requests.get(url + 'users/all', params={\"token\": \"invalid.token\"}).raise_for_status()\n\n\n# test the data stored for users_all function\ndef test_users_all(url):\n clear()\n test_data = creatData(url)\n boyu1_dict = test_data.register_user('123@gmail.com', 'boyupass', 'Boyu',\n 'Caiiiiiiiiiiiiiiiii')\n boyu2_dict = test_data.register_user('isaac@gmail.com', 'boyupass', 'Boyu',\n 'Caiiiiiiiiiiiiiiiii')\n resp_users = requests.get(url + 'users/all', params={'token': boyu1_dict['token']})\n users_dict = json.loads(resp_users.text)\n # check details in users_dict\n assert len(users_dict['users']) == 2\n assert len(users_dict['users'][0].keys()) == 6\n assert len(users_dict['users'][1].keys()) == 6\n assert users_dict['users'][0]['u_id'] == boyu1_dict['u_id']\n assert users_dict['users'][0]['email'] == '123@gmail.com'\n assert users_dict['users'][0]['name_first'] == 'Boyu'\n assert users_dict['users'][0]['name_last'] == 'Caiiiiiiiiiiiiiiiii'\n assert len(users_dict['users'][0]['handle_str']) <= 20\n assert users_dict['users'][1]['u_id'] == boyu2_dict['u_id']\n assert users_dict['users'][1]['email'] == 'isaac@gmail.com'\n assert users_dict['users'][1]['name_first'] == 'Boyu'\n assert users_dict['users'][1]['name_last'] == 'Caiiiiiiiiiiiiiiiii'\n assert len(users_dict['users'][1]['handle_str']) <= 20\n assert users_dict['users'][1]['handle_str'] != users_dict['users'][0]['handle_str']\n\n\n# test user is not member\ndef test_admin_userpermission_change_not_member(url):\n clear()\n test_data = creatData(url)\n boyu_dict = test_data.register_boyu()\n with pytest.raises(requests.exceptions.HTTPError):\n requests.post(url + 'admin/userpermission/change', json={\n 'token': boyu_dict['token'],\n 'u_id': -1,\n 'permission_id': 1\n }).raise_for_status()\n\n\n# test permission is wrong\ndef test_admin_userpermission_invalid_permission_id(url):\n clear()\n test_data = creatData(url)\n boyu_dict = test_data.register_boyu()\n with pytest.raises(requests.exceptions.HTTPError):\n requests.post(url + 'admin/userpermission/change', json={\n 'token': boyu_dict['token'],\n 'u_id': boyu_dict['u_id'],\n 'permission_id': 1.5\n }).raise_for_status()\n\n\n# test admin is not owner\ndef test_admin_userpermission_not_owner(url):\n clear()\n test_data = creatData(url)\n test_data.register_boyu()\n wenyao_dict = test_data.register_wenyao()\n with pytest.raises(requests.exceptions.HTTPError):\n requests.post(url + 'admin/userpermission/change', json={\n 'token': wenyao_dict['token'],\n 'u_id': wenyao_dict['u_id'],\n 'permission_id': 2\n }).raise_for_status()\n\n\n# test admin_userpermission_change\ndef test_admin_userpermission_change(url):\n clear()\n test_data = creatData(url)\n boyu_dict = test_data.register_boyu()\n wenyao_dict = test_data.register_wenyao()\n channel_dict = test_data.creat_channel(boyu_dict['token'], 'team_1', True)\n # invite a new user to the channel\n resp_channel_invite = requests.post(url + 'channel/invite', json={\n 'token': boyu_dict['token'],\n 'channel_id': channel_dict['channel_id'],\n 'u_id': wenyao_dict['u_id']\n })\n channel_invite = json.loads(resp_channel_invite.text)\n assert channel_invite == {}\n # add that user as flockr owner\n resp_users1 = requests.post(url + 'admin/userpermission/change', json={\n 'token': boyu_dict['token'],\n 'u_id': wenyao_dict['u_id'],\n 'permission_id': 1\n })\n user_dict1 = json.loads(resp_users1.text)\n assert user_dict1 == {}\n # check is the new added flockr owner has the channel owner's premission\n resp_channel_removeowner = requests.post(url + 'channel/removeowner', json={\n 'token': wenyao_dict['token'],\n 'channel_id': channel_dict['channel_id'],\n 'u_id': boyu_dict['u_id']\n })\n channel_removeowner = json.loads(resp_channel_removeowner.text)\n assert channel_removeowner == {}\n # check if the channel_details are correct\n resp_channel_details = requests.get(url + 'channel/details', params={\n 'token': wenyao_dict['token'],\n 'channel_id': channel_dict['channel_id']\n })\n channel_details = json.loads(resp_channel_details.text)\n assert channel_details == {\n 'name': 'team_1',\n 'owner_members': [\n {\n 'u_id': wenyao_dict['u_id'],\n 'name_first': 'Wenyao',\n 'name_last': 'Chen',\n 'profile_img_url': '',\n }\n ],\n 'all_members': [\n {\n 'u_id': boyu_dict['u_id'],\n 'name_first': 'Boyu',\n 'name_last': 'Cai',\n 'profile_img_url': '',\n },\n {\n 'u_id': wenyao_dict['u_id'],\n 'name_first': 'Wenyao',\n 'name_last': 'Chen',\n 'profile_img_url': '',\n }\n ],\n }\n\n\n# test the search message from the same channel\ndef test_search_message(url):\n clear()\n test_data = creatData(url)\n boyu_dict = test_data.register_boyu()\n channel_1 = test_data.creat_channel(boyu_dict['token'], 'team_1', True)\n # add 3 messages to 1 channel\n resp_message1 = requests.post(url + 'message/send', json={\n 'token': boyu_dict['token'],\n 'channel_id': channel_1['channel_id'],\n 'message': \"hello!!!\"\n })\n requests.post(url + 'message/send', json={\n 'token': boyu_dict['token'],\n 'channel_id': channel_1['channel_id'],\n 'message': \"h_e_l_l_o\"\n })\n requests.post(url + 'message/send', json={\n 'token': boyu_dict['token'],\n 'channel_id': channel_1['channel_id'],\n 'message': \"hi there\"\n })\n id_1 = json.loads(resp_message1.text)\n # test the data returned by search\n resp_search = requests.get(url + 'search', params={\n 'token': boyu_dict['token'],\n 'query_str': \"hello\"\n })\n search_result = json.loads(resp_search.text)\n assert len(search_result['messages']) == 1\n assert search_result['messages'][0][\"message\"] == 'hello!!!'\n assert search_result['messages'][0][\"u_id\"] == boyu_dict['u_id']\n assert search_result['messages'][0][\"message_id\"] == id_1['message_id']\n\n\n# test the search message between different channels\ndef test_search_message_different_channels(url):\n clear()\n test_data = creatData(url)\n boyu_dict = test_data.register_boyu()\n channel_1 = test_data.creat_channel(boyu_dict['token'], 'team_1', True)\n channel_2 = test_data.creat_channel(boyu_dict['token'], 'team_2', True)\n # send a message including hello to channel1\n requests.post(url + 'message/send', json={\n 'token': boyu_dict['token'],\n 'channel_id': channel_1['channel_id'],\n 'message': \"hello1\"\n })\n sleep(1)\n # send a message including hello to channel2\n requests.post(url + 'message/send', json={\n 'token': boyu_dict['token'],\n 'channel_id': channel_2['channel_id'],\n 'message': \"hello2\"\n })\n sleep(1)\n # send another message including hello to channel1\n requests.post(url + 'message/send', json={\n 'token': boyu_dict['token'],\n 'channel_id': channel_1['channel_id'],\n 'message': \"hello3\"\n })\n sleep(1)\n resp_search = requests.get(url + 'search', params={\n 'token': boyu_dict['token'],\n 'query_str': \"hello\"\n })\n search_result = json.loads(resp_search.text)\n assert len(search_result['messages']) == 3\n assert search_result['messages'][0]['message'] == 'hello3'\n assert search_result['messages'][1]['message'] == 'hello2'\n assert search_result['messages'][2]['message'] == 'hello1'\n\n\n\ndef test_admin_user_remove(url):\n clear()\n test_data = creatData(url)\n boyu_dict = test_data.register_boyu()\n wenyao_dict = test_data.register_wenyao()\n res = requests.delete(url + 'admin/user/remove', json={\n 'token': boyu_dict['token'],\n 'u_id': wenyao_dict['u_id'],\n })\n assert res.status_code == 200", "sub_path": "src/other_http_test.py", "file_name": "other_http_test.py", "file_ext": "py", "file_size_in_byte": 11218, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "other.clear", "line_number": 10, "usage_type": "call"}, {"api_name": "httptest_helper.creatData", "line_number": 11, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 11, "usage_type": "argument"}, {"api_name": "requests.post", "line_number": 14, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 14, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 19, "usage_type": "call"}, {"api_name": "requests.delete", "line_number": 21, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 21, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 22, "usage_type": "call"}, {"api_name": "httptest_helper.creatData", "line_number": 24, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 24, "usage_type": "argument"}, {"api_name": "requests.post", "line_number": 29, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 29, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 34, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 36, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 36, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 37, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 46, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 46, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 51, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 51, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 54, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 55, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 55, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 58, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 61, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 61, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 65, "usage_type": "call"}, {"api_name": "other.clear", "line_number": 74, "usage_type": "call"}, {"api_name": "httptest_helper.creatData", "line_number": 75, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 75, "usage_type": "argument"}, {"api_name": "pytest.raises", "line_number": 78, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 78, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 79, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 79, "usage_type": "name"}, {"api_name": "other.clear", "line_number": 84, "usage_type": "call"}, {"api_name": "httptest_helper.creatData", "line_number": 85, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 85, "usage_type": "argument"}, {"api_name": "requests.get", "line_number": 90, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 90, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 91, "usage_type": "call"}, {"api_name": "other.clear", "line_number": 111, "usage_type": "call"}, {"api_name": "httptest_helper.creatData", "line_number": 112, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 112, "usage_type": "argument"}, {"api_name": "pytest.raises", "line_number": 114, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 114, "usage_type": "attribute"}, {"api_name": "requests.post", "line_number": 115, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 115, "usage_type": "name"}, {"api_name": "other.clear", "line_number": 124, "usage_type": "call"}, {"api_name": "httptest_helper.creatData", "line_number": 125, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 125, "usage_type": "argument"}, {"api_name": "pytest.raises", "line_number": 127, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 127, "usage_type": "attribute"}, {"api_name": "requests.post", "line_number": 128, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 128, "usage_type": "name"}, {"api_name": "other.clear", "line_number": 137, "usage_type": "call"}, {"api_name": "httptest_helper.creatData", "line_number": 138, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 138, "usage_type": "argument"}, {"api_name": "pytest.raises", "line_number": 141, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 141, "usage_type": "attribute"}, {"api_name": "requests.post", "line_number": 142, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 142, "usage_type": "name"}, {"api_name": "other.clear", "line_number": 151, "usage_type": "call"}, {"api_name": "httptest_helper.creatData", "line_number": 152, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 152, "usage_type": "argument"}, {"api_name": "requests.post", "line_number": 157, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 157, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 162, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 165, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 165, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 170, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 173, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 173, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 178, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 181, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 181, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 185, "usage_type": "call"}, {"api_name": "other.clear", "line_number": 215, "usage_type": "call"}, {"api_name": "httptest_helper.creatData", "line_number": 216, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 216, "usage_type": "argument"}, {"api_name": "requests.post", "line_number": 220, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 220, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 225, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 225, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 230, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 230, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 235, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 237, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 237, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 241, "usage_type": "call"}, {"api_name": "other.clear", "line_number": 250, "usage_type": "call"}, {"api_name": "httptest_helper.creatData", "line_number": 251, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 251, "usage_type": "argument"}, {"api_name": "requests.post", "line_number": 256, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 256, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 261, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 263, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 263, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 268, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 270, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 270, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 275, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 276, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 276, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 280, "usage_type": "call"}, {"api_name": "other.clear", "line_number": 289, "usage_type": "call"}, {"api_name": "httptest_helper.creatData", "line_number": 290, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 290, "usage_type": "argument"}, {"api_name": "requests.delete", "line_number": 293, "usage_type": "call"}, {"api_name": "httptest_helper.url", "line_number": 293, "usage_type": "name"}]} +{"seq_id": "181064755", "text": "\"\"\"\n乳がんデータセット\n\nデータセットに含まれる特徴量は、乳房塊の微細針吸引物（FNA）のデジタル化画像から計算される。\n画像中に存在する細胞核の特徴を捉えたものである。\n569データ含まれていて、WDBC-MalignantとWDBC-Benignに分類する。\n\n説明変数\n1. mean radius 平均半径\n2. mean texture テクスチャをグレースケールにした際の平均\n3. mean perimeter 平均外周の長さ\n4. mean area 平均面積\n5. mean smoothness 平均なめらかさ（半径の分散）\n6. mean compactness 外周長さ^2 / 面積 - 1.0で示すコンパクトさ平均\n7. mean concavity 輪郭の凹部の重要度の平均\n8. mean concave points 輪郭の凹部の数の平均\n9. mean symmetry 対称性\n10. mean fractal dimension フラクタル次元の平均\n11. radius error 半径誤差\n12. texture error テクスチャの誤差\n13. perimeter error 外周の誤差\n14. area error 面積の誤差\n15. smoothness error なめらかさの誤差\n16. compactness error コンパクトさの誤差\n17. concavity error 輪郭の凹部の重要度の誤差\n18. concave points error 輪郭の凹部の数の誤差\n19. symmetry error 対称性の誤差\n20. fractal dimension error フラクタル次元の誤差\n21. worst radius 半径最悪値\n22. worst texture テクスチャ最悪値\n23. worst perimeter 外周の長さ最悪値\n24. worst area 面積の最悪値\n25. worst smoothness なめらかさの最悪値\n26. worst compactness コンパクトさの最悪値\n27. worst concavity 輪郭の凹部の重要度の最悪値\n28. worst concave points 輪郭の凹部の数の最悪値\n29. worst symmetry 対称性の最悪値\n30. worst fractal dimension フラクタル次元の最悪値\n\n\"\"\"\n\n\n\nimport numpy as np\nimport adaboost\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.datasets import load_breast_cancer\ncancer = load_breast_cancer()#乳癌識別データセット\nx = cancer.data.astype(np.float64)\ny = cancer.target.astype(np.float64)\ny[y==0] = -1.0\n\nx_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n\nboost = 20\nmax_depth = 7\n##################################################################################\nw = np.ones([x_train.shape[0]],dtype=np.float64)\ntree = adaboost.Tree(n_max=max_depth)\nloss = tree.train(x_train,y_train,w)\n\no = tree.inference(x_test)\npoint = (np.sum(o==y_test)/float(x_test.shape[0]))* 100.0\nprint(\"決定木=\"+str(point)+\"%\")#決定木=91.2280701754%\n\n##################################################################################\nada = adaboost.Adaboost(boost,max_depth=max_depth)\nada.train(x_train,y_train)\no = ada.inference(x_test)\npoint = (np.sum(o==y_test)/float(x_test.shape[0]))* 100.0\nprint(\"ADABOOST=\"+str(point)+\"%\")#ADABOOST=96.4912280702%\n\n\n\n\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2840, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "sklearn.datasets.load_breast_cancer", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 49, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 50, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 58, "usage_type": "attribute"}, {"api_name": "adaboost.Tree", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 63, "usage_type": "call"}, {"api_name": "adaboost.Adaboost", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 70, "usage_type": "call"}]} +{"seq_id": "486165067", "text": "import pymongo as pm\nfrom pymongo import errors\nfrom datetime import datetime\n\nfrom bot_app.models import User\n\n\nclass DataBaseClass:\n def __init__(self, login, password, host):\n self.client = pm.MongoClient(f'mongodb+srv://{login}:{password}@{host}/?retryWrites=true&w=majority')\n self.db = self.client['bot_db']\n self.users_col = self.db['users']\n self.questions_col = self.db['questions']\n self.autoinc = self.db['autoinc']\n self.stat_col = self.db['stat']\n\n def get_user(self, user_id):\n res = self.users_col.find_one({'user_id': user_id}, {'_id': 0})\n return User(**res) if res else False\n\n def add_user(self, user: User):\n try:\n user.register_time = datetime.utcnow()\n res = self.users_col.insert_one(user.__dict__)\n return True if res.inserted_id else False\n except errors.OperationFailure:\n return False\n\n def user_exists(self, user_id):\n return self.users_col.find_one({'user_id': user_id}) is not None\n\n def edit_user(self, user_id, user_obj: User = None, **kwargs):\n if user_obj:\n res = self.users_col.update_one({'user_id': user_obj.user_id},\n {'$set': user_obj.__dict__})\n else:\n res = self.users_col.update_one({'user_id': user_id},\n {'$set': kwargs})\n return res.modified_count != 0\n\n def delete_user(self, user_id):\n res = self.users_col.delete_one({'user_id': user_id})\n self.questions_col.delete_many({'user_id': user_id})\n return res.deleted_count != 0\n\n def add_question(self, user_id, text):\n res = self.questions_col.insert_one({'_id': self.get_unique_id(),\n 'user_id': user_id,\n 'text': text,\n 'datetime': datetime.utcnow(),\n 'answered': False,\n 'answer': None})\n return res.inserted_id if res.inserted_id else False\n\n def get_unique_id(self):\n res = self.autoinc.find_one_and_update({'_id': 'lastQuestionCounter'},\n {'$inc': {'counter': 1}},\n upsert=True)\n return int(res['counter'])\n\n def stat_dialogflow_inc(self):\n res = self.autoinc.update_one({'_id': 'dflow_count'}, {'$inc': {'counter': 1}})\n return res.modified_count\n", "sub_path": "bot_app/database.py", "file_name": "database.py", "file_ext": "py", "file_size_in_byte": 2601, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "pymongo.MongoClient", "line_number": 10, "usage_type": "call"}, {"api_name": "bot_app.models.User", "line_number": 19, "usage_type": "call"}, {"api_name": "bot_app.models.User", "line_number": 21, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 23, "usage_type": "name"}, {"api_name": "pymongo.errors.OperationFailure", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pymongo.errors", "line_number": 26, "usage_type": "name"}, {"api_name": "bot_app.models.User", "line_number": 32, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 50, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 50, "usage_type": "name"}]} +{"seq_id": "358978870", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\nfrom recommender.reader.ratings import RatingGetter\nfrom recommender.configx.configx import Configx\nfrom recommender.metrics.metric import Matric\n\nclass MF(object):\n \"\"\"\n base class for matrix factorization\n \"\"\"\n\n def __init__(self):\n super(MF, self).__init__()\n self.rg = RatingGetter()\n self.config = Configx()\n self.iter_rmse = []\n self.iter_mae = []\n\n def init_model(self):\n self.P = np.random.rand(self.rg.get_train_size()[0], self.config.factor)\n self.Q = np.random.rand(self.rg.get_train_size()[1], self.config.factor)\n self.loss, self.lastLoss = 0.0, 0.0\n\n def train_model(self):\n pass\n\n def predict(self, u, i):\n if self.rg.contains_user(u) and self.rg.contains_item(i):\n return self.P[self.rg.train_user[u]].dot(self.Q[self.rg.train_item[i]])\n elif self.rg.contains_user(u) and not self.rg.contains_item(i):\n return self.rg.userMeans[u]\n elif not self.rg.contains_user(u) and self.rg.contains_item(i):\n return self.rg.itemMeans[i]\n else:\n return self.rg.globalMean\n\n def predict_model(self):\n result = []\n for index, line in enumerate(self.rg.test_set()):\n user, item, rating = line\n prediction = self.predict(user, item)\n prediction = self.checkRatingBoundary(prediction)\n result.append([rating, prediction])\n rmse = Matric.rmse(result)\n mae = Matric.mae(result)\n self.iter_rmse.append(rmse)\n self.iter_mae.append(mae)\n return rmse, mae\n # print(f'最终误差RMSE为{rmse}')\n\n def checkRatingBoundary(self, prediction):\n if prediction > self.config.max_val:\n return self.config.max_val\n elif prediction < self.config.min_val:\n return self.config.min_val\n else:\n return round(prediction, 3)\n\n def isConverged(self, iteration):\n deltaLoss = self.lastLoss - self.loss\n rmse, mae = self.predict_model()\n print('%s iteration %d: loss = %.4f, delta_loss = %.5f learning_Rate = %.5f rmse=%.5f mae=%.5f' % \\\n (self.__class__, iteration, self.loss, deltaLoss, self.config.lr, rmse, mae))\n # check if converged\n converged = abs(deltaLoss) < self.config.threshold\n self.lastLoss = self.loss\n return converged\n\n def show_rmse(self):\n '''\n show figure for rmse and epoch\n '''\n nums = range(len(self.iter_rmse))\n plt.plot(nums, self.iter_rmse, label='RMSE')\n plt.plot(nums, self.iter_mae, label='MAE')\n plt.xlabel('# of epoch')\n plt.ylabel('metric')\n plt.title(self.__class__)\n plt.legend()\n plt.show()\n", "sub_path": "model/mf.py", "file_name": "mf.py", "file_ext": "py", "file_size_in_byte": 2814, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "recommender.reader.ratings.RatingGetter", "line_number": 15, "usage_type": "call"}, {"api_name": "recommender.configx.configx.Configx", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 22, "usage_type": "attribute"}, {"api_name": "recommender.metrics.metric.Matric.rmse", "line_number": 45, "usage_type": "call"}, {"api_name": "recommender.metrics.metric.Matric", "line_number": 45, "usage_type": "name"}, {"api_name": "recommender.metrics.metric.Matric.mae", "line_number": 46, "usage_type": "call"}, {"api_name": "recommender.metrics.metric.Matric", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}]} +{"seq_id": "653797231", "text": "import os \nimport sys\nimport importlib.util\nimport pdb\n\nfrom .framework_interface import FrameworkInterface, exec_measurement\nsys.path.append(os.path.dirname(os.path.realpath(__file__)) + '/scrabble')\nfrom ..db import *\n\nPOINT_POSTFIXES = ['sensor', 'setpoint', 'alarm', 'command', 'meter']\n\nfrom scrabble import Scrabble # This may imply incompatible imports.\n\n\nclass ScrabbleInterface(FrameworkInterface):\n \"\"\"docstring for ScrabbleInterface\"\"\"\n def __init__(self, target_building, exp_id='none', conf={\n 'source_buildings': ['ebu3b'],\n 'source_samples_list': [5],\n 'logger_postfix': 'temp',\n 'seed_num': 5}):\n super(ScrabbleInterface, self).__init__(conf, exp_id, 'scrabble')\n self.target_building = target_building\n self.source_buildings = conf['source_buildings']\n self.sample_num_list = conf['source_samples_list']\n self.seed_num = conf['seed_num']\n if self.target_building not in self.source_buildings:\n self.source_buildings = self.source_buildings + [self.target_building]\n self.sample_num_list = self.sample_num_list + [self.seed_num]\n conf['use_cluster_flag'] = True\n conf['use_brick_flag'] = True\n conf['negative_flag'] = True\n self.logger_postfix = conf['logger_postfix']\n\n column_names = ['VendorGivenName', \n 'BACnetName', \n 'BACnetDescription']\n\n self.building_sentence_dict = dict()\n self.building_label_dict = dict()\n self.building_tagsets_dict = dict()\n for building in self.source_buildings:\n true_tagsets = {}\n label_dict = {}\n for labeled in LabeledMetadata.objects(building=building):\n srcid = labeled.srcid\n true_tagsets[srcid] = labeled.tagsets\n fullparsing = None\n for clm in column_names:\n one_fullparsing = [i[1] for i in labeled.fullparsing[clm]]\n if not fullparsing:\n fullparsing = one_fullparsing\n else:\n fullparsing += ['O'] + one_fullparsing\n # This format is alinged with the sentence \n # conformation rule.\n label_dict[srcid] = fullparsing\n\n self.building_tagsets_dict[building] = true_tagsets\n self.building_label_dict[building] = label_dict\n sentence_dict = dict()\n for raw_point in RawMetadata.objects(building=building):\n srcid = raw_point.srcid\n if srcid in true_tagsets:\n metadata = raw_point['metadata']\n sentence = None\n for clm in column_names:\n if not sentence:\n sentence = [c for c in metadata[clm].lower()]\n else:\n sentence += ['\\n'] + \\\n [c for c in metadata[clm].lower()]\n sentence_dict[srcid] = sentence\n self.building_sentence_dict[building] = sentence_dict\n\n # Validation of the dataset\n for building in self.source_buildings:\n for srcid, label_pairs in self.building_label_dict[building]\\\n .items():\n assert len(label_pairs) == \\\n len(self.building_sentence_dict[building][srcid])\n\n self.scrabble = Scrabble(\n self.source_buildings,\n self.target_building,\n self.sample_num_list,\n self.building_sentence_dict,\n self.building_label_dict,\n self.building_tagsets_dict,\n conf)\n @exec_measurement\n def learn_auto(self, iter_num=1):\n params = (self.source_buildings,\n self.sample_num_list,\n self.target_building)\n self.learned_srcids = []\n params = {\n 'use_cluster_flag': True,\n 'use_brick_flag': True,\n 'negative_flag': True,\n 'target_building': self.target_building,\n 'building_list': self.source_buildings,\n 'sample_num_list': self.scrabble.sample_num_list\n }\n #self.scrabble.char2tagset_iteration(iter_num, self.logger_postfix, *params)\n step_data = {'iter_num':0,\n 'next_learning_srcids': self.scrabble.get_random_srcids(\n self.scrabble.building_srcid_dict,\n self.source_buildings,\n self.sample_num_list),\n 'model_uuid': None}\n step_datas = [step_data]\n step_datas.append(self.scrabble.char2tagset_onestep(step_data, \n **params))\n pdb.set_trace()\n \n \n @exec_measurement\n def learn_auto2(self, iter_num=1):\n num_sensors_in_gray = 10000\n while num_sensors_in_gray > 0:\n new_srcids = self.zodiac.select_informative_samples_only(10)\n self.update_model(new_srcids)\n num_sensors_in_gray = self.zodiac.get_num_sensors_in_gray()\n pred_point_tagsets = self.zodiac.predict(self.target_srcids)\n for i, srcid in enumerate(self.target_srcids):\n self.pred['tagsets'][srcid] = set([pred_point_tagsets[i]])\n print(num_sensors_in_gray)\n self.evaluate()\n pdb.set_trace()\n", "sub_path": "oracle/frameworks/scrabble_interface.py", "file_name": "scrabble_interface.py", "file_ext": "py", "file_size_in_byte": 5756, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sys.path.append", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 7, "usage_type": "call"}, {"api_name": "framework_interface.FrameworkInterface", "line_number": 15, "usage_type": "name"}, {"api_name": "scrabble.Scrabble", "line_number": 83, "usage_type": "call"}, {"api_name": "pdb.set_trace", "line_number": 115, "usage_type": "call"}, {"api_name": "framework_interface.exec_measurement", "line_number": 91, "usage_type": "name"}, {"api_name": "pdb.set_trace", "line_number": 130, "usage_type": "call"}, {"api_name": "framework_interface.exec_measurement", "line_number": 118, "usage_type": "name"}]} +{"seq_id": "24857386", "text": "import pymysql\n\n# Creating database for Families\n\n# Opening CSV File\nf = open(\"families.csv\", \"r\")\nfString = f.read()\n\nflist = []\nfor line in fString.split(\"\\n\"):\n flist.append(line.split(\",\"))\n# connect to DB\n\nhostname = 'localhost'\nusername = 'root'\npassword = 'vardaan'\ndatabase = 'Agile'\n\ndb = pymysql.connect(host=hostname, user=username, passwd=password, db=database)\ncursor = db.cursor()\n\n# Drop table if it already exists\ncursor.execute(\"DROP TABLE IF EXISTS FAMILIES\")\n\n# Create table from first line in fList\nFID = flist[0][0]\nMarried = flist[0][1]\nDivorced = flist[0][2]\nHusbandID = flist[0][3]\nHusbandName = flist[0][4]\nWIfeID = flist[0][5]\nWifeName = flist[0][6]\nChildren = flist[0][7]\n\n# Create FAMILIES table //place comma after each column except last one\n\nqueryCreateFamilyTable = \"\"\"CREATE TABLE FAMILIES(\n FID VARCHAR(255) ,\n Married DATE ,\n Divorced DATE ,\n HusbandID VARCHAR(255) ,\n HusbandName VARCHAR(255) ,\n WifeID VARCHAR(255) ,\n WifeName VARCHAR(255) ,\n Children VARCHAR(255) )\"\"\"\n\ncursor.execute(queryCreateFamilyTable)\n\n# Delete first row beacuse it is not needed\ndel flist[0]\n\n# Inserting values to the table FAMILIES\nrows = ''\nfor i in range(len(flist) - 1):\n rows += \"('{}', '{}', '{}', '{}', '{}', '{}','{}' )\".format(flist[i][0], flist[i][1], flist[i][2], flist[i][3],\n flist[i][4], flist[i][5], flist[i][6], flist[i][7])\n if i != len(flist) - 2:\n rows += ','\n\nqueryInsertFamily = \"INSERT INTO FAMILIES VALUES\" + rows + \";\"\n\ntry:\n # Execute the Query\n cursor.execute(queryInsertFamily)\n db.commit()\nexcept:\n # Rollback if any error\n db.rollback()\ndb.close()\n\n# Creating database for Individuals\n\n# Opening CSV File\nf = open(\"individuals.csv\", \"r\")\nfString = f.read()\n\nflist = []\nfor line in fString.split(\"\\n\"):\n flist.append(line.split(\",\"))\n# connect to DB\n\nhostname = 'localhost'\nusername = 'root'\npassword = 'vardaan'\ndatabase = 'Agile'\n\ndb = pymysql.connect(host=hostname, user=username, passwd=password, db=database)\ncursor = db.cursor()\n\n# Drop table if it already exists\ncursor.execute(\"DROP TABLE IF EXISTS INDIVIDUALS\")\n\n# Create table from first line in fList\nID = flist[0][0]\nNAME = flist[0][1]\nGENDER = flist[0][2]\nBIRTHDAY = flist[0][3]\nDEATH = flist[0][4]\nAGE = flist[0][5]\nCHILDIN = flist[0][6]\nSPOUSEIN = flist[0][7]\n\n# Create FAMILIES table //place comma after each column except last one\n\nqueryCreateIndiTable = \"\"\"CREATE TABLE INDIVIDUALS(\n ID VARCHAR(255) ,\n NAME VARCHAR(255) ,\n GENDER VARCHAR(255) ,\n BIRTHDAY DATE ,\n DEATH DATE ,\n AGE INT(25) ,\n CHILDIN VARCHAR(255) ,\n SPOUSEIN VARCHAR(255) )\"\"\"\n\ncursor.execute(queryCreateIndiTable)\n\n# Delete first row beacuse it is not needed\ndel flist[0]\n\n# Inserting values to the table FAMILIES\nrows = ''\nfor i in range(len(flist) - 1):\n rows += \"('{}', '{}', '{}', '{}', '{}', '{}','{}' )\".format(flist[i][0], flist[i][1], flist[i][2], flist[i][3],\n flist[i][4], flist[i][5], flist[i][6], flist[i][7])\n if i != len(flist) - 2:\n rows += ','\n\nqueryInsertIndi = \"INSERT INTO INDIVIDUALS VALUES\" + rows + \";\"\n\ntry:\n # Execute the Query\n cursor.execute(queryInsertIndi)\n db.commit()\nexcept:\n # Rollback if any error\n db.rollback()\ndb.close()\n", "sub_path": "csvToDB.py", "file_name": "csvToDB.py", "file_ext": "py", "file_size_in_byte": 3755, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pymysql.connect", "line_number": 19, "usage_type": "call"}, {"api_name": "pymysql.connect", "line_number": 87, "usage_type": "call"}]} +{"seq_id": "445437077", "text": "#importing some useful packages\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimport numpy as np\nimport os\nimport cv2\n#%matplotlib inline\n# Import everything needed to edit/save/watch video clips\nfrom moviepy.editor import VideoFileClip\n#from IPython.display import HTML\n\nimport math\n\ndef grayscale(img):\n \"\"\"Applies the Grayscale transform\n This will return an image with only one color channel\n but NOTE: to see the returned image as grayscale\n (assuming your grayscaled image is called 'gray')\n you should call plt.imshow(gray, cmap='gray')\"\"\"\n return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n # Or use BGR2GRAY if you read an image with cv2.imread()\n # return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n \ndef canny(img, low_threshold, high_threshold):\n \"\"\"Applies the Canny transform\"\"\"\n return cv2.Canny(img, low_threshold, high_threshold)\n\ndef gaussian_blur(img, kernel_size):\n \"\"\"Applies a Gaussian Noise kernel\"\"\"\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)\n\ndef region_of_interest(img, vertices):\n \"\"\"\n Applies an image mask.\n \n Only keeps the region of the image defined by the polygon\n formed from `vertices`. The rest of the image is set to black.\n `vertices` should be a numpy array of integer points.\n \"\"\"\n #defining a blank mask to start with\n mask = np.zeros_like(img) \n \n #defining a 3 channel or 1 channel color to fill the mask with depending on the input image\n if len(img.shape) > 2:\n channel_count = img.shape[2] # i.e. 3 or 4 depending on your image\n ignore_mask_color = (255,) * channel_count\n else:\n ignore_mask_color = 255\n \n #filling pixels inside the polygon defined by \"vertices\" with the fill color \n cv2.fillPoly(mask, vertices, ignore_mask_color)\n \n #returning the image only where mask pixels are nonzero\n masked_image = cv2.bitwise_and(img, mask)\n return masked_image\n\n\ndef draw_lines(img, lines, color=[255, 0, 0], thickness=10):\n \"\"\"\n NOTE: this is the function you might want to use as a starting point once you want to \n average/extrapolate the line segments you detect to map out the full\n extent of the lane (going from the result shown in raw-lines-example.mp4\n to that shown in P1_example.mp4). \n \n Think about things like separating line segments by their \n slope ((y2-y1)/(x2-x1)) to decide which segments are part of the left\n line vs. the right line. Then, you can average the position of each of \n the lines and extrapolate to the top and bottom of the lane.\n \n This function draws `lines` with `color` and `thickness`. \n Lines are drawn on the image inplace (mutates the image).\n If you want to make the lines semi-transparent, think about combining\n this function with the weighted_img() function below\n \"\"\"\n left_lines=[]\n right_lines = []\n avg_left_slope=0.\n avg_right_slope=0.\n top_point_left = ()\n bottom_point_left = ()\n top_point_right = ()\n bottom_point_right = ()\n relative_error = 0.\n tolerance = 0.05\n length_of_left=0\n length_of_right=0\n for line in lines:\n for x1,y1,x2,y2 in line:\n slope = (y2-y1)/(x2-x1)\n #if slope is negative, it is a left line\n if slope < 0:\n if avg_left_slope == 0:\n length_of_left = calculateDistance((x1,y1), (x2,y2))\n avg_left_slope = slope\n else:\n relative_error = abs((slope - avg_left_slope)/avg_left_slope)\n if relative_error > tolerance:\n continue\n else:\n new_length_to_add = calculateDistance((x1,y1), (x2,y2))\n avg_left_slope = find_new_avg_slope(avg_left_slope, slope, new_length_to_add, length_of_left)\n length_of_left += new_length_to_add\n left_lines.append(line)\n #if slope is positive, it is a right line\n elif slope > 0 :\n if avg_right_slope == 0:\n length_of_right = calculateDistance((x1,y1), (x2,y2))\n avg_right_slope = slope\n else:\n relative_error = abs((slope - avg_right_slope)/avg_right_slope)\n if relative_error > tolerance:\n continue\n else:\n new_length_to_add = calculateDistance((x1,y1), (x2,y2))\n avg_right_slope = find_new_avg_slope(avg_right_slope, slope, new_length_to_add, length_of_right)\n length_of_right += new_length_to_add \n right_lines.append(line)\n #img.shape[0] = height (y)\n #img.shape[1] = width (x)\n if avg_right_slope == 0 or avg_left_slope==0:\n print('slope = 0')\n midway_right = find_midway(right_lines, max(img.shape[0], img.shape[1]))\n top_point_right = find_intersection_point( midway_right, avg_right_slope, round(img.shape[0]*0.592)) #find_lowest_y_point(right_lines, max(img.shape[0], img.shape[1])) #\n bottom_point_right = find_intersection_point(midway_right, avg_right_slope, img.shape[0]-1)\n\n midway_left = find_midway(left_lines, max(img.shape[0], img.shape[1]))\n top_point_left = find_intersection_point( midway_left, avg_left_slope, round(img.shape[0]*0.592)) #find_lowest_y_point(left_lines, max(img.shape[0], img.shape[1]))\n bottom_point_left = find_intersection_point(midway_left, avg_left_slope, img.shape[0]-1)\n\n cv2.line(img, top_point_right, bottom_point_right , color, thickness)\n cv2.line(img, top_point_left, bottom_point_left, color, thickness)\n #draw_hough_lines(img, right_lines, [255,255,0])\n #draw_hough_lines(img, left_lines, [0,255,255])\n\ndef calculateDistance(p1,p2):\n (x1,y1) = p1\n (x2,y2) = p2\n distance = math.sqrt((x2-x1)**2+(y2-y1)**2)\n return distance\n\ndef draw_hough_lines(img, lines, color=[255, 0, 255], thickness=2):\n for line in lines:\n for x1,y1,x2,y2 in line:\n cv2.line(img, (x1,y1), (x2,y2) , color, thickness)\n\n\n#Finds the bottom point on the image that intersects with the specified line\ndef find_intersection_point(point_on_line, slope, intersection_y):\n x0, y0 = point_on_line\n if slope==0:\n print(\"!!!!!Slope is 0!!!!!\")\n return (0,0)\n x1=int(round((intersection_y - y0)/slope))+x0\n return (x1, intersection_y)\n\n#Finds the point which has the lowest y value in a list of lines\ndef find_lowest_y_point(lines, max_pixel):\n lowest_point=(max_pixel, max_pixel)\n for line in lines:\n for x1,y1,x2,y2 in line:\n if y1highest_point[1]:\n highest_point = (x1, y1)\n if y2>highest_point[1]:\n highest_point = (x2, y2)\n return highest_point\n\ndef find_midway(lines, max_pixel):\n (x1,y1) = find_lowest_y_point(lines, max_pixel)\n (x2,y2) = find_highest_y_point(lines)\n (x3,y3) = (int(round((x1+x2)/2)),int(round((y1+y2)/2)))\n return (x3,y3)\n\ndef find_new_avg_slope(old_slope, slope_to_add, new_length_to_add, length_of_line):\n new_slope = ((old_slope*length_of_line)+(slope_to_add*new_length_to_add))/(length_of_line+new_length_to_add)\n return new_slope\n\ndef hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap):\n \"\"\"\n `img` should be the output of a Canny transform.\n \n Returns an image with hough lines drawn.\n \"\"\"\n lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len, maxLineGap=max_line_gap)\n line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)\n draw_lines(line_img, lines)\n return line_img\n\n# Python 3 has support for cool math symbols.\n\ndef weighted_img(img, initial_img, α=1., β=5, γ=0.):\n \"\"\"\n `img` is the output of the hough_lines(), An image with lines drawn on it.\n Should be a blank image (all black) with lines drawn on it.\n \n `initial_img` should be the image before any processing.\n \n The result image is computed as follows:\n \n initial_img * α + img * β + γ\n NOTE: initial_img and img must be the same shape!\n \"\"\"\n if len(initial_img.shape) != 3:\n initial_img = cv2.cvtColor(initial_img,cv2.COLOR_GRAY2RGB)\n return cv2.addWeighted(initial_img, α, img, β, γ)\n\n# TODO: Build your pipeline that will draw lane lines on the test_images\n# then save them to the test_images_output directory.\n\n# Read in and grayscale the image\ndef read(filename):\n image = mpimg.imread('test_images/'+filename)\n return image\n \ndef make_canny(image):\n gray = grayscale(image)\n # Define a kernel size and apply Gaussian smoothing\n kernel_size = 5\n blur_gray = gaussian_blur(gray, kernel_size)\n\n # Define our parameters for Canny and apply\n low_threshold = 50\n high_threshold = 180\n edges = canny(blur_gray, low_threshold, high_threshold)\n return edges\ndef mask_image(edges_img):\n # This time we are defining a four sided polygon to mask\n imshape = edges_img.shape\n #img.shape[0] = height (y) #img.shape[1] = width (x)\n vertices = np.array([[(0,imshape[0]),(imshape[1]*0.468, imshape[0]*0.61), (imshape[1]*0.52, imshape[0]*0.61), (imshape[1],imshape[0])]], dtype=np.int32)\n masked_edges = region_of_interest(edges_img, vertices)\n return masked_edges\ndef hough_transform(original_img, masked_edges, filename, showEnabled=True):\n # Define the Hough transform parameters\n # Make a blank the same size as our image to draw on\n rho = 1 # distance resolution in pixels of the Hough grid\n theta = np.pi/180 # angular resolution in radians of the Hough grid\n threshold = 20 # minimum number of votes (intersections in Hough grid cell)\n min_line_length = 20 # minimum number of pixels making up a line\n max_line_gap = 20 # maximum gap in pixels between connectable line segments\n\n \n line_img = hough_lines(masked_edges, rho, theta, threshold, min_line_length, max_line_gap)\n\n lines_edges = weighted_img(line_img, original_img)\n if showEnabled==True:\n plt.imshow(lines_edges)\n plt.title(filename + ' - Lane lines marked')\n \n if not os.path.exists('test_images_output'):\n os.makedirs('test_images_output')\n\n plt.savefig('test_images_output/'+filename)\n return None\n else:\n return lines_edges\n\n\ndef work_on_images():\n for file in os.listdir(\"test_images/\"):\n init_img = read(file)\n edges = make_canny(init_img)\n masked_edges = mask_image(edges)\n #combined_img = weighted_img(init_img, masked_edges)\n hough_transform(init_img, masked_edges, file)\n\ndef work_on_video(filename):\n white_output = 'test_videos_output/'+ filename\n ## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video\n ## To do so add .subclip(start_second,end_second) to the end of the line below\n ## Where start_second and end_second are integer values representing the start and end of the subclip\n ## You may also uncomment the following line for a subclip of the first 5 seconds\n ##clip1 = VideoFileClip(\"test_videos/solidWhiteRight.mp4\").subclip(0,5)\n clip1 = VideoFileClip(\"test_videos/\"+filename)#.subclip(5.28,5.3) #solidWhiteRight.mp4\")\n white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!\n white_clip.write_videofile(white_output, audio=False)\n\ndef process_image(image):\n # NOTE: The output you return should be a color image (3 channel) for processing video below\n # TODO: put your pipeline here,\n # you should return the final output (image where lines are drawn on lanes)\n edges = make_canny(image)\n masked_edges = mask_image(edges)\n result = hough_transform(image , masked_edges, '', False)\n return result\n\nif __name__ == '__main__':\n work_on_images()\n #work_on_video(\"solidWhiteRight.mp4\")\n #work_on_video(\"solidYellowLeft.mp4\")", "sub_path": "P1-newapproach.py", "file_name": "P1-newapproach.py", "file_ext": "py", "file_size_in_byte": 12378, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "cv2.cvtColor", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2GRAY", "line_number": 20, "usage_type": "attribute"}, {"api_name": "cv2.Canny", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.GaussianBlur", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 41, "usage_type": "call"}, {"api_name": "cv2.fillPoly", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 54, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 130, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 131, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 138, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 144, "usage_type": "call"}, {"api_name": "cv2.HoughLinesP", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 195, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 214, "usage_type": "call"}, {"api_name": "cv2.COLOR_GRAY2RGB", "line_number": 214, "usage_type": "attribute"}, {"api_name": "cv2.addWeighted", "line_number": 215, "usage_type": "call"}, {"api_name": "matplotlib.image.imread", "line_number": 222, "usage_type": "call"}, {"api_name": "matplotlib.image", "line_number": 222, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 240, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 247, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 257, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 257, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 258, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 258, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 260, "usage_type": "call"}, {"api_name": "os.path", "line_number": 260, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 261, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 263, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 263, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 270, "usage_type": "call"}, {"api_name": "moviepy.editor.VideoFileClip", "line_number": 284, "usage_type": "call"}]} +{"seq_id": "142285221", "text": "import numpy as np\nfrom sklearn.feature_selection import RFE\nfrom sklearn.linear_model import Lasso\nfrom sklearn.ensemble import ExtraTreesRegressor\nfrom sklearn.decomposition import PCA\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler\nfrom scipy.stats import boxcox\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport os\nimport xgboost as xgb\nfrom sklearn.feature_selection import VarianceThreshold, SelectKBest, f_regression\n\n# variables that control the number of features to be selected in each FS method\nNUM_FEATURES_UNIVARIATE = 10 # This should be less than the total number of input features\nNUM_FEATURES_RFE = 10 # This should be less than the total number of input features\nNUM_FEATURES_PCA = 2 # This should be less than the total number of input features\nimport warnings\n\nwarnings.filterwarnings(\"ignore\")\n\n\"\"\"\n User edits in this file\n --------------------\n\n lines 16, 17, and 18 in order to control the number of features to be selected in each feature selection method\n\n\n Scaling\n --------------------\n\n * scale=True, scale_input=True, scale_output=True: Will scale both the input and the output columns.\n * scale=True, scale_input=True, scale_output=False: Will scale only the input columns.\n * scale=True, scale_input=False, scale_output=True: Will scale only the output column.\n * scale=True, scale_input=False, scale_output=False: Will not scale any columns, although scale=True, but either scale_input or scale_output must be True\n * scale=False, scale_input=True, scale_output=True: Will not scale any columns, although both scale_input=True and scale_output=True, but scale must be True as well in order to perform any scaling exercise.\n * scale=False, scale_input=True, scale_output=False: Will not scale any columns, although scale_input=True, but scale must be True as well in order to perform any scaling exercise.\n * scale=False, scale_input=False, scale_output=True: Will not scale any columns, although scale_output=True, but scale must be True as well in order to perform any scaling exercise.\n * scale=False, scale_input=False, scale_output=False: Will not scale any columns. \n\n\n Indexing Input Columns in order to Scale\n --------------------------------------------\n\n If scale=True and scale_input=True\n * input_zscore=(start_index_1, end_index_1): will apply Z-score scaling for the input columns starting at index start_index_1 and ending at end_index_1 (eclusive). By defualt, None. If None, no z-score scaling to any of the input columns is applied.\n * input_minmax=(start_index_2, end_index_2): will apply min-max scaling for the input columns starting at index start_index_2 and ending at end_index_2 (eclusive). By defualt, None. If None, no min-max scaling to any of the input columns is applied.\n * input_box=(start_index_3, end_index_3): will apply box-cox transformation for the input columns starting at index start_index_3 and ending at end_index_3 (eclusive). By defualt, None. If None, no box-cox transformation to any of the input columns is applied.\n * input_log=(start_index_4, end_index_4): will apply log transformation for the input columns starting at index start_index_4 and ending at end_index_4 (eclusive). By defualt, None. If None, no log transformation to any of the input columns is applied.\n\n\n Specifying Scaling Type to the Output Column\n --------------------------------------------\n\n If scale=True and scale_output=True:\n * output_zscore: Boolean, by default, False. If True, Z-score scaling for the output column will be applied.\n * output_minmax: Boolean, by default, False. If True, min-max scaling for the output column will be applied.\n * output_box: Boolean, by default, False. If True, box-cox transformation for the output column will be applied.\n * output_log: Boolean, by default, False. If True, log transformation for the output column will be applied.\n\n Note: Either one of the above must be True, and all others must be False because we have to apply only one kind of scaling for the output column.\n\n Saving Feature Selection Plots\n --------------------\n\n * output_folder: the path to the output folder that will be holding several modeling plots. If the path specified does not exist, it will be created dynamically at runtime.\n\n Columns\n --------------------\n\n * cols_drop: list containing the names of the columns the user wants to drop from the data. By default, None. If None, no columns will be dropped from the data.\n * target_variable: name of the column holding the target variable (this will be the output column)\n\n\n Raises\n --------------------\n ValueError\n * If NUM_FEATURES_UNIVARIATE (line 16 in feature_selection.py)is greater than the total number of input features.\n ValueError\n * If NUM_FEATURES_RFE (line 17 in feature_selection.py) is greater than the total number of input features.\n ValueError\n * If NUM_FEATURES_PCA (line 19 in feature_selection.py) is greater than the total number of input features.\n\n\n Feature Selection Methods:\n --------------------------\n\n * drop_zero_std(): drops columns that have 0 standard deviation. (Actually will not drop but show the \n columns that must be dropped)\n\n * drop_low_var(): drops columns that have low variance. (Actually will not drop but show the \n columns that must be dropped)\n\n * drop_high_correlation(): drops columns that have high correlation. (Actually will not drop but show the \n columns that must be dropped)\n\n * feature_importance(xg_boost=True, extra_trees=False): Applies feature importance to the data.\n * xg_boost=True, extra_trees=False: will perform feature importance using XG Boost only\n * xg_boost=False, extra_trees=True: will perform feature importance using Extra Trees only\n * xg_boost=True, extra_trees=True: will perform feature importance using both XG Boost and Extra Trees\n * xg_boost=False, extra_trees=False: Nothing will happen. Avoid this if you want to use feature selection.\n * Default Behavior: i.e. if we do: feature_importance() it will do only XG Boost. As: xg_boost=False, extra_trees=True\n\n * univariate(): Applies Univariate Feature Selection with NUM_FEATURES_UNIVARIATE being selected (specified in line 17 in feature_selection.py). Raises Vlaue Error in this is greater than the total number of input features.\n\n * rfe(): Applies Recursive Feature Elimination with NUM_FEATURES_RFE being selected (specified in line 18 in feature_selection.py). Raises Vlaue Error in this is greater than the total number of input features.\n\n * pca(): Applies PCA NUM_FEATURES_PCA principal components done. (specified in line 19 in feature_selection.py). Raises Vlaue Error in this is greater than the total number of input features.\n\n\"\"\"\n\n\nclass FeatureSelection:\n\n def __init__(self, df, target_variable, output_folder, cols_drop=None,\n scale=True, scale_input=True, scale_output=False,\n output_zscore=False, output_minmax=True, output_box=False, output_log=False,\n input_zscore=None, input_minmax=None, input_box=None, input_log=None):\n\n # drop un-wanted columns from the data\n if cols_drop is not None:\n df = df.drop(cols_drop, axis=1)\n\n # drop NaN values\n df = df.dropna()\n\n # features/columns names (without including target variable)\n self.feature_names = list(df.drop(target_variable, axis=1).columns.values)\n\n # define input and output\n X = np.array(df.loc[:, df.columns != target_variable])\n y = np.array(df.loc[:, target_variable])\n\n X_df = df.loc[:, df.columns != target_variable]\n y_df = df.loc[:, target_variable]\n\n self.df = df\n self.target_variable = target_variable\n self.output_folder = output_folder\n\n self.X = X\n self.y = y\n\n self.X_df = X_df\n self.y_df = y_df\n\n # scaling input & output\n self.scale = scale\n self.scale_input = scale_input\n self.scale_output = scale_output\n\n # specify scaling method for output\n self.output_zscore = output_zscore\n self.output_minmax = output_minmax\n self.output_box = output_box\n self.output_log = output_log\n\n # specify scaling method for input\n self.input_zscore = input_zscore\n self.input_minmax = input_minmax\n self.input_box = input_box\n self.input_log = input_log\n\n # lists that will store the index of the columns to scale\n self.idx_zscore, self.idx_minmax, self.idx_box, self.idx_log = None, None, None, None\n\n self.labelsdict = {\n 'demand': 'demand',\n 'civilians_rank': 'civilians',\n 'distance': 'dist',\n 'AverageTemp': 'Avg.Temp',\n 'AverageWindSpeed': 'Avg.WS',\n 'Precipitation': 'precip',\n 'w_{t-1}': 't-1',\n 'w_{t-2}': 't-2',\n 'w_{t-3}': 't-3',\n 'w_{t-4}': 't-4',\n 'w_{t-5}': 't-5',\n 'w_{t-1}_trend': 'trend',\n 'w_{t-1}_seasonality': 'season',\n 'service_General Medicine': 'Gen.Med',\n 'service_Gynaecology': 'Gynaecol',\n 'service_Pediatrics': 'Ped',\n 'service_Pharmacy': 'Pharm',\n 'mohafaza_B': 'mB',\n 'mohafaza_N': 'mN',\n 'mohafaza_NE': 'mNE',\n }\n\n if scale:\n # if we want to scale\n if input_zscore is not None:\n self.idx_zscore = list(range(input_zscore[0], input_zscore[1]))\n if input_minmax is not None:\n self.idx_minmax = list(range(input_minmax[0], input_minmax[1]))\n if input_box is not None:\n self.idx_box = list(range(input_box[0], input_box[1]))\n if input_log is not None:\n self.idx_log = list(range(input_log[0], input_log[1]))\n\n self.X, self.y = self.scale_cols()\n\n def drop_zero_std(self):\n \"\"\"\n function that removes features having 0 standard deviation\n Note: This function performs analysis using all the features (with target varoable included)\n \"\"\"\n print('\\n********** Method 1: Calculate the no of features which has standard deviation as zero. **********\\n')\n # Remove Constant Features\n df = self.df\n constant_features = [feat for feat in df.columns if df[feat].std() == 0]\n if not constant_features:\n print('We did not find any features having std of 0')\n print(\"data shape remains: {}\".format(df.shape))\n return df\n else:\n print('The following columns have 0 std: {}. They will be removed'.format(constant_features))\n df.drop(labels=constant_features, axis=1, inplace=True)\n print(\"Original data shape: {}\".format(df.shape))\n print(\"Reduced data shape: {}\".format(df.shape))\n return df\n\n def drop_low_var(self, variance_threshold=0.18):\n \"\"\"\n function that drops the columns having low variance\n Note: This function performs analysis using all the features (with target variable included)\n \"\"\"\n print('\\n********** Method 2: Calculate the no of features which has low variance. **********\\n')\n df = self.df\n features = list(df.columns.values)\n sel = VarianceThreshold(threshold=variance_threshold)\n sel.fit(df)\n mask = sel.get_support()\n reduced_df = df.loc[:, mask]\n selected_features = []\n for i in range(len(features)):\n if mask[i]:\n selected_features.append(features[i])\n dropped_features = set(features) - set(selected_features)\n print(\"Original data shape- \", df.shape)\n print(\"Reduced feature dataset shape-\", reduced_df.shape)\n print(\"Dimensionality reduced from {} to {}.\".format(df.shape[1], reduced_df.shape[1]))\n print('Selected features: {}. Len: {}'.format(selected_features, len(selected_features)))\n print('Dropped features: {}. Len: {}'.format(dropped_features, len(dropped_features)))\n\n return reduced_df\n\n def drop_high_correlation(self, moph_project=False):\n '''\n drops columns that have high correlation with each other\n :param moph_project: boolean, indicating if this is for this particular project. If yes,\n it will replace columns names by shorter abbreviations\n :return:\n '''\n df = self.df\n output_folder = self.output_folder\n\n # check if output folder exists, if not create it\n if not os.path.exists(output_folder):\n os.makedirs(output_folder)\n\n # labels to replace columns names by for shorter representation\n if moph_project:\n df = df.rename(columns=self.labelsdict)\n\n # the correlation matrix plot\n corr = df.corr()\n # Add the mask to the heatmap\n mask = np.triu(np.ones_like(corr, dtype=bool), 1)\n\n fig, ax = plt.subplots(figsize=(10, 8))\n ax.set_ylim(20.0, 0)\n heatmap = sns.heatmap(corr, mask=mask, center=0, linewidths=1, annot=True, fmt=\".2f\", ax=ax)\n heatmap = heatmap.get_figure()\n heatmap.set_size_inches(18.5, 10.5)\n\n # rotate x ticks\n ax.tick_params(axis='x', rotation=45)\n ax.tick_params(axis='y', rotation=45)\n\n plt.title('Correlation matrix')\n plt.savefig(output_folder + 'corr_matrix', dpi=100)\n plt.close()\n\n # for tick in ax.xaxis.get_major_ticks():\n # tick.label.set_fontsize(6)\n\n # the columns with high correlation part\n\n # redefine the data frame (with the original namings - not the abbreviations)\n df = self.df\n corr_matrix = df.corr().abs()\n # Create a True/False mask and apply it\n mask = np.triu(np.ones_like(corr_matrix, dtype=bool))\n tri_df = corr_matrix.mask(mask)\n # List column names of highly correlated features (r >0.5 )\n to_drop = [c for c in tri_df.columns if any(tri_df[c] > 0.5)]\n # Drop the features in the to_drop list\n reduced_df = df.drop(to_drop, axis=1)\n print(\"The reduced_df dataframe has {} columns\".format(reduced_df.shape[1]))\n print(\"Dropped columns: {}\".format(to_drop))\n\n\n # def drop_high_correlation(self):\n # \"\"\"\n # function that drops columns having high correlation\n # Note: This function performs analysis using all the features (with target variable included)\n # \"\"\"\n # print('\\n********** Method 3: Remove the features which have a high correlation. **********\\n')\n # df = self.df\n # corr = df.corr()\n # # mask = np.triu(np.ones_like(corr, dtype=bool))\n #\n # # Add the mask to the heatmap\n # fig, ax = plt.subplots(figsize=(10, 8))\n #\n # # ax.set_ylim(20.0, 0)\n # # heatmap = sns.heatmap(corr, mask=mask, center=0, linewidths=1, annot=True, fmt=\".2f\", ax=ax)\n # heatmap = sns.heatmap(corr, center=0, linewidths=1, annot=True, fmt=\".2f\", ax=ax)\n # heatmap = heatmap.get_figure()\n #\n # output_folder = self.output_folder\n # if not os.path.exists(output_folder):\n # os.makedirs(output_folder)\n # heatmap.set_size_inches(18.5, 10.5)\n # heatmap.savefig(output_folder + 'corr_matrix.png')\n #\n # corr_matrix = df.corr().abs()\n #\n # # Create a True/False mask and apply it\n # mask = np.triu(np.ones_like(corr_matrix, dtype=bool))\n # tri_df = corr_matrix.mask(mask)\n #\n # # List column names of highly correlated features (r >0.5 )\n # to_drop = [c for c in tri_df.columns if any(tri_df[c] > 0.5)]\n #\n # # Drop the features in the to_drop list\n # reduced_df = df.drop(to_drop, axis=1)\n # print(\"The reduced_df dataframe has {} columns\".format(reduced_df.shape[1]))\n # print(\"Dropped Columns: {}\".format(to_drop))\n #\n # plt.close()\n\n def feature_importance(self, xg_boost=True, extra_trees=False):\n \"\"\"\n function that displays feature importance using XG-Boost and Extra Trees\n Note: This function performs analysis using X and y\n * xg_boost=True, extra_trees=False: will perform feature importance using XG Boost only\n * xg_boost=False, extra_trees=True: will perform feature importance using Extra Trees only\n * xg_boost=True, extra_trees=True: will perform feature importance using both XG Boost and Extra Trees\n * xg_boost=False, extra_trees=False: Nothing will happen. Avoid this if you want to use feature selection.\n \"\"\"\n output_folder = self.output_folder\n feature_names = self.feature_names\n\n X = self.X_df\n y = self.y_df\n\n if xg_boost:\n print('\\n********** Method 4: Calculating the feature importance using XGBoost. **********\\n')\n ''' feature importance using XGBoost '''\n feature_names = feature_names\n housing_dmatrix = xgb.DMatrix(X, y, feature_names=feature_names)\n # Create the parameter dictionary: params\n params = {\"objective\": \"reg:squarederror\", \"max_depth\": \"4\"}\n # Train the model: xg_reg\n xg_reg = xgb.train(dtrain=housing_dmatrix, params=params, num_boost_round=10)\n\n feature_imp = dict(\n sorted(xg_reg.get_score(importance_type='weight').items(), key=lambda kv: kv[1], reverse=True))\n print('\\nFeatures - Importance\\n')\n for key, value in feature_imp.items():\n print('%s: %.5f' % (key, value))\n print('\\n')\n\n # Plot the feature importances\n xgb.plot_importance(xg_reg)\n\n if not os.path.exists(output_folder):\n os.makedirs(output_folder)\n fig = plt.gcf()\n fig.set_size_inches(15, 10.5)\n plt.title('XGBoost Feature Importance')\n fig.savefig(output_folder + 'xgb_fs', dpi=100)\n plt.close()\n print('saved plot in {}/{}'.format(output_folder, 'xgb_fs'))\n\n if extra_trees:\n print('\\n********** Method 5: Calculating the feature importance using Extra Trees. **********\\n')\n model = ExtraTreesRegressor(n_estimators=100, random_state=42)\n model.fit(X, y)\n feature_imp = {}\n for i in range(len(model.feature_importances_)):\n # print('%s: %.5f' % (columns[i], model.feature_importances_[i]))\n feature_imp[feature_names[i]] = model.feature_importances_[i]\n feature_imp = dict(sorted(feature_imp.items(), key=lambda kv: kv[1], reverse=True))\n print('\\nFeatures - Importance\\n')\n for key, value in feature_imp.items():\n print('%s: %.5f' % (key, value))\n print('\\n')\n # print(model.feature_importances_)\n # use inbuilt class feature_importances of tree based classifiers\n # plot graph of feature importances for better visualization\n feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n feat_importances.nlargest(20).plot(kind='barh')\n if not os.path.exists(output_folder):\n os.makedirs(output_folder)\n fig = plt.gcf()\n fig.set_size_inches(15, 10.5)\n plt.title('Extra Trees Feature Importance')\n fig.savefig(output_folder + 'extratrees_fs.png', dpi=100)\n plt.close()\n print('saved plot in {}/{}'.format(output_folder, 'extratrees_fs.png'))\n\n def univariate(self, moph_project=False):\n ''' univariate feature selection '''\n\n if NUM_FEATURES_UNIVARIATE > self.X.shape[1]:\n raise ValueError('NUM_FEATURES_UNIVARIATE must be less than the total number of input columns.'\n '\\n. Please change line 18 in feature_selection.py')\n print('\\n********** Method 6: Univariate Feature Selection **********\\n')\n # X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)\n\n X = self.X_df\n y = self.y_df\n labels = self.labelsdict\n # labels to replace columns names by for shorter representation\n if moph_project:\n del labels[self.target_variable]\n X = X.rename(columns=self.labelsdict)\n\n output_folder = self.output_folder\n\n univariate = f_regression(X, y)\n\n # Capture P values in a series\n univariate = pd.Series(univariate[1])\n univariate.index = X.columns\n univariate.sort_values(ascending=False, inplace=True)\n # Plot the P values\n univariate.sort_values(ascending=False).plot.bar(figsize=(20, 8))\n # plt.show()\n plt.yscale(\"log\")\n plt.xticks(rotation=45)\n\n if not os.path.exists(output_folder):\n os.makedirs(output_folder)\n\n plt.title('Univariate Feature Selection')\n plt.savefig(output_folder + 'univariate_fs')\n plt.close()\n\n k_best_features = SelectKBest(f_regression, k=NUM_FEATURES_UNIVARIATE).fit(X.fillna(0), y)\n print(list(X.columns[k_best_features.get_support()]))\n\n X_train = k_best_features.transform(X.fillna(0))\n print(X_train.shape)\n\n def rfe(self):\n '''\n Recursive feature elimination\n :param k: top k features returned\n :return:\n '''\n\n if NUM_FEATURES_RFE > self.X.shape[1]:\n raise ValueError('NUM_FEATURES_RFE must be less than the total number of input columns.'\n '\\n. Please change line 19 in feature_selection.py')\n\n print('\\n********** Method 7: RFE **********\\n')\n\n X = self.X\n y = self.y\n\n estimator = Lasso()\n selector = RFE(estimator, NUM_FEATURES_RFE)\n fit = selector.fit(X, y)\n print(\"Num Features: %d\" % fit.n_features_)\n print(\"Selected Features: %s\" % fit.support_)\n print(\"Feature Ranking: %s\" % fit.ranking_)\n selected = []\n for i in range(len(fit.support_)):\n if fit.support_[i]:\n selected.append(self.feature_names[i])\n print('Selected Features: ', selected)\n\n # def pca(self):\n # '''\n # principal components analysis\n # :param k: top k components\n # :return:\n # '''\n #\n # if NUM_FEATURES_PCA > self.X.shape[1]:\n # raise ValueError('NUM_FEATURES_PCA must be less than the total number of input columns.'\n # '\\n. Please change line 20 in feature_selection.py')\n #\n # print('\\n********** Method 8: PCA **********\\n')\n #\n # X = self.X\n #\n # print(X.shape)\n #\n # df = self.df\n # print(len(df))\n #\n # # get quartiles of the data\n # desc = df[self.target_variable].describe()\n #\n # # quartiles\n # min = desc['min']\n # quart = desc['25%']\n # half = desc['50%']\n # three = desc['75%']\n # max = desc['max']\n #\n # df['group'] = pd.cut(df[self.target_variable], bins=[min, quart, half, three, max],\n # labels=['min-25%', '25%-50%', '50%-75%', '75%-max'])\n #\n # pca = PCA(n_components=NUM_FEATURES_PCA)\n # principal_components = pca.fit_transform(X)\n #\n # principalDf = pd.DataFrame(data=principal_components, columns=['PC1', 'PC2'])\n #\n # df.reset_index(drop=True, inplace=True)\n # principalDf.reset_index(drop=True, inplace=True)\n #\n # finalDf = pd.concat([principalDf, df[['group']]], axis=1)\n #\n # fig = plt.figure(figsize=(8, 8))\n # ax = fig.add_subplot(1, 1, 1)\n # ax.set_xlabel('PC1', fontsize=15)\n # ax.set_ylabel('PC2', fontsize=15)\n # ax.set_title('2 component PCA', fontsize=20)\n # targets = ['min-25%', '25%-50%', '50%-75%', '75%-max']\n # colors = ['r', 'g', 'b', 'k']\n # for target, color in zip(targets, colors):\n # indicesToKeep = finalDf['group'] == target\n # ax.scatter(finalDf.loc[indicesToKeep, 'PC1']\n # , finalDf.loc[indicesToKeep, 'PC2']\n # , c=color\n # , s=50)\n # ax.legend(targets)\n # ax.grid()\n #\n # output_folder = self.output_folder\n # if not os.path.exists(output_folder):\n # os.makedirs(output_folder)\n # plt.savefig(output_folder + 'pca_plot')\n # plt.close()\n #\n # self.pca_biplot(principal_components[:, 0:2], np.transpose(pca.components_[0:2, :]), df['group'],\n # self.feature_names)\n # fig = plt.gcf()\n # fig.set_size_inches(20, 10.5)\n # fig.savefig(output_folder + 'pca_biplot')\n # plt.close()\n #\n # def pca_biplot(self, score, coeff, y, labels=None):\n # xs = score[:, 0]\n # ys = score[:, 1]\n # n = coeff.shape[0]\n # scalex = 1.0 / (xs.max() - xs.min())\n # scaley = 1.0 / (ys.max() - ys.min())\n # plt.scatter(xs * scalex, ys * scaley, c='c')\n # for i in range(n):\n # plt.arrow(0, 0, coeff[i, 0] * 2, coeff[i, 1] * 2,\n # color='k', alpha=0.5)\n # if labels is None:\n # plt.text(coeff[i, 0] * 2.3, coeff[i, 1] * 2.3, \"Var\" + str(i + 1), color='k', fontweight='bold', ha='center',\n # va='center')\n # else:\n # plt.text(coeff[i, 0] * 2.3, coeff[i, 1] * 2.3, labels[i], color='k', fontweight='bold', ha='center', va='center')\n # plt.xlim(-1, 1)\n # plt.ylim(-1, 1)\n # plt.xlabel(\"PC{}\".format(1))\n # plt.ylabel(\"PC{}\".format(2))\n # plt.grid()\n\n def scale_cols(self):\n X = self.X\n y = self.y\n if self.input_zscore is not None:\n # apply Standard scaling to the specified columns.\n scaler = StandardScaler()\n X = X.astype('float64')\n\n X_zscaled = scaler.fit_transform(X[:, self.idx_zscore])\n\n for i in range(len(self.idx_zscore)):\n X[:, self.idx_zscore[i]] = X_zscaled[:, i]\n\n if self.scale_output:\n if self.output_zscore:\n scaler_out = StandardScaler()\n y = y.reshape(-1, 1)\n y = scaler_out.fit_transform(y)\n y = y.reshape(-1)\n\n if self.input_minmax is not None:\n # apply MinMax scaling to the specified columns.\n scaler = MinMaxScaler()\n\n if X.dtype != 'float64':\n X = X.astype('float64')\n\n X_minmaxscaled = scaler.fit_transform(X[:, self.idx_minmax])\n\n for i in range(len(self.idx_minmax)):\n X[:, self.idx_minmax[i]] = X_minmaxscaled[:, i]\n\n if self.scale_output:\n if self.output_minmax:\n scaler_out = MinMaxScaler()\n\n y = y.reshape(-1, 1)\n y = scaler_out.fit_transform(y)\n y = y.reshape(-1)\n\n if self.input_box is not None:\n # apply BoxCox transform to the specified columns.\n\n if X.dtype != 'float64':\n X = X.astype('float64')\n\n X_boxscaled = np.array([list(boxcox(X[:, self.idx_box[i]])[0]) for i in range(len(self.idx_box))]).T\n\n for i in range(len(self.idx_box)):\n X[:, self.idx_box[i]] = X_boxscaled[:, i]\n\n if self.scale_output:\n if self.output_box:\n y, _ = boxcox(y)\n\n if self.input_log is not None:\n # apply Log transform to the specified columns.\n\n if X.dtype != 'float64':\n X = X.astype('float64')\n\n X_logscaled = np.log(X[:, self.idx_log])\n\n for i in range(len(self.idx_log)):\n X[:, self.idx_log[i]] = X_logscaled[:, i]\n\n if self.scale_output:\n if self.output_log:\n y = np.log(y)\n\n return X, y\n\n", "sub_path": "Code/feature_selection/feature_selection_code.py", "file_name": "feature_selection_code.py", "file_ext": "py", "file_size_in_byte": 28241, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "warnings.filterwarnings", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 133, "usage_type": "call"}, {"api_name": "sklearn.feature_selection.VarianceThreshold", "line_number": 232, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 260, "usage_type": "call"}, {"api_name": "os.path", "line_number": 260, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 261, "usage_type": "call"}, {"api_name": "numpy.triu", "line_number": 270, "usage_type": "call"}, {"api_name": "numpy.ones_like", "line_number": 270, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 272, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 272, "usage_type": "name"}, {"api_name": "seaborn.heatmap", "line_number": 274, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 282, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 282, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 283, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 283, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 284, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 284, "usage_type": "name"}, {"api_name": "numpy.triu", "line_number": 295, "usage_type": "call"}, {"api_name": "numpy.ones_like", "line_number": 295, "usage_type": "call"}, {"api_name": "xgboost.DMatrix", "line_number": 364, "usage_type": "call"}, {"api_name": "xgboost.train", "line_number": 368, "usage_type": "call"}, {"api_name": "xgboost.plot_importance", "line_number": 378, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 380, "usage_type": "call"}, {"api_name": "os.path", "line_number": 380, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 381, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 382, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 382, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 384, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 384, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 386, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 386, "usage_type": "name"}, {"api_name": "sklearn.ensemble.ExtraTreesRegressor", "line_number": 391, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 405, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 407, "usage_type": "call"}, {"api_name": "os.path", "line_number": 407, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 408, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 409, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 409, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 411, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 411, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 413, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 413, "usage_type": "name"}, {"api_name": "sklearn.feature_selection.f_regression", "line_number": 435, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 438, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.yscale", "line_number": 444, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 444, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 445, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 445, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 447, "usage_type": "call"}, {"api_name": "os.path", "line_number": 447, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 448, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 450, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 450, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 451, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 451, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 452, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 452, "usage_type": "name"}, {"api_name": "sklearn.feature_selection.SelectKBest", "line_number": 454, "usage_type": "call"}, {"api_name": "sklearn.feature_selection.f_regression", "line_number": 454, "usage_type": "argument"}, {"api_name": "sklearn.linear_model.Lasso", "line_number": 476, "usage_type": "call"}, {"api_name": "sklearn.feature_selection.RFE", "line_number": 477, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 586, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 596, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 603, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 615, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 627, "usage_type": "call"}, {"api_name": "scipy.stats.boxcox", "line_number": 627, "usage_type": "call"}, {"api_name": "scipy.stats.boxcox", "line_number": 634, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 642, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 649, "usage_type": "call"}]} +{"seq_id": "76177798", "text": "import common\nfrom Utility.util import Version\n\nfrom Run.util import Record\nimport Algo\nimport Scene\nimport Experiment as Exp\n\nimport numpy as np\nimport time\nimport math\n\n\nclass Train:\n def __init__(self, draw=False, episodes=200, batch_size=64, max_n_step=50):\n self.max_n_step = max_n_step\n self.draw = draw\n self.episodes = episodes\n self.batch_size = batch_size\n\n def __call__(self, context):\n self.env = context.env\n self.scene = context.scene\n self.agent = context.agent\n self._train = self.train_off_policy if self.agent.off_policy else self.train_on_policy\n self.path = context.path(DataPath.train)\n\n total_task_size = self.episodes * self.scene.n_scenarios\n # self.task_r = Record(total_task_size, self.path.task_record_file, {\n # 'id': int, 'n_step': int, 'outcome': int, 'n_learn': int,\n # 'total_reward': float, 'average_reward': float,\n # 'total_actor_loss': float, 'average_actor_loss': float,\n # 'total_critic_loss': float, 'average_critic_loss': float})\n self.outcomes = np.zeros(self.env.n_outcomes, np.int)\n time_start = time.time()\n self.current_step = 0\n for episode in range(self.episodes):\n print('current episode: ', episode)\n for task_id, scenario in enumerate(self.scene.scenarios):\n self._train(scenario, (episode * self.scene.n_scenarios + task_id) / total_task_size)\n run_time = time.time() - time_start\n with open(self.path.record_file, 'a') as fp:\n s = \"date: {}\\n\" \\\n \"time: {}\\n\" \\\n \"step: {}\\n\" \\\n \"n_scenes: {}\\n\" \\\n \"episode: {}\\n\" \\\n \"outcome: {}\\n\"\n s = s.format(time.ctime(), run_time, self.current_step, self.scene.n_scenarios, self.episodes, self.outcomes)\n fp.write(s)\n print(s)\n # self.task_r.save()\n print('train end\\n\\n\\n')\n return self\n\n def train_off_policy(self, scenario, task_percentage):\n observation = self.env.reset(scenario)\n # self.task_r.init() # __enter__\n for i in range(self.max_n_step):\n action = self.agent.sample(observation, task_percentage=task_percentage)\n # print(\"goal_dis: {}, goal_dir: {}\".format(self.env.goal_dis, self.env.goal_dir))\n observation_, reward, done = self.env.step(action)\n # print(\"out: {}, reward: {}\".format(out, reward))\n # self.task_r.total_reward += reward\n if self.draw:\n self.env.render(sleep_time=0.001, show_arrow=True, show_scope=False)\n self.agent.store_exp(observation, action, reward, observation_, done)\n if self.current_step > self.batch_size and self.current_step % 5 == 0:\n c_loss, a_loss = self.agent.learn(self.batch_size)\n # self.task_r.total_critic_loss += c_loss\n # self.task_r.total_actor_loss += a_loss\n # self.task_r.n_learn += 1\n # if self.current_step % 100 == 0:\n # print('c_loss: {0:10f}, a_loss: {1:10f}'.format(c_loss, a_loss))\n observation = observation_\n self.current_step += 1\n if done:\n # self.task_r.n_step = i + 1\n break\n # else:\n # self.task_r.n_step = self.max_n_step\n self.outcomes[self.env.result] += 1\n # self.task_r.outcome = self.env.result\n # self.task_r.average_reward = self.task_r.total_reward / self.task_r.n_step\n # self.task_r.average_actor_loss = 0. if self.task_r.n_learn == 0 else self.task_r.total_actor_loss / self.task_r.n_learn\n # self.task_r.average_critic_loss = 0. if self.task_r.n_learn == 0 else self.task_r.total_critic_loss / self.task_r.n_learn\n # self.task_r.inc() # __exit__ 必须放在最后\n\n def train_on_policy(self, scenario, task_percentage):\n observation = self.env.reset(scenario)\n self.agent.reset()\n # self.task_r.init() # __enter__\n for i in range(self.max_n_step):\n # print(observation)\n action = self.agent.sample(observation)\n observation_, reward, done = self.env.step(action)\n # self.task_r.total_reward += reward\n if self.draw:\n self.env.render(sleep_time=0.01, show_arrow=True, show_scope=True)\n c_loss, a_loss = self.agent.learn(observation, action, reward, observation_, done)\n # self.task_r.total_critic_loss += c_loss\n # self.task_r.total_actor_loss += a_loss\n # self.task_r.n_learn += 1\n observation = observation_\n self.current_step += 1\n if done:\n # self.task_r.n_step = i + 1\n break\n # else:\n # self.task_r.n_step = self.max_n_step\n self.outcomes[self.env.result] += 1\n # self.task_r.outcome = self.env.result\n # self.task_r.average_reward = self.task_r.total_reward / self.task_r.n_step\n # self.task_r.average_actor_loss = 0. if self.task_r.n_learn == 0 else self.task_r.total_actor_loss / self.task_r.n_learn\n # self.task_r.average_critic_loss = 0. if self.task_r.n_learn == 0 else self.task_r.total_critic_loss / self.task_r.n_learn\n # self.task_r.inc() # __exit__ 必须放在最后\n\n\nclass Predict:\n def __init__(self, draw=True, draw_rate=0.2, test_predict=True, pick=False, debug=False, max_n_step=50, save=False):\n self.save = save\n self.draw = draw\n self.draw_rate = draw_rate # 只绘制前百分之xxx\n self.max_n_step = max_n_step\n self.pick = pick\n self.debug = debug\n self.test_predict = test_predict\n self._run = self._pick_task if pick else self.predict\n\n def __call__(self, context):\n self.scene = context.scene\n self.env = context.env\n self.agent = context.agent\n self.env = context.env\n self.path = context.path(DataPath.test_predict if self.test_predict else DataPath.train_predict)\n self._run()\n return self\n\n def predict(self):\n time_start = time.time()\n # 记录信息：步数，路程，角速率切换，切换次数，最大切换角速率，结果\n if self.save:\n task_r = Record(self.scene.n_scenarios, self.path.task_record_file, {\n 'id': int, 'n_step': int, 'outcome': int,\n 'total_reward': float, 'average_reward': float,\n 'total_distance': float, 'total_time': float, 'linear_velocity': float,\n 'total_change_yaw_rate': float, 'average_change_yaw_rate': float, 'max_change_yaw_rate': float,\n 'total_yaw_rate': float, 'average_yaw_rate': float, 'max_yaw_rate': float,\n 'total_turning_angle': float, 'average_turning_angle': float, 'max_turning_angle': float})\n outcomes = np.zeros(self.env.n_outcomes, np.int)\n for scenario in self.scene.scenarios:\n observation = self.env.reset(scenario)\n # task_r.init() # __enter__\n for i in range(self.max_n_step):\n action = self.agent.predict(observation)\n observation_, reward, done = self.env.step(action)\n if self.save:\n task_r.total_reward += reward\n task_r.total_distance += self.env.delta_dis\n task_r.total_time += self.env.delta_time\n task_r.total_change_yaw_rate += abs(self.env.delta_yaw_rate)\n if abs(self.env.delta_yaw_rate) > abs(task_r.max_change_yaw_rate):\n task_r.max_change_yaw_rate = self.env.delta_yaw_rate\n task_r.total_yaw_rate += abs(self.env.yaw_rate)\n if abs(self.env.yaw_rate) > abs(task_r.max_yaw_rate):\n task_r.max_yaw_rate = self.env.yaw_rate\n task_r.total_turning_angle += abs(self.env.delta_dir)\n if abs(self.env.delta_dir) > abs(task_r.max_turning_angle):\n task_r.max_turning_angle = self.env.delta_dir\n if self.save:\n if self.draw and task_r.count < self.draw_rate*self.scene.n_scenarios:\n self.env.render(sleep_time=0.01, show_arrow=False, show_scope=True, show_pos=True)\n else:\n self.env.render(sleep_time=0.01, show_arrow=False, show_scope=False, show_pos=True)\n observation = observation_\n if done:\n if self.save:\n task_r.n_step = i + 1\n break\n else:\n if self.save:\n task_r.n_step = self.max_n_step\n outcomes[self.env.result] += 1\n # task_r.total_turning_angle = math.degrees(task_r.total_turning_angle)\n # task_r.max_turning_angle = math.degrees(task_r.max_turning_angle)\n if self.save:\n task_r.average_turning_angle = task_r.total_turning_angle / task_r.n_step # change_times\n task_r.average_change_yaw_rate = task_r.total_change_yaw_rate / task_r.n_step # change_times\n task_r.average_yaw_rate = task_r.total_yaw_rate / task_r.n_step # change_times\n task_r.average_reward = task_r.total_reward / task_r.n_step\n task_r.outcome = self.env.result\n task_r.linear_velocity = self.env.action.linear_velocity\n task_r.inc() # __exit__ 必须放在最后\n\n run_time = time.time() - time_start\n if self.save:\n with open(self.path.record_file, 'a') as fp: # 这里还应该将Record中的记录项保存起来，否则不方便读取保存的record\n s = \"date: {}\\n\" \\\n \"time: {}\\n\" \\\n \"n_scenes: {}\\n\" \\\n \"outcome: {}\\n\"\n s = s.format(time.ctime(), run_time, self.scene.n_scenarios, outcomes)\n fp.write(s)\n print(s)\n task_r.save()\n setattr(self, 'task_r', task_r) # 用于数据后续处理\n print('predict end\\n\\n\\n')\n\n def _pick_task(self):\n # while True:\n # task_id = int(input('scene_id: '))\n for task_id in range(100):\n if task_id < 0 or task_id >= self.scene.n_scenarios:\n break\n scenario = self.scene.scenarios[task_id]\n observation = self.env.reset(scenario)\n for i in range(self.max_n_step):\n if self.debug:\n print(\"step: {}\".format(i))\n print(\"obs_dis: {}\".format(self.env.obs_distances))\n print(\"goal_dis: {}, goal_dir: {}\".format(self.env.goal_dis, self.env.goal_dir))\n action = self.agent.predict(observation)\n observation_, reward, done = self.env.step(action)\n if self.debug:\n print(\"reward: {}, out: {}\".format(reward, action))\n input('continue')\n # if i % 5 == 0:\n self.env.render(sleep_time=0.01, show_arrow=False, show_scope=True, show_pos=True, circle_scope=False)\n # else:\n # self.env.render(sleep_time=0.01, show_arrow=False, show_scope=False, show_pos=True)\n\n observation = observation_\n if done:\n if self.env.result == self.env.success:\n print('success')\n self.env.render_trajectory(self.env.residual_traj, show_pos=False)\n break\n # input('screenshot: start')\n # time.sleep(2)\n self.env.canvas.screenshot(self.path.picked_image_dir.join(str(task_id)))\n print('save screenshot: {}'.format(task_id))\n\n\nclass DataPath:\n \"\"\"调用顺序：首先是__init__, 其次是model_load_file(如果需要), 然后是__call__, 最后是model_save_file(如果需要)\"\"\"\n train, train_predict, test_predict = 'train_', 'train_pred_', 'test_pred_' # run_type\n\n def __init__(self, root_dir, run_type=None):\n \"\"\"root_dir: 存放某次运行产生的数据的目录\"\"\"\n self.root_dir = root_dir\n self.shared_dir = self.root_dir('shared_data')\n self.v = Version(self.shared_dir)\n if run_type is not None: self.run_type = run_type\n\n def __call__(self, run_type):\n \"\"\"创建与当前运行类型相关的数据文件\"\"\"\n v_str = self.v.version_plusone_str if run_type == self.train else self.v.version_str\n self.private_dir = self.root_dir(run_type + v_str)\n # self.image_dir = self.private_dir('image')\n self.picked_image_dir = self.private_dir('picked_image')\n self.task_record_file = self.private_dir.join('task_record')\n self.record_file = self.private_dir.join('record')\n # self.loss_file = self.private_dir.join('loss')\n # self.reward_file = self.private_dir.join('reward')\n # self.outcome_file = self.private_dir.join('outcome')\n return self\n\n @property\n def model_save_file(self):\n return self.shared_dir.join('model' + self.v.version_str_plusplus)\n\n @property\n def model_load_file(self):\n return self.shared_dir.join('model' + self.v.version_str)\n\n @property\n def latest_model_file(self):\n return self.shared_dir.join('model' + str(self.v.version-1)) # 加载最新版本\n\n def __iter__(self):\n self.iter_stop = self.v.version\n self.v.version = 1\n return self\n\n def __next__(self):\n if self.v.version <= self.iter_stop:\n self.__call__(self.run_type)\n self.v.version += 1\n return self\n else:\n self.v.version = self.iter_stop # 复原\n raise StopIteration\n\n def __len__(self):\n \"\"\"迭代过程中禁用此函数\"\"\"\n return self.v.version\n\n\nclass Context:\n \"\"\"某次运行中的某轮运行(Train or Test)所依赖的所有资源，Context实例将沿着各轮运行构成的序列传递\"\"\"\n def __init__(self, agent, env, scene, path, eagerly=False):\n self.agent = agent # 还未build\n self.env = env\n self.scene = scene\n self.path = path\n self.eagerly = eagerly\n\n def dispatch(self, call):\n return call() if self.eagerly else call\n\n \"\"\"场景的上文控制：默认是复用场景\"\"\"\n def load_scene(self, *args, **kwargs):\n def _load_scene():\n self.scene.load_scene(*args, **kwargs) # 延迟加载是必要的\n return self.dispatch(_load_scene)\n\n \"\"\"模型的上文控制：默认是复用模型\"\"\"\n def build_model(self):\n return self.dispatch(self.agent.build_model)\n\n def load_model(self, model_load_name=None):\n \"\"\"目前还不支持tf静态图模式，原因是优化器没有重建，而模型重建后，各层的name不一样了; 不支持静态图模式，存在全局数据\"\"\"\n def _load_model():\n load_file = self.path.model_load_file if model_load_name is None else self.path.shared_dir.join(model_load_name)\n self.agent.load_model(load_file)\n return self.dispatch(_load_model)\n\n \"\"\"模型的下文控制：默认是不保存模型\"\"\"\n def save_model(self, model_save_name=None):\n def _save_model():\n save_file = self.path.model_save_file if model_save_name is None else self.path.shared_dir.join(model_save_name)\n self.agent.save_model(save_file)\n return self.dispatch(_save_model)\n\n\nclass RunUnit:\n \"\"\"执行流程：首先是above_ctrl，其次是execute，最后是below_ctrl；操作对象都是context\"\"\"\n def __init__(self, execute, context, above_ctrl, below_ctrl):\n self.context = context\n self.execute = execute\n self.above_ctrl = above_ctrl\n self.below_ctrl = below_ctrl\n\n def __call__(self):\n # 处理上文，复用上文数据\n for ctrl in self.above_ctrl:\n ctrl()\n # 运行\n self.execute(self.context)\n # 处理下文，是否保存数据\n for ctrl in self.below_ctrl:\n ctrl()\n\n\nclass RunSequence:\n \"\"\"管理某次运行中的各轮运行\"\"\"\n def __init__(self, context, init_ctrl=None):\n self.context = context\n self.sequence = [] if init_ctrl is None else init_ctrl\n\n def append(self, execute, above_ctrl=None, below_ctrl=None):\n self.sequence.append(RunUnit(execute, self.context,\n [] if above_ctrl is None else above_ctrl,\n [] if below_ctrl is None else below_ctrl))\n\n def __call__(self, mode):\n start, end = mode\n return [_run() for _run in self.sequence[start:end + 1]]\n\n\ndef run(args):\n print('run')\n from Env.flight import Flight\n env = Flight(max_detect_range=120., min_turning_radius=10., detect_angle_interval=5, safe_dis=0.,\n use_border=True)\n \"\"\"选择算法，获取算法对象和可选的Sp分派器，获得Sp和可选的Model分派器，最终生成agent\"\"\"\n algo = Algo.AlgoDispatch(args.algo_t, args.model_t)\n supervisor = algo(buffer_size=500, gamma=0.9) # , tau=0.01\n model = supervisor(env.d_states, env.action.n_actions, critic_lr=0.001) # 学习率比较关键\n agent = model(size_splits=env.d_states_detail, actor_n_layers=(20, 10), critic_n_layers=(20, 10), n_filters=5,\n state_n_layers=(20, 10))\n\n exp_dir = Exp.experiment_path(args)\n v = Version(exp_dir)\n setattr(args, 'start_times', v.version) # 记录中的起始运行次数\n Exp.record_args(args)\n root_dir = exp_dir(v.version_str_plusplus)\n # result_path = Exp.experiment_path(args)\n # root_dir = result_path(Version(result_path).version_str_plusplus)\n context = Context(agent, env, Scene.ScenarioLoader(), DataPath(root_dir)) # 一次运行的上下文\n\n draw = common.use_windows\n\n seq = RunSequence(context)\n\n train = Train(draw=False, episodes=args.episodes)\n seq.append(train, [context.build_model(), context.load_scene(**Scene.easy_task4x40)], [context.save_model()])\n test = Predict(draw=draw, draw_rate=0.1, test_predict=False)\n seq.append(test)\n test = Predict(draw=draw, draw_rate=0.15)\n seq.append(test, [context.load_scene(**Scene.hard_task4x100)])\n\n train = Train(draw=False, episodes=args.episodes)\n seq.append(train, [context.load_scene(**Scene.hard_task4x50)], [context.save_model()])\n test = Predict(draw=draw, draw_rate=0.1, test_predict=False)\n seq.append(test)\n test = Predict(draw=draw, draw_rate=0.15)\n seq.append(test, [context.load_scene(**Scene.hard_task4x100)])\n\n # M_TRAIN, M_TRAIN_TEST, M_TEST, M_RETRAIN, M_RETRAIN_RETEST, M_RETEST, M_ALL, M_DEBUG, M_PICK = \\\n # (0, 1), (0, 2), (2, 2), (3, 4), (3, 5), (5, 5), (0, 5), (6, 6), (7, 7) # 基本任务的组合模式\n mode_map = {'train': (0, 1), 'train_pred': (0, 2), 'test': (2, 2), 'retrain': (3, 4), 'retrain_pred': (3, 5),\n 'retest': (5, 5), 'all': (0, 5), 'pick': (6, 6), 'debug': (7, 7)}\n seq(mode_map[args.run_m])\n\n\ndef tst_cyclic(agent, env, args):\n exp_dir = Exp.experiment_path(args)\n times = args.times\n setattr(args, 'start_times', times) # 记录中的起始运行次数\n Exp.record_args(args)\n v = Version(exp_dir)\n root_dir = exp_dir(v.latest_version_str) # str(times)\n context = Context(agent, env, Scene.ScenarioLoader(), DataPath(root_dir), eagerly=True) # 立即执行\n\n if args.obstacle_type == 'C': # circle\n train_task = Scene.circle_train_task4x200\n test_task = Scene.circle_test_task4x100\n else:\n train_task = Scene.line_train_task4x200\n test_task = Scene.line_test_task4x100\n draw = common.use_windows\n # draw = False\n context.load_model('model0')\n # Predict(draw=draw, draw_rate=0.03, test_predict=False, max_n_step=args.max_n_step)(context)\n context.load_scene(**test_task)\n Predict(draw=draw, draw_rate=1., max_n_step=args.max_n_step, save=True)(context)\n\n\ndef run_cyclic(agent, env, args):\n exp_dir = Exp.experiment_path(args)\n v = Version(exp_dir)\n setattr(args, 'start_times', v.version) # 记录中的起始运行次数\n Exp.record_args(args)\n root_dir = exp_dir(v.version_str_plusplus)\n context = Context(agent, env, Scene.ScenarioLoader(), DataPath(root_dir), eagerly=True) # 立即执行\n context.build_model()\n\n if args.obstacle_type == 'C': # circle\n train_task = Scene.circle_train_task4x200\n test_task = Scene.circle_test_task4x100\n else:\n train_task = Scene.line_train_task4x200\n test_task = Scene.line_test_task4x100\n draw = common.use_windows\n # draw = False\n for i in range(args.rounds): # 运行轮数\n print('current round: ', i)\n context.load_scene(**train_task, percentage=args.percentage) # Scene.hard_task4x50\n Train(draw=False, episodes=args.episodes, max_n_step=args.max_n_step)(context)\n context.save_model()\n # Predict(draw=draw, draw_rate=0.03, test_predict=False, max_n_step=args.max_n_step)(context)\n context.load_scene(**test_task)\n Predict(draw=draw, draw_rate=0.01, max_n_step=args.max_n_step, save=True)(context)\n\n\ndef run_baseline(args):\n args.env_t = 'baseline'\n args.algo_t = Algo.dqn\n args.model_t = Algo.M_CNN\n\n print('run_baseline')\n print(args.__dict__)\n\n from Env.flight import Flight, DiscreteAction\n env = Flight(DiscreteAction(45, 10., math.pi / 60), max_detect_range=120., detect_angle_interval=5, safe_dis=0.,\n use_border=True)\n algo = Algo.AlgoDispatch(args.algo_t, args.model_t)\n supervisor = algo(buffer_size=500, gamma=0.9) # , tau=0.01\n model = supervisor(env.d_states, env.action.n_actions, critic_lr=0.001)\n agent = model(size_splits=env.d_states_detail, actor_n_layers=(20, 10), critic_n_layers=(20, 20, 20), n_filters=5,\n state_n_layers=(20,))\n\n if args.is_train:\n run_cyclic(agent, env, args)\n else:\n tst_cyclic(agent, env, args)\n\n\nif __name__ == \"__main__\":\n class Args:\n def __init__(self):\n self.is_train = False\n self.obstacle_type = 'C' # 'C':circle; 'L': line\n self.episodes = 10 # 10\n self.times = 1\n self.rounds = 1 # 4\n self.percentage = 0.25 # 使用训练场景的前百分之几\n self.max_n_step = 100\n args = Args()\n run_baseline(args)\n\n", "sub_path": "Run/run.py", "file_name": "run.py", "file_ext": "py", "file_size_in_byte": 22722, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.zeros", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 34, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 35, "usage_type": "call"}, {"api_name": "time.time", "line_number": 41, "usage_type": "call"}, {"api_name": "time.ctime", "line_number": 49, "usage_type": "call"}, {"api_name": "time.time", "line_number": 140, "usage_type": "call"}, {"api_name": "Run.util.Record", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 150, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 195, "usage_type": "call"}, {"api_name": "time.ctime", "line_number": 202, "usage_type": "call"}, {"api_name": "Utility.util.Version", "line_number": 252, "usage_type": "call"}, {"api_name": "Env.flight.Flight", "line_number": 374, "usage_type": "call"}, {"api_name": "Algo.AlgoDispatch", "line_number": 377, "usage_type": "call"}, {"api_name": "Experiment.experiment_path", "line_number": 383, "usage_type": "call"}, {"api_name": "Utility.util.Version", "line_number": 384, "usage_type": "call"}, {"api_name": "Experiment.record_args", "line_number": 386, "usage_type": "call"}, {"api_name": "Scene.ScenarioLoader", "line_number": 390, "usage_type": "call"}, {"api_name": "common.use_windows", "line_number": 392, "usage_type": "attribute"}, {"api_name": "Scene.easy_task4x40", "line_number": 397, "usage_type": "attribute"}, {"api_name": "Scene.hard_task4x100", "line_number": 401, "usage_type": "attribute"}, {"api_name": "Scene.hard_task4x50", "line_number": 404, "usage_type": "attribute"}, {"api_name": "Scene.hard_task4x100", "line_number": 408, "usage_type": "attribute"}, {"api_name": "Experiment.experiment_path", "line_number": 418, "usage_type": "call"}, {"api_name": "Experiment.record_args", "line_number": 421, "usage_type": "call"}, {"api_name": "Utility.util.Version", "line_number": 422, "usage_type": "call"}, {"api_name": "Scene.ScenarioLoader", "line_number": 424, "usage_type": "call"}, {"api_name": "Scene.circle_train_task4x200", "line_number": 427, "usage_type": "attribute"}, {"api_name": "Scene.circle_test_task4x100", "line_number": 428, "usage_type": "attribute"}, {"api_name": "Scene.line_train_task4x200", "line_number": 430, "usage_type": "attribute"}, {"api_name": "Scene.line_test_task4x100", "line_number": 431, "usage_type": "attribute"}, {"api_name": "common.use_windows", "line_number": 432, "usage_type": "attribute"}, {"api_name": "Experiment.experiment_path", "line_number": 441, "usage_type": "call"}, {"api_name": "Utility.util.Version", "line_number": 442, "usage_type": "call"}, {"api_name": "Experiment.record_args", "line_number": 444, "usage_type": "call"}, {"api_name": "Scene.ScenarioLoader", "line_number": 446, "usage_type": "call"}, {"api_name": "Scene.circle_train_task4x200", "line_number": 450, "usage_type": "attribute"}, {"api_name": "Scene.circle_test_task4x100", "line_number": 451, "usage_type": "attribute"}, {"api_name": "Scene.line_train_task4x200", "line_number": 453, "usage_type": "attribute"}, {"api_name": "Scene.line_test_task4x100", "line_number": 454, "usage_type": "attribute"}, {"api_name": "common.use_windows", "line_number": 455, "usage_type": "attribute"}, {"api_name": "Algo.dqn", "line_number": 469, "usage_type": "attribute"}, {"api_name": "Algo.M_CNN", "line_number": 470, "usage_type": "attribute"}, {"api_name": "Env.flight.Flight", "line_number": 476, "usage_type": "call"}, {"api_name": "Env.flight.DiscreteAction", "line_number": 476, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 476, "usage_type": "attribute"}, {"api_name": "Algo.AlgoDispatch", "line_number": 478, "usage_type": "call"}]} +{"seq_id": "483024898", "text": "import os\n\nfrom setuptools import setup, find_packages\nimport versioneer\n\n\ndef read(file_name):\n \"\"\"\n Read the contents of a text file and return its content.\n\n :param str file_name: The name of the file to read.\n\n :return: The content of the text file.\n :rtype: str\n \"\"\"\n return open(\n os.path.join(os.path.dirname(__file__), file_name),\n encoding='utf-8'\n ).read()\n\n\nsetup(\n name='audiorename',\n version=versioneer.get_version(),\n cmdclass=versioneer.get_cmdclass(),\n author='Josef Friedrich',\n author_email='josef@friedrich.rocks',\n description=('Rename audio files from metadata tags.'),\n license='MIT',\n packages=find_packages(),\n keywords='audio',\n url='https://github.com/Josef-Friedrich/audiorename',\n install_requires=[\n 'phrydy>=1.2.0',\n 'tmep>=2.0.0',\n 'ansicolor',\n 'six',\n 'musicbrainzngs',\n ],\n scripts=['bin/audiorenamer'],\n long_description=read('README.rst'),\n classifiers=[\n 'Development Status :: 3 - Alpha',\n ],\n zip_safe=False, )\n", "sub_path": "pypi_install_script/audiorename-2.1.0.tar/setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1082, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 17, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 22, "usage_type": "call"}, {"api_name": "versioneer.get_version", "line_number": 24, "usage_type": "call"}, {"api_name": "versioneer.get_cmdclass", "line_number": 25, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "277987286", "text": "\"\"\"wikipedia module\"\"\"\nimport wikipedia\n\n\nclass Wiki:\n \"\"\"Class to call the wikipedia api\"\"\"\n\n def __init__(self):\n wikipedia.set_lang(\"fr\")\n\n def get_wiki_result(self, lat, lng, question):\n \"\"\"Return the summary and the url of the wikipedia page searched\"\"\"\n try:\n wiki_page = wikipedia.page(question)\n\n return {\n \"summary\": wiki_page.summary[:500],\n \"url\": wiki_page.url\n }\n\n except wikipedia.exceptions.PageError:\n return \"no result\"\n\n except wikipedia.exceptions.DisambiguationError:\n try:\n wiki_search = wikipedia.geosearch(lat, lng, question)\n wiki_page = wikipedia.page(wiki_search[0])\n\n return {\n \"summary\": wiki_page.summary[:500],\n \"url\": wiki_page.url\n }\n\n except IndexError:\n return \"no result\"\n\n except wikipedia.exceptions.DisambiguationError:\n return \"no result\"\n", "sub_path": "grandpy/apiwiki.py", "file_name": "apiwiki.py", "file_ext": "py", "file_size_in_byte": 1057, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "wikipedia.set_lang", "line_number": 9, "usage_type": "call"}, {"api_name": "wikipedia.page", "line_number": 14, "usage_type": "call"}, {"api_name": "wikipedia.exceptions", "line_number": 21, "usage_type": "attribute"}, {"api_name": "wikipedia.exceptions", "line_number": 24, "usage_type": "attribute"}, {"api_name": "wikipedia.geosearch", "line_number": 26, "usage_type": "call"}, {"api_name": "wikipedia.page", "line_number": 27, "usage_type": "call"}, {"api_name": "wikipedia.exceptions", "line_number": 37, "usage_type": "attribute"}]} +{"seq_id": "187764911", "text": "from pyspark import SparkContext\nfrom pyspark.streaming import StreamingContext\nssc = StreamingContext(sc, 1)\n\n# Create a DStream that will connect to hostname:port, like localhost:9999\nlines_RDD = ssc.socketTextStream(\"localhost\", 8888)\n\n# Split each line into words\ndata_RDD = lines_RDD.flatMap(lambda line: line.split(\",\"))\n\ndata_RDD.pprint()\n\nssc.start() # Start the computation\nssc.awaitTermination() # Wait for the computation to terminate\n", "sub_path": "receiver/legacy_shovel/receiver_spark.py", "file_name": "receiver_spark.py", "file_ext": "py", "file_size_in_byte": 459, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "pyspark.streaming.StreamingContext", "line_number": 3, "usage_type": "call"}]} +{"seq_id": "573912548", "text": "import sys\r\nimport math\r\nfrom collections import OrderedDict\r\nfrom collections import defaultdict\r\nimport operator\r\nimport pandas as pd\r\nfrom sklearn.metrics import confusion_matrix\r\nfrom sklearn.metrics import accuracy_score\r\nfrom tqdm import tqdm\r\n\r\ndef first_pass(training_file, all_features, occurrence_of_class):\r\n with open(training_file, \"r\") as rfp:\r\n line = rfp.readline()\r\n\r\n while line:\r\n elements = line.strip().split() ## a list of elements in line\r\n \r\n class_name = elements[0] ## this first element is the class name\r\n \r\n if class_name not in occurrence_of_class:\r\n occurrence_of_class[class_name] = [1, 0.0, 0.0]\r\n else:\r\n occurrence_of_class[class_name][0] = occurrence_of_class[class_name][0] + 1\r\n \r\n list_of_features = elements[1:]\r\n\r\n for feature_pair in list_of_features:\r\n \r\n feature = feature_pair.split(':')[0]\r\n\r\n if feature not in all_features:\r\n all_features[feature] = True\r\n\r\n line = rfp.readline()\r\n\r\ndef second_pass(training_file, training_data_structure, all_features, occurrence_of_class, \r\n z_dictionary):\r\n with open(training_file, \"r\") as rfp:\r\n line = rfp.readline()\r\n \r\n for class_name in occurrence_of_class:\r\n \r\n if class_name not in training_data_structure:\r\n \r\n z_dictionary[class_name] = 0\r\n training_data_structure[class_name] = {} ## create a new dictionary\r\n \r\n for possible_feature in all_features:\r\n ## the key will be a list of 3 elements:\r\n ## occurrence, prob and log_prob\r\n training_data_structure[class_name][possible_feature] = [0, 0.0, 0.0] \r\n \r\n while line:\r\n elements = line.strip().split() ## a list of elements in line\r\n \r\n class_name = elements[0] ## this first element is the class name\r\n \r\n list_of_features = elements[1:]\r\n \r\n for feature_pair in list_of_features:\r\n \r\n feature_occurrence_pair = feature_pair.split(':')\r\n \r\n feature = feature_occurrence_pair[0]\r\n \r\n occurrence = int(feature_occurrence_pair[1])\r\n \r\n training_data_structure[class_name][feature][0] += occurrence\r\n \r\n z_dictionary[class_name] += occurrence\r\n\r\n line = rfp.readline() \r\n \r\ndef read_training_data(training_file):\r\n training_data_structure = {} ## this is a dictionary of dictionaries\r\n \r\n all_features = {} ## this is a dictionary that maps all the features to True\r\n \r\n ## this is a dictionary that maps the class_name to \r\n ## a list of 3 elements: first one is the total number of training instances that \r\n ## have the class_name, second one is the prob, and third one is the log10 prob\r\n \r\n occurrence_of_class = {}\r\n \r\n z_dictionary = {}\r\n\r\n first_pass(training_file, all_features, occurrence_of_class)\r\n\r\n second_pass(training_file, training_data_structure, all_features, occurrence_of_class, z_dictionary)\r\n\r\n return training_data_structure, all_features, occurrence_of_class, z_dictionary\r\n\r\ndef count_feature(feature_dictionary, feature):\r\n return feature_dictionary[feature][0]\r\n \r\ndef output_model_file(training_data_structure, model_file, class_prior_delta, cond_prob_delta,\r\n all_features, occurrence_of_class, z_dictionary):\r\n with open(model_file, \"w\") as wfp:\r\n wfp.write('%%%%% prior prob P(c) %%%%%\\n')\r\n \r\n total_class_instance_count = 0\r\n \r\n number_of_classes = len(training_data_structure)\r\n \r\n for class_name in training_data_structure:\r\n \r\n ## return the total number of counts \r\n count = occurrence_of_class[class_name][0]\r\n total_class_instance_count = total_class_instance_count + count\r\n\r\n for class_name in training_data_structure:\r\n count_c_i = occurrence_of_class[class_name][0]\r\n prob = float(class_prior_delta + count_c_i) / float(class_prior_delta * number_of_classes + total_class_instance_count)\r\n log_prob = math.log10(prob)\r\n wfp.write(class_name + '\\t' + str(prob) + '\\t' + str(log_prob) + '\\n')\r\n \r\n occurrence_of_class[class_name][1] = prob\r\n occurrence_of_class[class_name][2] = log_prob\r\n \r\n wfp.write('%%%%% conditional prob P(f|c) %%%%%\\n')\r\n \r\n all_features = OrderedDict(sorted(all_features.items()))\r\n \r\n for class_name in training_data_structure: \r\n \r\n wfp.write('%%%%% conditional prob P(f|c) c=' + class_name + ' %%%%%\\n') \r\n \r\n feature_dictionary = training_data_structure[class_name]\r\n \r\n class_count = occurrence_of_class[class_name][0]\r\n \r\n for feature in all_features:\r\n \r\n feature_count = count_feature(feature_dictionary, feature)\r\n \r\n prob = float(cond_prob_delta + feature_count) / float(cond_prob_delta * len(all_features) + z_dictionary[class_name])\r\n \r\n log_prob = math.log10(prob)\r\n \r\n wfp.write(feature + '\\t' + class_name + '\\t' + str(prob) + '\\t' + str(log_prob) + '\\n')\r\n \r\n training_data_structure[class_name][feature][1] = prob\r\n training_data_structure[class_name][feature][2] = log_prob\r\n \r\n\r\ndef write_to_sys_file(predict, i, wfp, predicted_class_name):\r\n \r\n ## adjust the ratio\r\n \r\n adjust_predict = {}\r\n \r\n ## this is the base to adjust with\r\n\r\n base = predict[predicted_class_name] \r\n \r\n for class_name in predict:\r\n if class_name not in adjust_predict:\r\n adjust_predict[class_name] = predict[class_name] - base\r\n \r\n sum_prob = 0.0\r\n \r\n output_dictionary = {}\r\n\r\n for class_name in adjust_predict:\r\n sum_prob = sum_prob + pow(10, adjust_predict[class_name])\r\n\r\n for class_name in adjust_predict:\r\n prob = pow(10, adjust_predict[class_name]) / sum_prob\r\n\r\n if class_name not in output_dictionary:\r\n output_dictionary[class_name] = prob\r\n\r\n sorted_dictionary = sorted(output_dictionary.items(), key=operator.itemgetter(1), reverse=True)\r\n \r\n string_to_write = ''\r\n \r\n for element in sorted_dictionary:\r\n string_to_write = string_to_write + element[0] + '\\t' + str(element[1]) + '\\t'\r\n \r\n wfp.write('array:' + str(i) + '\\t' + predicted_class_name + '\\t' + string_to_write + '\\n')\r\n \r\n \r\ndef classify(training_data_structure, all_features, occurrence_of_class, wfp, training_file, type):\r\n \r\n wfp.write('%%%%% training data:\\n')\r\n \r\n y_true = []\r\n y_pred = []\r\n \r\n with open(training_file, \"r\") as rfp:\r\n \r\n ## i is the index number\r\n \r\n for i, line in enumerate(tqdm(rfp)): \r\n \r\n elements = line.strip().split() ## a list of elements in line\r\n \r\n claimed_class_name = elements[0] ## this first element is the claimed class name\r\n \r\n list_of_elements = elements[1:]\r\n \r\n ## map each feature to its occurrence\r\n \r\n target_feature = {}\r\n \r\n for pair in list_of_elements:\r\n feature_occurrence_pair = pair.split(':')\r\n feature = feature_occurrence_pair[0]\r\n occurrence = int(feature_occurrence_pair[1])\r\n target_feature[feature] = occurrence\r\n \r\n predicted_class_name = None\r\n \r\n ## a dictionary that maps a class_name to \r\n ## its log_classify_val\r\n\r\n predict = {} \r\n \r\n for class_name in training_data_structure:\r\n \r\n class_prob = 0\r\n \r\n stored_features = training_data_structure[class_name]\r\n \r\n num_times_wk_appears_in_di = 0\r\n \r\n for feature in stored_features:\r\n \r\n if feature in target_feature:\r\n num_times_wk_appears_in_di = target_feature[feature]\r\n else:\r\n num_times_wk_appears_in_di = 0\r\n\r\n class_prob = class_prob + (num_times_wk_appears_in_di * stored_features[feature][2])\r\n\r\n class_prob = class_prob + occurrence_of_class[class_name][2]\r\n\r\n if class_name not in predict:\r\n predict[class_name] = class_prob \r\n \r\n \r\n predicted_class_name = max(predict.items(), key = operator.itemgetter(1))[0]\r\n y_true.append(claimed_class_name)\r\n y_pred.append(predicted_class_name) \r\n \r\n write_to_sys_file(predict, i, wfp, predicted_class_name)\r\n \r\n sorted_list = sorted(occurrence_of_class.items(), key=lambda x: x[0])\r\n\r\n label_list = []\r\n\r\n for element in sorted_list:\r\n label_list.append(element[0])\r\n\r\n cm = pd.DataFrame(confusion_matrix(y_true, y_pred, labels=label_list), index=label_list, columns=label_list)\r\n\r\n print(\"Confusion matrix for the %s data:\" % type)\r\n\r\n print(\"row is the truth, column is the system output\\n\")\r\n\r\n print(cm.to_string() + \"\\n\")\r\n\r\n print(\"accuracy=%s\\n\\n\" % (accuracy_score(y_true, y_pred)))\r\n \r\n \r\n \r\ndef main():\r\n\r\n ## get all the parameters from the command line\r\n\r\n training_file = sys.argv[1]\r\n testing_file = sys.argv[2]\r\n class_prior_delta = float(sys.argv[3])\r\n cond_prob_delta = float(sys.argv[4])\r\n model_file = sys.argv[5]\r\n sys_file = sys.argv[6]\r\n \r\n \r\n training_data_structure, all_features, occurrence_of_class, z_dictionary = read_training_data(training_file)\r\n \r\n training_data_structure = OrderedDict(sorted(training_data_structure.items()))\r\n \r\n feature_class_dictionary = output_model_file(training_data_structure, model_file, class_prior_delta, cond_prob_delta,\r\n all_features, occurrence_of_class, z_dictionary)\r\n \r\n \"\"\"\r\n \r\n print(len(all_features)) \r\n \r\n for class_name in training_data_structure:\r\n print('the length of class ' + class_name + ' is ' + str(len(training_data_structure[class_name])))\r\n\r\n print(training_data_structure[class_name])\r\n\r\n \"\"\" \r\n\r\n with open(sys_file, \"w\") as wfp: \r\n \r\n classify(training_data_structure, all_features, occurrence_of_class, wfp, training_file, \"training\")\r\n wfp.write('\\n')\r\n wfp.write('\\n')\r\n classify(training_data_structure, all_features, occurrence_of_class, wfp, testing_file, \"testing\")\r\n \r\n \r\n \r\nif __name__ == '__main__':\r\n main() \r\n \r\n", "sub_path": "multinomial.py", "file_name": "multinomial.py", "file_ext": "py", "file_size_in_byte": 11687, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "math.log10", "line_number": 114, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 122, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 138, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 173, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 194, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 242, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 255, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 255, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 263, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 271, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 272, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 273, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 274, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 275, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 276, "usage_type": "attribute"}, {"api_name": "collections.OrderedDict", "line_number": 281, "usage_type": "call"}]} +{"seq_id": "506556619", "text": "from flask import Flask, render_template\napp = Flask(__name__)\n\n\n@app.route('/')\ndef index():\n return \"Khánh\"\n\n@app.route('/bmi//')\ndef bmi(weight, height):\n height = height / 100\n bmi = weight /(height * height)\n if bmi < 16 :\n return 'BMI ='+ str(bmi) + ' : Severely underweight'\n elif bmi < 18.5 :\n return 'BMI ='+ str(bmi) + ' : Underweight'\n elif bmi < 25 :\n return 'BMI ='+ str(bmi) + ' : Normal'\n elif bmi < 30 :\n return 'BMI ='+ str(bmi) + ' : Overweight'\n else:\n return 'BMI ='+ str(bmi) + ' : Obese'\n\n@app.route('/bmi_render//')\ndef bmi_render(weight, height):\n height = height / 100\n bmi = weight /(height * height)\n\n BMI = {\n \"bmi\" : bmi,\n \"tinh_trang\":\"\"\n }\n\n return render_template('bmi.html',BMI=BMI)\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n ", "sub_path": "Web01/hw1/BMI.py", "file_name": "BMI.py", "file_ext": "py", "file_size_in_byte": 913, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "flask.Flask", "line_number": 2, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "534776504", "text": "import os\nimport functools\nimport threading\n\n\ndef debounce(interval_s):\n \"\"\"Debounce calls to this function until interval_s seconds have passed.\"\"\"\n def wrapper(func):\n @functools.wraps(func)\n def debounced(*args, **kwargs):\n if hasattr(debounced, '_timer'):\n debounced._timer.cancel()\n debounced._timer = threading.Timer(interval_s, func, args, kwargs)\n debounced._timer.start()\n return debounced\n return wrapper\n\n\ndef flatten(list_of_lists):\n return [item for lst in list_of_lists for item in lst]\n\n\ndef line_col_to_pos(source, position):\n line = position['line']\n col = position['character']\n\n lines = source.splitlines()\n offset = 0\n for l in range(0, line):\n offset += (len(lines[l]) + 2)\n offset += col\n\n return offset\n\n\ndef pos_to_line_col(source, pos):\n line = 0\n lines = source.splitlines()\n for l in lines:\n temp = pos - (len(lines[line]) + 2)\n if temp >= 0:\n pos = temp\n line += 1\n\n return line, pos\n", "sub_path": "textx_langserv/utils/_utils.py", "file_name": "_utils.py", "file_ext": "py", "file_size_in_byte": 1069, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "threading.Timer", "line_number": 13, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "46292048", "text": "from dagster import (\n InputDefinition,\n dagster_type_loader,\n execute_pipeline,\n pipeline,\n solid,\n usable_as_dagster_type,\n)\n\n\n# def_start_marker\n@dagster_type_loader(config_schema={\"diameter\": float, \"juiciness\": float, \"cultivar\": str})\ndef apple_loader(_context, config):\n return Apple(\n diameter=config[\"diameter\"], juiciness=config[\"juiciness\"], cultivar=config[\"cultivar\"]\n )\n\n\n@usable_as_dagster_type(loader=apple_loader)\nclass Apple:\n def __init__(self, diameter, juiciness, cultivar):\n self.diameter = diameter\n self.juiciness = juiciness\n self.cultivar = cultivar\n\n\n@solid(input_defs=[InputDefinition(\"input_apple\", Apple)])\ndef my_solid(context, input_apple):\n context.log.info(f\"input apple diameter: {input_apple.diameter}\")\n\n\n@pipeline\ndef my_pipeline():\n my_solid()\n\n\n# def_end_marker\n\n\ndef execute_with_config():\n # execute_start_marker\n execute_pipeline(\n my_pipeline,\n run_config={\n \"solids\": {\n \"my_solid\": {\n \"inputs\": {\n \"input_apple\": {\"diameter\": 2.4, \"juiciness\": 6.0, \"cultivar\": \"honeycrisp\"}\n }\n }\n }\n },\n )\n # execute_end_marker\n", "sub_path": "examples/docs_snippets/docs_snippets/concepts/io_management/load_custom_type_from_config.py", "file_name": "load_custom_type_from_config.py", "file_ext": "py", "file_size_in_byte": 1266, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "dagster.dagster_type_loader", "line_number": 12, "usage_type": "call"}, {"api_name": "dagster.usable_as_dagster_type", "line_number": 19, "usage_type": "call"}, {"api_name": "dagster.solid", "line_number": 27, "usage_type": "call"}, {"api_name": "dagster.InputDefinition", "line_number": 27, "usage_type": "call"}, {"api_name": "dagster.pipeline", "line_number": 32, "usage_type": "name"}, {"api_name": "dagster.execute_pipeline", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "124819389", "text": "from flask import Flask, escape, request, jsonify\n\napp = Flask(__name__)\n\n@app.route('/')\ndef hello():\n name = request.args.get(\"name\", \"World\")\n return f'Hello, {escape(name)}!'\n\n@app.route('/users/', methods = ['POST'])\ndef user(user_id):\n data = request.json\n data.update({'id': user_id})\n return jsonify(data)\n\n\nif __name__ == '__main__':\n app.run()", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 380, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "flask.Flask", "line_number": 3, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 7, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 7, "usage_type": "name"}, {"api_name": "flask.escape", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 12, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 12, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "235307413", "text": "#Trabajo Practico 1.2 - Analisis Económico-Matemático de Apuestas En Ruleta\n#Integrantes: Alvarez, Elicegui, Galarza, Muñoz\nimport random as rnd\nimport os # Importo os porque me permite usar funciones del sistema y en este programa la uso para limpiar la terminal a la hora de mostrar los menus\nimport matplotlib.pyplot as plt # Importo la libreria matplotlib\nimport numpy as np # Importo la libria Numpy\nimport os # Acceso a funciones del sistema operativo\n\nglobal cantApFav # Define una variablo global cantidad apuestas Favorables\n\n\ndef ruleta(): # Defino una funcion ruleta la cuál me devuelve un numero aleatoreo entre 0 y 37\n return rnd.randint(0, 36)\n\n\ndef menu(): #Defino la funcion de menu principal\n os.system('cls')\n print(\"*** MENU DE OPCIONES ***\")\n print(\"Selecciona una opción\")\n print(\"1 - Martingala\")\n print(\"2 - Fibonacci\")\n print(\"3 - D'Alambert\")\n print(\"0 - Salir\")\n while True:\n try:\n op = int(input(\"Ingrese su opción: \"))\n except ValueError:\n print(\"Debes ingresar un número (valido)\")\n continue\n if op < 0 or op > 3:\n print(\"Debes ingresar un número comprendido entre 0 y 3\")\n continue\n else:\n break\n return op\n\n\ndef menu2(s: str) -> int: #este es el submenu --Los tipos en las funciones no son necesarios pero es una buena practica\n os.system('cls')\n print(\"***\"+s+\"***\")\n print(\" \")\n print(\"Selecciona un tipo de capital para la simulación\")\n print(\"1 - Capital infinito\")\n print(\"2 - Capital acotado\")\n print(\"0 - Salir\")\n while True:\n try:\n op = int(input(\"Ingrese su opción: \"))\n except ValueError:\n print(\"Debes ingresar un número (valido)\")\n continue\n if op < 0 or op > 2:\n print(\"Debes ingresar un número comprendido entre 0 y 2\")\n continue\n else:\n break\n return op\n\n\ndef valida_monto(): #Funcion para validar que ingrese un monto correcto NO letras, NO numeros negativos\n while True:\n try:\n monto = int(\n input('Indique el monto de capital inicial con el que comenzaremos: '))\n except ValueError:\n print(\"Debes ingresar un capital valido\")\n continue\n if monto < 0:\n print(\"Debes ingresar un capital superior a 0\")\n continue\n else:\n break\n return monto\n\n\ndef girar(calculaPromedio, modo, dinero_tot = None):\n if dinero_tot == None:\n dinero_disp = 0\n else:\n dinero_disp = dinero_tot\n valorApuesta = 1\n cantApFav = 0\n resultados = []\n resultados.append(dinero_disp)\n frecRelativa = []\n apuesta = 1\n if (modo == \"Fibonacci\"):\n contafib = 1\n\n for i in range(tiradas):\n result = ruleta()\n if (modo == \"Martingala\"):\n if (result % 2 == apuesta) & (result != 0):\n dinero_disp += valorApuesta\n valorApuesta = 1\n cantApFav += 1\n else:\n dinero_disp -= valorApuesta\n valorApuesta = valorApuesta*2\n elif (modo == \"D'Alambert\"):\n if (result % 2 == apuesta) & (result != 0):\n cantApFav += 1\n dinero_disp += valorApuesta\n if apuesta > 1:\n valorApuesta = valorApuesta - 1\n else:\n dinero_disp -= valorApuesta\n valorApuesta = valorApuesta + 1\n elif (modo == \"Fibonacci\"):\n contafib = 0 # OJO AQUI !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n if (result % 2 == apuesta) & (result != 0):\n dinero_disp += valorApuesta\n cantApFav += 1\n if contafib < 3:\n contafib = 1\n else:\n contafib -= 2\n else:\n dinero_disp -= valorApuesta\n contafib += 1\n valorApuesta = fib(contafib)\n resultados.append(dinero_disp)\n frecRelativa.append(cantApFav/(i+1))\n if calculaPromedio:\n promedio[0][i] = promedio[0][i]+dinero_disp/repeticiones\n promedio[1][i] = promedio[1][i]+cantApFav/(i+1)/repeticiones\n if dinero_tot != None:\n if dinero_disp == 0:\n break\n elif dinero_disp < valorApuesta:\n valorApuesta = dinero_disp\n results = []\n results.append(frecRelativa)\n results.append(resultados)\n return results\n\ndef fib(n):\n if n < 2:\n return 1\n else:\n return fib(n - 1) + fib(n - 2)\n\ndef correr(modo, dinero_tot = None):\n if tiradas == 1:\n results = girar(False, modo, dinero_tot)\n plt.plot(results[0])\n plt.title(modo + \" - Frecuencia relativa de apuestas ganadas\")\n plt.hlines((18/37),0,tiradas, color='red')\n plt.ylabel('Frecuencia relativa')\n plt.xlabel('Numero total de apuestas')\n plt.show()\n\n plt.plot(results[1])\n plt.title(modo)\n if dinero_tot != None:\n plt.ylabel('Dinero')\n plt.hlines(cap_ini,0,tiradas, color='red')\n else:\n plt.ylabel('Beneficio acumulado')\n plt.hlines(0,0,tiradas, color='red')\n plt.xlabel('Numero de apuestas')\n plt.show()\n else:\n global promedio\n promedio = [[],[]]\n din = []\n fr = []\n for i in range(tiradas):\n promedio[0].append(0)\n promedio[1].append(0)\n\n for j in range(repeticiones):\n results = girar(True, modo, dinero_tot)\n fr.append(results[0])\n din.append(results[1])\n\n for i in range(repeticiones):\n plt.plot(fr[i])\n plt.plot(promedio[1], color='black', label='Promedio')\n plt.legend(loc=\"lower left\")\n plt.title(modo + \" - Frecuencia relativa de apuestas ganadas\")\n plt.hlines((18/37),0,tiradas, color='red')\n plt.ylabel('Frecuencia relativa')\n plt.xlabel('Numero total de apuestas')\n plt.show()\n\n for i in range(repeticiones):\n plt.plot(din[i])\n plt.plot(promedio[0], color='black', label='Promedio')\n plt.legend(loc=\"lower left\")\n plt.title(modo)\n\n if dinero_tot != None:\n plt.ylabel('Dinero')\n plt.hlines(cap_ini,0,tiradas, color='red')\n else:\n plt.ylabel('Beneficio acumulado')\n plt.hlines(0,0,tiradas, color='red')\n plt.xlabel('Numero de apuestas')\n plt.show()\n# Programa principal\nif __name__ == '__main__':\n os.system('cls')\n print(\"***CARGA DE DATOS INICIALES***\")\n promedio = [[], []]\n repeticiones = int(input('Indique la cantidad de veces a repetir el experimento: '))\n tiradas = int(input('Indique la cantidad de veces que se hara girar la ruleta: '))\n \n while True:\n estrategia = menu() # estrategia le puse este nombre por Seleccion de estrategia\n if estrategia == 1:\n while True:\n seleccion = menu2(\"Estrategia seleccionada Martingala\")\n if seleccion == 2:\n cap_ini = valida_monto()\n correr(\"Martingala\", cap_ini)\n elif seleccion == 1:\n correr(\"Martingala\")\n else:\n break\n elif estrategia == 2:\n while True:\n seleccion = menu2(\"Estrategia seleccionada Fibonacci\")\n if seleccion == 2:\n cap_ini = valida_monto()\n correr(\"Fibonacci\", cap_ini)\n elif seleccion == 1:\n correr(\"Fibonacci\")\n else:\n break\n elif estrategia == 3:\n while True:\n seleccion = menu2(\"Estrategia seleccionada D'Alambert\")\n if seleccion == 2:\n cap_ini = valida_monto()\n correr(\"D'Alambert\", cap_ini)\n elif seleccion == 1:\n correr(\"D'Alambert\")\n else: \n break\n else:\n print(\"Saliendo...\")\n break\n print(\"FIN\")\n", "sub_path": "trabajo_practico1.2/Trabajo Practico 1.2 VER 1.0 .py", "file_name": "Trabajo Practico 1.2 VER 1.0 .py", "file_ext": "py", "file_size_in_byte": 8232, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "random.randint", "line_number": 13, "usage_type": "call"}, {"api_name": "os.system", "line_number": 17, "usage_type": "call"}, {"api_name": "os.system", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 147, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 147, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hlines", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 148, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 149, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 149, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 150, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 150, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 151, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 153, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 156, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 156, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hlines", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 157, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 159, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hlines", "line_number": 160, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 160, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 161, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 162, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 162, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 178, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 178, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 179, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 180, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 180, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 181, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 181, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hlines", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 182, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 183, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 183, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 184, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 184, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 185, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 188, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 189, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 189, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 190, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 190, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 191, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 191, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 194, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hlines", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 195, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hlines", "line_number": 198, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 198, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 199, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 199, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 200, "usage_type": "name"}, {"api_name": "os.system", "line_number": 203, "usage_type": "call"}]} +{"seq_id": "338262835", "text": "#!/usr/bin/env python\n# coding: utf-8\n#--------------------Библиотеки----------------------------------\nfrom io import BytesIO\nimport pandas as pd\nimport requests\nimport dash\nfrom dash.dependencies import Input, Output\nimport dash_core_components as dcc\nimport dash_html_components as html\nimport plotly.graph_objs as go\n#--------------------/Библиотеки---------------------------------- \n\n\n#--------------------Таблицы-------------------------------------------------------------------------\n#--------------------------------сложная тема---------------------------\ndef create_most_dif_df():\n r = requests.get('https://docs.google.com/spreadsheets/d/1x85oldnFJr2SqHQhvhTVYj08T62FbIiwL9ub2QB9TZY/export?format=csv')\n data = r.content\n df = pd.read_csv(BytesIO(data), index_col=0).reset_index()\n df.columns = ['timestamp','gender','age','city','most_difficult_theme','quality_rate','job_rate','review']\n df['timestamp']= pd.to_datetime(df['timestamp'], format='%d.%m.%Y %H:%M:%S')\n df['day'] = df['timestamp'].astype('datetime64[D]')\n most_dif = df.groupby('most_difficult_theme')['most_difficult_theme'].count()\n most_dif.name = 'count'\n most_dif = most_dif.reset_index()\n return most_dif\n#--------------------------------/сложная тема---------------------------\n#--------------------------------рейтинг--------------------------------\ndef create_rating_df():\n r = requests.get('https://docs.google.com/spreadsheets/d/1x85oldnFJr2SqHQhvhTVYj08T62FbIiwL9ub2QB9TZY/export?format=csv')\n data = r.content\n df = pd.read_csv(BytesIO(data), index_col=0).reset_index()\n df.columns = ['timestamp','gender','age','city','most_difficult_theme','quality_rate','job_rate','review']\n df['timestamp']= pd.to_datetime(df['timestamp'], format='%d.%m.%Y %H:%M:%S')\n df['day'] = df['timestamp'].astype('datetime64[D]')\n df['review'].fillna(df.review.mean(),inplace=True)\n df['pr_rate']= df.quality_rate*df.job_rate*df.review\n # Таблица со средними рейтингами по дням\n pr_rate_per_day = df.groupby('day')['pr_rate'].mean().round(2).reset_index()\n return pr_rate_per_day \n#--------------------------------/рейтинг---------------------------------\n#--------------------------------Карта-----------------------------------\ndef create_map_df():\n r = requests.get('https://docs.google.com/spreadsheets/d/1x85oldnFJr2SqHQhvhTVYj08T62FbIiwL9ub2QB9TZY/export?format=csv')\n data = r.content\n df = pd.read_csv(BytesIO(data), index_col=0).reset_index()\n df.columns = ['timestamp','gender','age','city','most_difficult_theme','quality_rate','job_rate','review']\n df['timestamp']= pd.to_datetime(df['timestamp'], format='%d.%m.%Y %H:%M:%S')\n df['day'] = df['timestamp'].astype('datetime64[D]')\n df['review'].fillna(df.review.mean(),inplace=True)\n df['pr_rate']= df.quality_rate*df.job_rate*df.review\n list_of_cities = df['city']\n token_Geocoder = 'c29e4b87-39fb-4f54-9689-ccc8cec48cd7'\n url = 'https://geocode-maps.yandex.ru/1.x/?format=json&apikey={}&geocode='.format(token_Geocoder)\n coordinates1 = []\n coordinates2 = []\n for city in list_of_cities:\n if city == city: # чтоб не столкнуться с nan\n url_formatted = url + city\n response = requests.get(url_formatted).json()\n data1 = response['response']['GeoObjectCollection']['featureMember'][0]['GeoObject']['Point'].get('pos')\n coordinate1 = (float(data1.split()[1]))\n coordinate2 = (float(data1.split()[0]))\n coordinates1.append(coordinate1)\n coordinates2.append(coordinate2)\n else:\n coordinates.append('')\n df['x'] = coordinates1\n df['y'] = coordinates2\n size = df.groupby('city')['city'].count()\n size.name = 'size'\n df = df.merge(size,on = 'city')\n return df\n#--------------------------------/Карта-----------------------------------\n#--------------------/Таблицы-------------------------------------------------------------------------\n\n\n#---------------------словарь цветов-----------------------------\ncolors = {\n 'background': '#27292d',\n 'H':'white',\n 'text': '#e8f0fc',\n 'lines' :'red',\n 'grid' : '#59616e'\n}\n#---------------------/словарь цветов----------------------------- \n \n#---------------------CSS + app---------------------------------- \nexternal_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']\napp = dash.Dash(__name__, external_stylesheets=external_stylesheets)\n#---------------------/CSS + app---------------------------------- \n \n#--------------------------------------------------------layout-------------------------------------------------------\napp.layout = html.Div(\n html.Div([\n dcc.Graph(id='1live-update-graph'),\n dcc.Graph(id='live-update-graph'),\n dcc.Graph(id='map'),\n dcc.Interval(\n id='interval-component',\n interval=6*1000, # in milliseconds\n n_intervals=0\n ),\n dcc.Interval(\n id='map-interval-component',\n interval=20*60000, #(каждые 20 минут, наверное можно сделать еще больше период)\n n_intervals=0\n )\n ])\n)\n#----------------------diff_theme_plot---------------------------------- \n@app.callback(Output('live-update-graph', 'figure'),\n [Input('interval-component', 'n_intervals')])\n\n\ndef update_graph_live(n):\n most_dif = create_most_dif_df()\n # логика для диффзем\n x = most_dif['most_difficult_theme']\n y = most_dif['count'].sort_values(ascending = False)\n colorss = ['lightslategray',] * 5\n colorss[0] = 'crimson'\n#--------------------diff_theme-------------------\n \n data=[go.Bar(x=most_dif['most_difficult_theme'], y=y,\n text=most_dif['count'].sort_values(ascending = False),\n textposition='outside',\n marker_color=colorss)]\n layout = go.Layout(plot_bgcolor=colors['background'],\n paper_bgcolor = colors['background'],\n font=dict(color=colors['text']),\n xaxis=dict(gridcolor=colors['grid'],\n showgrid=False),\n yaxis=dict(gridcolor=colors['grid'])\n )\n fig = go.Figure(data=data, layout=layout)\n #fig.add_shape(go.layout.Shape(type=\"line\", x0=df.loc[14,'timestamp'], y0=0, x1=df.loc[14,'timestamp'], y1=650, line=dict(color='green')))\n \n return fig\n#----------------------/diff_theme_plot----------------------------------\n\n#----------------------rating_plot--------------------------------------\n \n@app.callback(Output('1live-update-graph', 'figure'),\n [Input('interval-component', 'n_intervals')])\ndef update_graph_live(n):\n pr_rate_per_day = create_rating_df()\n # логика для рэйтинга\n pr_day_rate = pr_rate_per_day.iloc[-2,1]\n last_day_rate = pr_rate_per_day.iloc[-1,1]\n#-------------------------------------\n data=[go.Indicator(mode = 'number+delta',\n value = last_day_rate,\n delta = {\"reference\": pr_day_rate, \"valueformat\": \".0f\"},\n title = {\"text\": \"Praktikum rate\"},\n domain = {'y': [0, 1], 'x': [0.25, 0.75]}\n ),\n go.Scatter(\n x = pr_rate_per_day['day'],\n y = pr_rate_per_day['pr_rate'],\n marker_color = 'crimson') \n ]\n layout = go.Layout(plot_bgcolor=colors['background'],\n paper_bgcolor = colors['background'],\n font=dict(color=colors['text']),\n xaxis=dict(gridcolor=colors['grid'],\n showgrid=False),\n yaxis=dict(gridcolor=colors['grid'])\n )\n fig = go.Figure(data=data, layout=layout)\n #fig.add_shape(go.layout.Shape(type=\"line\", x0=df.loc[14,'timestamp'], y0=0, x1=df.loc[14,'timestamp'], y1=650, line=dict(color='green')))\n \n return fig\n#----------------------/rating_plot--------------------------------------\n#----------------------map_plot-----------------------------------------\n@app.callback(Output('map', 'figure'),\n [Input('map-interval-component', 'n_intervals')])\ndef update_graph_live(n):\n df = create_map_df()\n # логика для карты\n mapbox_access_token = \"pk.eyJ1IjoiYWxiZWw5OTk5IiwiYSI6ImNrNmI0M2NydTA1YjAzZnBha2dtcnJ1YmYifQ.7H3jZRqSGUnb88yeLgkN_A\"\n \n#-------------------------------------\n\n\n data = [go.Scattermapbox(lat=df['x'], lon=df['y'],\n mode='markers+text',\n hovertext=df['size'],\n textfont=dict(color='#e8f0fc'),\n marker=dict(size=df['size']+20,\n color = 'crimson'),\n text=df['city'])]\n\n layout = go.Layout(mapbox_style='dark',\n autosize=True,\n hovermode='closest',\n mapbox=dict(accesstoken=mapbox_access_token,\n bearing=0,\n center=dict(lat=55, lon=62),\n pitch=0, zoom=2.3),\n plot_bgcolor = colors['background'],\n paper_bgcolor = colors['background'])\n\n fig = go.Figure(data=data, layout=layout)\n #fig.add_shape(go.layout.Shape(type=\"line\", x0=df.loc[14,'timestamp'], y0=0, x1=df.loc[14,'timestamp'], y1=650, line=dict(color='green')))\n \n return fig\n#----------------------/map_plot----------------------------------------\n#--------------------------------------------------------/layout------------------------------------------------------- \n \ndef run_server(self,\n port=8050,\n debug=True,\n threaded=True,\n **flask_run_options):\n self.server.run(port=port, debug=debug, **flask_run_options)\n \n \n# условная конструкция и запуск\nif __name__ == '__main__':\n app.run_server(debug=True, port=8099) # or whatever you choose", "sub_path": "rus_upd_dfs.py", "file_name": "rus_upd_dfs.py", "file_ext": "py", "file_size_in_byte": 10432, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "requests.get", "line_number": 18, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 20, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 22, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 31, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 33, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 33, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 35, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 45, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 47, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 47, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 49, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 61, "usage_type": "call"}, {"api_name": "dash.Dash", "line_number": 91, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 95, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 96, "usage_type": "call"}, {"api_name": "dash_core_components.Graph", "line_number": 97, "usage_type": "call"}, {"api_name": "dash_core_components.Graph", "line_number": 98, "usage_type": "call"}, {"api_name": "dash_core_components.Graph", "line_number": 99, "usage_type": "call"}, {"api_name": "dash_core_components.Interval", "line_number": 100, "usage_type": "call"}, {"api_name": "dash_core_components.Interval", "line_number": 105, "usage_type": "call"}, {"api_name": "plotly.graph_objs.Bar", "line_number": 126, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 126, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Layout", "line_number": 130, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 130, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Figure", "line_number": 137, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 137, "usage_type": "name"}, {"api_name": "dash.dependencies.Output", "line_number": 113, "usage_type": "call"}, {"api_name": "dash.dependencies.Input", "line_number": 114, "usage_type": "call"}, {"api_name": "plotly.graph_objs.Indicator", "line_number": 153, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 153, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Scatter", "line_number": 159, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 159, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Layout", "line_number": 164, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 164, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Figure", "line_number": 171, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 171, "usage_type": "name"}, {"api_name": "dash.dependencies.Output", "line_number": 145, "usage_type": "call"}, {"api_name": "dash.dependencies.Input", "line_number": 146, "usage_type": "call"}, {"api_name": "plotly.graph_objs.Scattermapbox", "line_number": 187, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 187, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Layout", "line_number": 195, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 195, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Figure", "line_number": 205, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 205, "usage_type": "name"}, {"api_name": "dash.dependencies.Output", "line_number": 177, "usage_type": "call"}, {"api_name": "dash.dependencies.Input", "line_number": 178, "usage_type": "call"}]} +{"seq_id": "473193241", "text": "import oandapy\nimport os\nfrom datetime import datetime\nfrom dateutil.relativedelta import relativedelta\nfrom flask import Flask, request, jsonify, render_template\nfrom flask.ext.assets import Environment, Bundle\n# from nocache import nocache\nimport pdb\n\nDEBUG = True\n\n# Temporary for pretty printing\nimport pprint\npp = pprint.PrettyPrinter(indent=2)\n\nclass ServalFlask(Flask):\n\tNO_CACHE = 0\n\tdef get_send_file_max_age(self, name):\n\t\tif name.lower().endswith(('.html', '.css', '.js')):\n\t\t\treturn self.__class__.NO_CACHE\n\t\telse:\n\t\t\treturn super().get_send_file_max_age(name)\n\nOANDA_API_KEY = 'OANDA_API_KEY'\nif OANDA_API_KEY not in os.environ:\n\traise OSError(\"Missing {} environment variable. Please set before proceeding.\".format(OANDA_API_KEY))\n\noanda = oandapy.API(environment='practice', access_token=os.environ[OANDA_API_KEY])\n\n# Setup flask\napp = ServalFlask(__name__)\n\n# Precompile assets\nassets = Environment(app)\njst = Bundle('templates/*.jst', filters='jst', output='compiled_templates.js')\nassets.register('jst', jst)\nassets['jst'].urls() # Build template bundle\n\nscss = Bundle('stylesheets/*.scss', filters='scss', output='compiled_styles.css')\nassets.register('scss_all', scss)\n\n# Define routes\n@app.route('/')\ndef show_app():\n\treturn render_template('application.html')\n\n# Define API routes\n@app.route('/api/price-history')\ndef api_price_history():\n\targs = request.args\n\tend_date = datetime.utcnow()\n\tstart_date = end_date - relativedelta(weeks=1)\n\tresponse = oanda.get_history(\n\t\tinstrument=args.get('instrument', 'EUR_USD'),\n\t\tgranularity=args.get('granularity', 'H1'),\n\t\tstart=args.get('start', start_date.isoformat()),\n\t\tend=args.get('end', end_date.isoformat()),\n\t\tcandleFormat=args.get('candleFormat', 'midpoint'),\n\t\talignmentTimezone=args.get('alignmentTimezone', 'America/New_York'))\n\treturn jsonify(**response)\n\nif __name__ == '__main__':\n\tapp.run(debug=DEBUG)", "sub_path": "serval.py", "file_name": "serval.py", "file_ext": "py", "file_size_in_byte": 1881, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "pprint.PrettyPrinter", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 16, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 25, "usage_type": "attribute"}, {"api_name": "oandapy.API", "line_number": 28, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask.ext.assets.Environment", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.ext.assets.Bundle", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.ext.assets.Bundle", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 50, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 50, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 51, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 51, "usage_type": "name"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 52, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 60, "usage_type": "call"}]} +{"seq_id": "646067636", "text": "import os\nfrom scrapy.utils.project import get_project_settings\nfrom wangban_utils.Json2Xpath import Json2XPath\nSETTINGS = get_project_settings()\nBASE_JSONFILE_PATH = SETTINGS['BASE_JSONFILE_PATH']\n\nclass BaseSeleSpider:\n def __init__(self):\n self.refined_totalpage = 2\n jsonfile = os.path.join(BASE_JSONFILE_PATH,'{}.json'.format(self.name))\n self.xp = Json2XPath(jsonfile).get_xpath()\n\n\n def county_modify(self,item):\n return 'NONE'\n\n def get_totalpage(self,driver):\n #获取总页数，没有总页数，设置总页数为1\n try:\n total_page = driver.find_element_by_xpath(self.xp.total_page).text\n total_page = re.findall(r'1 / (\\d+)',total_page,re.I)[0]\n int(total_page)\n except ValueError:\n total_page = '1'\n total_page = self.set_totalpage(total_page)\n return total_page\n\n\n def service_able_check(self,driver):\n return True\n\n def set_totalpage(self,orignal):\n if int(orignal) > self.refined_totalpage:\n orignal = self.refined_totalpage\n return orignal\n\n\n def presence_elements(self,driver):\n return self.xp.column\n\n def get_elements(self,driver):\n try:\n elements = driver.find_elements_by_xpath(self.xp.column)\n except Exception as e:\n print('get_elements error',e)\n elements = []\n return elements\n\n def get_an_title(self,element,driver):\n an_title = 'NONE'\n try:\n an_title = element.find_element_by_xpath(self.xp.an_title).get_attribute('title')\n except Exception as e:\n print('get an title error',e)\n if not an_title:\n an_title = 'NONE'\n return an_title\n\n def get_on_date(self,element,driver):\n on_date = 'NONE'\n try:\n on_date = element.find_element_by_xpath(self.xp.on_date).text\n except Exception as e:\n print('get on date error',e)\n if not on_date:\n on_date = 'NONE'\n return on_date\n\n def get_an_sub(self,an_sub,element,driver):\n \n return an_sub\n\n def get_an_county(self,element,driver):\n an_county = 'NONE'\n return an_county\n\n def get_elem_url(self,elem,driver):\n element_url = self.source_url\n try:\n element_url = elem.find_element_by_xpath(self.xp.an_url).get_attribute('href')\n except Exception as e:\n print('get elem url error',e)\n if not element_url:\n element_url = self.source_url\n return element_url\n\n def click_next_page(self,driver,**kwgs):\n try:\n driver.find_element_by_xpath(self.xp.next_page).click()#点击翻页\n time.sleep(7)\n except Exception as e:\n driver.find_element_by_xpath(self.xp.next_page).click()#点击翻页\n time.sleep(7)\n\n\n def county_modify(self,item):\n return 'NONE'\n \n def get_content(self,driver):\n content = driver.find_element_by_xpath('//body').get_attribute('innerHTML')#获取网页html\n return content", "sub_path": "wangban/wangban/spiders/beifen/2/selecrawlers/baseselespider.py", "file_name": "baseselespider.py", "file_ext": "py", "file_size_in_byte": 3115, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "scrapy.utils.project.get_project_settings", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "wangban_utils.Json2Xpath.Json2XPath", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "68535882", "text": "\"\"\"Detectron config system.\n\n\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\nfrom ast import literal_eval\nfrom future.utils import iteritems\nimport copy, io,logging\nimport numpy as np\nimport os\nimport os.path as osp\nimport six\n\nfrom detectron.utils.collections import AttrDict\nfrom detectron.utils.io import cache_url\n\nlogger = logging.getLogger(__name__) # 日志模块\n\n__C = AttrDict()\n\ncfg = __C\n\n# Training options\n__C.TRAIN = AttrDict()\n\n__C.TRAIN.WEIGHTS = ''\n\n__C.TRAIN.DATASETS = ()\n\n__C.TRAIN.SCALES = (600, )\n\n__C.TRAIN.MAX_SIZE = 1000\n\n__C.TRAIN.IMS_PER_BATCH = 2\n\n# RoI minibatch size * per image * (number of regions of interest [ROIs]\n# Total number of RoIs per training minibatch =\n# TRAIN.BATCH_SIZE_PER_IM * TRAIN.IMS_PER_BATCH * NUM_GPUS\n__C.TRAIN.BATCH_SIZE_PER_IM = 64\n\n# Target fraction of RoI minibatch that is labeled foreground\n__C.TRAIN.FG_FRACTION = 0.25\n\n__C.TRAIN.FG_THRESH = 0.5\n\n# [LO, HI))\n__C.TRAIN.BG_THRESH_HI = 0.5\n__C.TRAIN.BG_THRESH_LO = 0.0\n\n__C.TRAIN.USE_FLIPPED = True\n\n__C.TRAIN.BBOX_THRESH =0.5\n\n# Divide by NUM_GPUS to determine actual period (e.g., 2000/8 => 2500 iters\n# to allow for linear training schedule scaling\n__C.TRAIN.SNAPSHOT_ITERS = 20000\n\n# Proposal files must be in correspondence with the datasets listed in Train.DATASETS\n__C.TRAIN.PROPOSAL_FILES = ()\n\n# Make minibatches from images that have similar aspect ratios(i.e. both\n# tall and thin or both short and wide)\n# This feature is critical for saving memory ( and makes training slightly faster)\n__C.TRAIN.ASPECT_GROUPING = True\n\n# ---------------------------------------------------------------------------- #\n# RPN training options\n# ---------------------------------------------------------------------------- #\n\n__C.TRAIN.RPN_POSITIVE_OVERLAP = 0.7\n\n__C.TRAIN.RPN_NEGATIVE_OVERLAP = 0.3\n\n__C.TRAIN.RPN_FG_FRACTION = 0.5\n\n# Total number of RPN examples per image\n__C.TRAIN.RPN_BATCH_SIZE_PER_IM = 256\n\n__C.TRAIN.RPN_NMS_THRESH = 0.7\n\n# Number of top scoring RPN proposals to keep before applying NMS\n# When FPN is used ,this is * per FPN level* (not total)\n__C.TRAIN.RPN_PER_NMS_TOP_N = 12000\n\n\n__C.TRAIN.RPN_POST_NMS_TOP_N = 2000\n\n# Remove RPN anchors that go outside the image by RPN_STRADDLE_THRESH pixels\n# Set to -1 or a large value, e.g. 100000, to disable pruning anchors\n__C.TRAIN.RPN_STRADDLE_THRESH = 0\n\n__C.TRAIN.PRN_MIN_SIZE = 0\n\n__C.TRAIN.CROWD_FILTER_THRESH =0.7\n\n\n# ------------------------------------------------------------------------------#\n# FPN options\n# ------------------------------------------------------------------------------#\n__C.FPN = AttrDict()\n\n__C.FPN.FPN_ON = False\n\n__C.FPN.DIM = 256\n\n__C.FPN.ZERO_INIT_LATERAL = False\n\n# Stride of the coarest FPN level\n__C.FPN.COARSEST_STRIDE = 32\n\n# FPN mya be used for just RPN, just object detection, or both\n__C.FPN.MULTILEVEL_ROIS = False\n# Hyperparameters for the RoI-to-FPN level mapping heuristic\n__C.FPN.ROI_CANONICAL_SCALE = 224\n__C.FPN.ROI_CANONICAL_LEVEL = 4\n\n__C.FPN.ROI_MAX_LEVEL = 5\n__C.FPN.ROI_MIN_LEVEL = 2\n\n__C.FPN.MULTILEVEL_RPN = False\n__C.FPN.RPN_MAX_LEVEL = 6\n__C.FPN.RPN_MIN_LEVEL = 2\n__C.FPN.RPN_ASPECT_RATIOS = (0.5, 1, 2)\n# RPN anchors start at this case on RPN_MIN_LEVEL\n# The anchor size doubled each level after that\n# With a default of 32 and levels 2 to 6, we get anchor sizes of 32 to 512\n__C.FPN.RPN_ANCHOR_START_SIZE = 32\n__C.FPN.EXTRA_CONV_LEVELS = False\n\n__C.FPN.USE_GN = False\n\n# ------------------------------------------------------------------------------#\n# Mask R-CNN options\n# ------------------------------------------------------------------------------#\n\n", "sub_path": "detectron/code_fork/config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 3699, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "logging.getLogger", "line_number": 20, "usage_type": "call"}, {"api_name": "detectron.utils.collections.AttrDict", "line_number": 22, "usage_type": "call"}, {"api_name": "detectron.utils.collections.AttrDict", "line_number": 27, "usage_type": "call"}, {"api_name": "detectron.utils.collections.AttrDict", "line_number": 103, "usage_type": "call"}]} +{"seq_id": "411692443", "text": "from collections import defaultdict\nfrom math import atan, sqrt\nimport numpy as np\n\n\ndef quantify(tracked_storms: np.ndarray, precip_data: np.ndarray, lat_data: np.ndarray, long_data: np.ndarray,\n time_interval: float, pixel_size: float) -> tuple:\n \"\"\"Quantitatively describes individual storms in terms of duration, size, mean intensity, and central location.\n :param tracked_storms: the tracked storms returned by the tracking algorithm, given as an array of dimensions\n Time x Rows x Cols.\n :param precip_data: the precipitation data corresponding to the tracked storms data, with the same dimensions as\n tracked_storms.\n :param lat_data: the latitude data corresponding to each [y][x] in tracked_storms, given as an array of\n dimensions 1 x Rows x Cols.\n :param long_data: the longitude data corresponding to each [y][x] in tracked_storms, given as an array of\n dimensions 1 x Rows x Cols.\n :param time_interval: the period between temporal 'snapshots', given as a float. The user should interpret the\n duration results in terms of the time unit implied here.\n :param pixel_size: the length/width one grid cell represents in the data. The user should interpret the size and\n average intensity results in terms of the distance unit implied here squared.\n :return: A tuple of size four containing the duration of each storm, as well as its size, intensity,\n and central location at each time step, in this order.\n \"\"\"\n\n # find the duration of the storms\n durations = get_duration(tracked_storms, time_interval)\n\n # find the size of each storm in each time slice\n sizes = get_size(tracked_storms, pixel_size)\n\n # find the average precipitation amount for each storm in each time slice\n averages = get_average(tracked_storms, precip_data)\n\n # and find the central location for each storm in each time slice\n central_locs = get_central_loc(tracked_storms, precip_data, lat_data, long_data)\n\n return durations, sizes, averages, central_locs\n\n\ndef get_duration(storms: np.ndarray, time_interval: float) -> np.ndarray:\n \"\"\"Computes the duration (in the time unit of time_interval) of each storm across all time slices given.\n :param storms: the tracked storms returned by the tracking algorithm, given as an array of dimensions\n Time x Rows x Cols.\n :param time_interval: the period between temporal 'snapshots', given as a float.\n :return: An array of length equal to the number of tracked storms + 1, where the value at [x] corresponds to\n the duration of the storm x. The index 0 (referring to the background) is always 0 and provided for ease of\n indexing.\n \"\"\"\n\n # find the number of time slices in the data\n lifetime = storms.shape[0]\n\n # initialize a new dictionary\n duration_dict = defaultdict(int)\n\n # and the number of storms\n total_storms = len(np.unique(storms))\n\n print(total_storms)\n\n # and an array to store the result in, where the value found at each index corresponds to the duration that storm\n result = np.zeros(total_storms)\n\n # then, for each time slice\n for time_index in range(lifetime):\n # compute the labels that appear in that time slice\n curr_labels = np.unique(storms[time_index])\n\n # for each label in the tracked storm data\n for label in range(total_storms):\n # if it appears in the current time slice\n if label and np.isin(label, curr_labels):\n # increment the number of time slices it appears in\n # (and if we haven't seen it before, set it to 1 in the dictionary (this is a property of defaultdict)\n duration_dict[label] += 1\n\n for key, value in duration_dict.items():\n if key:\n result[key] = value\n\n result = result * time_interval\n\n return result\n\n\ndef get_size(storms: np.ndarray, grid_cell_size: float) -> np.ndarray:\n \"\"\"Computes the size (in the distance unit of grid_cell_size) of each storm across all time slices given.\n :param storms: the tracked storms returned by the tracking algorithm, given as an array of dimensions\n Time x Rows x Cols.\n :param grid_cell_size: the length/width one grid cell represents in the data, given as a float.\n :return: a lifetime x total_storms array where the value found at [y][x] corresponds to the size of the storm at t=y,\n storm=x. Except in the case of index 0, which is always 0 for any t.\n \"\"\"\n\n # find the number of time slices in the data\n lifetime = storms.shape[0]\n\n # TODO: CHANGED TO LEN, NOT SURE HOW WORKING BEFORE\n # and the number of storms\n total_storms = len(np.unique(storms))\n\n # initialize an array with dimensions number of time slices by number of storms\n result = np.zeros((lifetime, total_storms))\n\n for time_index in range(lifetime):\n # find the unique labels\n labels = np.unique(storms[time_index])\n\n # for each label that appears in this time slice (that's not the background)\n for label in labels:\n if label:\n\n # compute its number of grid cells using a map and reduce technique\n storm_size = np.sum(np.where(storms[time_index] == label, 1, 0))\n\n # and place it at that correct location in the array to return\n result[time_index][label] = storm_size\n\n # multiply the number of grid cells in each storm by the grid cell size\n result = result * grid_cell_size\n\n return result\n\n\ndef get_average(storms: np.ndarray, precip: np.ndarray) -> np.ndarray:\n \"\"\"Computes the average intensity of each storm across all time slices given.\n :param storms: the tracked storms returned by the tracking algorithm, given as an array of dimensions\n Time x Rows x Cols.\n :param precip: the precipitation data corresponding to the tracked storms, with the same dimensions as\n tracked_storms.\n :return: a lifetime x total_storms array where the value found at [y][x] corresponds to the mean intensity of the\n storm at t=y, storm=x. Except in the case of index 0, which is always 0 for any t.\n \"\"\"\n\n # find the number of time slices in the data\n lifetime = storms.shape[0]\n\n # and the number of storms\n total_storms = len(np.unique(storms))\n\n # initialize an array with dimensions number of time slices by number of storms\n result = np.zeros((lifetime, total_storms))\n\n for time_index in range(lifetime):\n # find the unique labels\n labels = np.unique(storms[time_index])\n\n # for each label that appears in this time slice (that's not the background)\n for label in labels:\n\n if label:\n # find the precipitation where it appears in the current time slice\n storm_precip = np.where(storms[time_index] == label, precip[time_index], 0)\n\n # sum the precipitation\n storm_precip_sum = np.sum(storm_precip)\n\n # find the number of grid cells belonging to the storm\n storm_size = np.sum(np.where(storms[time_index] == label, 1, 0))\n\n # find the storm's average precipitation in this time slice\n storm_avg = storm_precip_sum / storm_size\n\n # and store it in the appropriate place in our result array\n result[time_index][label] = storm_avg\n\n return result\n\n\ndef get_central_loc(storms: np.ndarray, precip: np.ndarray, lats: np.ndarray, longs: np.ndarray) \\\n -> np.ndarray:\n \"\"\"Computes the central location on the earth's surface of each storm across all time slices given.\n :param storms: the tracked storms returned by the tracking algorithm, given as an array of dimensions\n Time x Rows x Cols.\n :param precip: the precipitation data corresponding to the tracked storms data, with the same dimensions as\n tracked_storms.\n :param lats: The latitude data corresponding to each [y][x] in tracked_storms, given as an array of dimensions\n 1 x Rows x Cols.\n :param longs: The longitude data corresponding to each [y][x] in tracked_storms, given as an array of dimensions\n 1 x Rows x Cols.\n :param size_array: the array returned by get_size(), a lifetime x total_storms array where the value found at [y][x]\n corresponds to the size of the storm at time=y, storm=x.\n :param lifetime: the number of time slices in the data, given as an integer.\n :param total_storms: the total number of storms INCLUDING the background, given as an integer.\n :return: a lifetime x total_storms array where the value found at [y][x] corresponds to the central location of the\n storm at t=y, storm=x. Except in the case of index 0, which is always 0 for any t.\n \"\"\"\n\n lifetime = storms.shape[0]\n\n total_storms = len(np.unique(storms))\n\n # initialize an array to store our result, but of type object to allow us to store an array in each cell\n result = np.zeros((lifetime, total_storms)).astype(object)\n\n # create arrays of x, y, and z values for the cartesian grid in R3\n x_array = np.cos(lats) * np.cos(longs)\n y_array = np.cos(lats) * np.sin(longs)\n z_array = np.sin(lats)\n\n # create an array to hold each central location as we calculate it\n central_location = np.empty(2)\n\n for time_index in range(lifetime):\n # find the unique labels\n labels = np.unique(storms[time_index])\n\n for label in labels:\n # if the storm exists in this time slice\n if label:\n\n # find the sum of the precipitation values belonging to the storm\n sum_precipitation = np.sum(np.where(storms[time_index] == label, precip[time_index], 0))\n\n # and compute the intensity weighted averages\n x_avg = np.sum(np.where(storms[time_index] == label, ((x_array[0] * precip[time_index]) /\n sum_precipitation), 0))\n\n y_avg = np.sum(np.where(storms[time_index] == label, ((y_array[0] * precip[time_index]) /\n sum_precipitation), 0))\n\n z_avg = np.sum(np.where(storms[time_index] == label, ((z_array[0] * precip[time_index]) /\n sum_precipitation), 0))\n\n h_avg = sqrt((x_avg ** 2) + (y_avg ** 2))\n # print(f\"CL before: {central_location}\")\n\n # the central location on earth's surface is given by the following\n central_location[0] = 2 * atan(y_avg / (sqrt((y_avg ** 2) + (x_avg ** 2)) + x_avg))\n central_location[1] = 2 * atan(z_avg / (sqrt((z_avg ** 2) + (h_avg ** 2)) + h_avg))\n\n # and we place it in the appropriate spot in the array\n result[time_index][label] = central_location\n\n # reset the central location - this seems to be necessary here\n central_location = np.zeros(2)\n\n return result\n", "sub_path": "quantification.py", "file_name": "quantification.py", "file_ext": "py", "file_size_in_byte": 11018, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "numpy.ndarray", "line_number": 6, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 40, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.isin", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 86, "usage_type": "attribute"}, {"api_name": "numpy.unique", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 125, "usage_type": "attribute"}, {"api_name": "numpy.unique", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 170, "usage_type": "attribute"}, {"api_name": "numpy.unique", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 213, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 213, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 222, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 225, "usage_type": "call"}, {"api_name": "math.atan", "line_number": 229, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 229, "usage_type": "call"}, {"api_name": "math.atan", "line_number": 230, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 171, "usage_type": "attribute"}]} +{"seq_id": "191845444", "text": "\"\"\"Just roughly normalises intensities of volumes and straightens each embryo to perform some sort of\r\n manual phenotyping\"\"\"\r\nfrom lama.img_processing import normalise\r\nfrom logzero import logger as logging\r\nfrom lama import common\r\nimport os\r\nimport nrrd\r\nfrom pathlib import Path\r\nfrom scipy import ndimage\r\nimport numpy as np\r\nimport SimpleITK as sitk\r\n\r\n\r\ndef get_images(dir, s):\r\n img_list = []\r\n spec_name_list = []\r\n int_paths = common.get_file_paths(dir)\r\n\r\n # enumerating for speed only\r\n for i, img_path in enumerate(int_paths):\r\n img, img_h = nrrd.read(img_path)\r\n # only get heatmap vals inside of the mask + padding\r\n img = img[s[0].start:s[0].stop,\r\n s[1].start:s[1].stop,\r\n s[2].start:s[2].stop]\r\n spec_name_list.append(os.path.splitext(img_path.name)[0])\r\n\r\n img_list.append(img)\r\n return img_list, spec_name_list\r\n\r\n\r\ndef resample(image, transform):\r\n \"\"\"\r\n This function resamples (updates) an image using a specified transform\r\n :param image: The sitk image we are trying to transform\r\n :param transform: An sitk transform (ex. resizing, rotation, etc.\r\n :return: The transformed sitk image\r\n \"\"\"\r\n reference_image = image\r\n interpolator = sitk.sitkBSpline\r\n default_value = -1\r\n return sitk.Resample(image, reference_image, transform,\r\n interpolator, default_value)\r\n\r\n\r\ndef rotate(vols, x, y, z):\r\n # TODO: fix so it doesn't clip\r\n logging.info(f\"rotating vol with manual rotation {x, y, z}\")\r\n rotated = []\r\n for vol in vols:\r\n print(np.shape(vol))\r\n fixed = sitk.GetImageFromArray(vol.astype(np.int16), isVector=False)\r\n rigid_euler = sitk.Euler3DTransform()\r\n rigid_euler.SetRotation(x, y, z)\r\n # set center as midpoints\r\n rigid_euler.SetCenter([coord // 2 for coord in np.shape(vol)[::-1]])\r\n # rigid_euler.TransformPoint([point for point in grid for grid in img])\r\n mov = resample(fixed, rigid_euler)\r\n rotated.append(sitk.GetArrayFromImage(mov).astype(np.uint8))\r\n\r\n return rotated\r\n\r\n\r\ndef main():\r\n wt_dir = Path(\r\n \"Z:/ArkellLab/Lab Members/Kyle/PhD/vmshare/Zic2_Kumba_LAMA/210521_vis_anal/wt\")\r\n mut_dir = Path(\r\n \"Z:/ArkellLab/Lab Members/Kyle/PhD/vmshare/Zic2_Kumba_LAMA/210521_vis_anal/non_wt\")\r\n\r\n mask, mask_h = nrrd.read(\r\n \"Z:/ArkellLab/Lab Members/Kyle/PhD/vmshare/Zic2_Kumba_LAMA/210423_g_by_e_stand_out/210415_g_by_e_anal/target/stats_mask.nrrd\")\r\n\r\n pop_avg, pop_h = nrrd.read(\r\n \"Z:/ArkellLab/Lab Members/Kyle/PhD/vmshare/Zic2_Kumba_LAMA/210423_g_by_e_stand_out/210415_g_by_e_anal/target/210224_pop_avg_deformable_8.nrrd\")\r\n\r\n s = ndimage.find_objects(mask)[0]\r\n\r\n # get the images\r\n wt_imgs, wt_names = get_images(wt_dir, s)\r\n\r\n mut_imgs, mut_names = get_images(mut_dir, s)\r\n\r\n int_norm = normalise.IntensityMaskNormalise()\r\n\r\n # normalise the images\r\n int_norm.add_reference(wt_imgs)\r\n\r\n int_norm.normalise(mut_imgs)\r\n\r\n int_norm.normalise(wt_imgs)\r\n\r\n # manually orient the embryos\r\n wt_imgs = rotate(wt_imgs, -0.0635, 0.02, 0.01)\r\n\r\n mut_imgs = rotate(mut_imgs, -0.0635, 0.02, 0.01)\r\n\r\n # write files\r\n logging.info('writing files')\r\n for i, vol in enumerate(mut_imgs):\r\n nrrd.write(mut_names[i] + \".nrrd\", vol)\r\n\r\n for i, vol in enumerate(wt_imgs):\r\n nrrd.write(wt_names[i] + \".nrrd\", vol)\r\n\r\n\r\nif __name__ == '__main__':\r\n main()\r\n", "sub_path": "lama/utilities/prep_for_man_valid.py", "file_name": "prep_for_man_valid.py", "file_ext": "py", "file_size_in_byte": 3487, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "lama.common.get_file_paths", "line_number": 17, "usage_type": "call"}, {"api_name": "lama.common", "line_number": 17, "usage_type": "name"}, {"api_name": "nrrd.read", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "SimpleITK.sitkBSpline", "line_number": 40, "usage_type": "attribute"}, {"api_name": "SimpleITK.Resample", "line_number": 42, "usage_type": "call"}, {"api_name": "logzero.logger.info", "line_number": 48, "usage_type": "call"}, {"api_name": "logzero.logger", "line_number": 48, "usage_type": "name"}, {"api_name": "numpy.shape", "line_number": 51, "usage_type": "call"}, {"api_name": "SimpleITK.GetImageFromArray", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.int16", "line_number": 52, "usage_type": "attribute"}, {"api_name": "SimpleITK.Euler3DTransform", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 56, "usage_type": "call"}, {"api_name": "SimpleITK.GetArrayFromImage", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 59, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 65, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 67, "usage_type": "call"}, {"api_name": "nrrd.read", "line_number": 70, "usage_type": "call"}, {"api_name": "nrrd.read", "line_number": 73, "usage_type": "call"}, {"api_name": "scipy.ndimage.find_objects", "line_number": 76, "usage_type": "call"}, {"api_name": "scipy.ndimage", "line_number": 76, "usage_type": "name"}, {"api_name": "lama.img_processing.normalise.IntensityMaskNormalise", "line_number": 83, "usage_type": "call"}, {"api_name": "lama.img_processing.normalise", "line_number": 83, "usage_type": "name"}, {"api_name": "logzero.logger.info", "line_number": 98, "usage_type": "call"}, {"api_name": "logzero.logger", "line_number": 98, "usage_type": "name"}, {"api_name": "nrrd.write", "line_number": 100, "usage_type": "call"}, {"api_name": "nrrd.write", "line_number": 103, "usage_type": "call"}]} +{"seq_id": "189172499", "text": "# -*- coding: utf-8 -*-\n\nimport requests\nimport lxml.html\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nfrom pyvirtualdisplay import Display\n\n\n\nimport db_con\n\nURL_FII = u'https://br.tradingview.com/symbols/BMFBOVESPA-{fundo}/'\nfii_buscado = ['TBOF11', 'XPML11', 'FVBI11', 'THRA11', 'WPLZ11']\n\n\ndef get_session():\n return requests.Session()\n\n\ndef clean_text(text):\n return text.text_content().strip()\n\n\ndef get_dirver(display=True):\n print('Iniciando FIREFOX')\n if display:\n display = Display(visible=0, size=(1200, 900))\n display.start()\n else:\n display = None\n\n driver = webdriver.Firefox()\n return driver, display\n\ndef raspando_informacao(lxml_page, driver, fii):\n titulo_longo_fii = lxml_page.xpath(\n u'//div[@class=\"tv-symbol-header__long-title\"]')\n bolsa_fii = lxml_page.xpath(\n u'.//div[@class=\"tv-symbol-header__exchange\"]')\n titulo_curto_fii = lxml_page.xpath(\n u'//div[@class=\"tv-symbol-header__short-title '\n u'tv-symbol-header__short-title--with-icon\"]')\n\n print('Parseando os dados')\n valor_atual = driver.find_element_by_xpath(\n u'//div[@class=\"tv-symbol-price-quote__value js-symbol-last\"]')\n moeda = driver.find_element_by_xpath(\n u'//div[@class=\"tv-symbol-price-quote__currency js-symbol-currency\"]')\n status_mercado = driver.find_element_by_xpath(\n u'//div[@class=\"tv-symbol-price-quote__sub-line\"]')\n valor_variacao = driver.find_element_by_xpath(\n u'//div/span[@class=\"js-symbol-change tv-symbol-price-quote__change-value\"]')\n valor_variacao_porcentagem = driver.find_element_by_xpath(\n u'//div/span[@class=\"js-symbol-change-pt tv-symbol-price-quote__change-value\"]')\n valor_fechamento_dia_anterior = driver.find_elements_by_xpath(\n u'//div[contains(text(),\"Anterior\")]/../div[1]')[1]\n valor_abertura = driver.find_elements_by_xpath(\n u'//div[contains(text(),\"Abrt\")]/../div[1]')[1]\n volume = driver.find_elements_by_xpath(\n u'//div[contains(text(),\"Volume\")]/../div[1]')[1]\n valor_minimo_dia = driver.find_elements_by_xpath(\n u'//span[@class=\"js-symbol-header__range-price-l\"]')[1]\n valor_maximo_dia = driver.find_elements_by_xpath(\n u'//span[@class=\"js-symbol-header__range-price-r\"]')[1]\n\n print('Limpando os dados')\n titulo_longo_fii = clean_text(titulo_longo_fii[0])\n bolsa_fii = clean_text(bolsa_fii[0])\n titulo_curto_fii = clean_text(titulo_curto_fii[0])\n valor_atual = valor_atual.text\n moeda = moeda.text\n status_mercado = status_mercado.text\n valor_variacao = valor_variacao.text\n valor_variacao_porcentagem = valor_variacao_porcentagem.text\n valor_fechamento_dia_anterior = valor_fechamento_dia_anterior.text\n valor_abertura = valor_abertura.text\n volume = volume.text\n valor_minimo_dia = valor_minimo_dia.text\n valor_maximo_dia = valor_maximo_dia.text\n \n dados = {\n 'nome': titulo_curto_fii,\n 'nome_normalizado': titulo_curto_fii,\n 'nome_completo': titulo_longo_fii,\n 'nome_completo_normalizado': titulo_longo_fii,\n 'bolsa': bolsa_fii,\n 'status_mercado': status_mercado,\n 'data_criacao': '03/09/2019'\n }\n print('##############################')\n print('############{fundo}############'.format(fundo=fii))\n print('##############################')\n print(titulo_longo_fii)\n print(bolsa_fii)\n print(titulo_curto_fii)\n print(valor_atual)\n print(moeda)\n print(status_mercado)\n print(valor_variacao)\n print(valor_variacao_porcentagem)\n print(valor_fechamento_dia_anterior)\n print(valor_abertura)\n print(volume)\n print(valor_minimo_dia)\n print(valor_maximo_dia)\n print('')\n print('')\n print('')\n \n db_con.insert(dados)\n \n \ndef get_page_fii():\n session = get_session()\n driver, display = get_dirver()\n if fii_buscado is None:\n print('Nenhum fundo na lista, saindo...')\n return None\n\n for fii in fii_buscado:\n page = session.get(URL_FII.format(fundo=fii))\n lxml_page = lxml.html.fromstring(page.content)\n driver.get(URL_FII.format(fundo=fii))\n print(driver.current_url)\n raspando_informacao(lxml_page, driver, fii)\n\n print('Finalizando...')\n if display:\n display.stop()\n\n driver.quit()\n\nif __name__ == '__main__':\n get_page_fii()\n", "sub_path": "worker_trading.py", "file_name": "worker_trading.py", "file_ext": "py", "file_size_in_byte": 4416, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "requests.Session", "line_number": 18, "usage_type": "call"}, {"api_name": "pyvirtualdisplay.Display", "line_number": 28, "usage_type": "call"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 33, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 33, "usage_type": "name"}, {"api_name": "db_con.insert", "line_number": 111, "usage_type": "call"}, {"api_name": "lxml.html.html.fromstring", "line_number": 123, "usage_type": "call"}, {"api_name": "lxml.html.html", "line_number": 123, "usage_type": "attribute"}, {"api_name": "lxml.html", "line_number": 123, "usage_type": "name"}]} +{"seq_id": "144368936", "text": "from flask import Flask,json,jsonify,render_template\nimport pandas\nimport statistics\nfrom datetime import datetime\n\napp = Flask(__name__)\n\nCSVFILE= \"./resource/jobentry_export_2019-8-23T9_59vanilla.csv\"\n\ndef csvCoverter(column):\n file= pandas.read_csv(CSVFILE,delimiter=\";\")\n array= file[column]\n return array\n\ndef median():\n listOfPageView = csvCoverter(\"pageviews_all\")\n return statistics.median(listOfPageView)\n\ndef average():\n listOfPageView = csvCoverter(\"pageviews_all\")\n return(statistics.mean(listOfPageView))\n\ndef createArrayOfTime(endDateArray,startDateArray):\n timeArray = []\n format=\"%d.%m.%Y\"\n for (endDate, startdate) in zip(endDateArray, startDateArray):\n delta=datetime.strptime(endDate, format)-datetime.strptime(startdate, format)\n timeArray.append(delta.days)\n return timeArray\n\ndef readingTimeToClick():\n listOfPageView = csvCoverter(\"pageviews_all\")\n listOfClick = csvCoverter(\"applyclicks_all\")\n listTimeOfOpen=createArrayOfTime(csvCoverter(\"date_ends\"),csvCoverter(\"date_posted\"))\n positionList=[]\n for (pageView,click,timeOfOpen) in zip(listOfPageView,listOfClick,listTimeOfOpen):\n valuesDictionary ={\"pageView\":pageView,\"click\":click,\"timeOfOpen\":timeOfOpen}\n positionList.append(valuesDictionary)\n return positionList\n\n@app.route(\"/api/stats\")\ndef statsController():\n return jsonify(status=\"success\",stats={\"median\":median(),\"average\":average(),\"graph\":readingTimeToClick()})\n\n@app.route(\"/\")\ndef homeController():\n return render_template(\"index.html\")\n\nif __name__ == \"__main__\":\n app.run(debug=False)", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1617, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "flask.Flask", "line_number": 6, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 11, "usage_type": "call"}, {"api_name": "statistics.median", "line_number": 17, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 47, "usage_type": "call"}]} +{"seq_id": "524032856", "text": "import pybullet as p\nimport pybullet_data\nimport time\nimport math\nfrom datetime import datetime\nimport numpy as np\nimport time\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sn\n\nfrom sim_objects import Bottle, Arm\nfrom environment import Environment, Action\n\nclass MDP():\n INF = 1e10\n def __init__(self, env, run_full_mdp=True, target_type=\"const\", \n DEBUG=False, cost_based=True):\n if run_full_mdp: self.max_iters = 100\n else: self.max_iters = 10\n self.gamma = 0.8\n \n self.env = env\n self.A = self.init_action_space(run_full_mdp)\n self.env.arm.set_general_max_reach(self.contact_heights)\n print(\"MAX_REACH: %.3f\" % self.env.arm.MAX_REACH)\n self.target_type = target_type\n self.DEBUG = DEBUG\n\n # self.sim_log = dict() # (x,y,action) -> [start, end]\n\n # full state space\n if run_full_mdp:\n self.dx = self.dy = 0.1\n self.xmin, self.xmax = 0, 1.5\n self.ymin, self.ymax = 0, 1.5\n else:\n # only consider one quadrant \n self.dx = self.dy = 0.1\n self.xmin, self.xmax = 0, 0.8\n self.ymin, self.ymax = 0, 0.8\n self.X = np.arange(\n start=self.xmin, \n stop=self.xmax+self.dx, \n step=self.dx)\n self.Y = np.arange(\n start=self.ymin, \n stop=self.ymax+self.dy, \n step=self.dy)\n self.H, self.W = len(self.Y), len(self.X)\n\n self.cost_based = cost_based\n if self.cost_based:\n self.OUT_OF_BOUNDS_COST = MDP.INF\n self.V = np.ones((self.H, self.W)) * MDP.INF\n else:\n self.V = np.zeros((self.H, self.W)) # 1D array\n\n # filter out unreachable states and set their value/cost\n self.valid_states = []\n for x in self.X:\n for y in self.Y:\n dist_from_base = np.linalg.norm(\n np.array([x,y]) - self.env.arm.base_pos[:2])\n too_close = (self.env.init_reach_p * dist_from_base < \n self.env.arm.min_dist)\n too_far = dist_from_base > self.env.arm.MAX_REACH\n print(x, y, too_close, too_far)\n if (too_close or too_far):\n (xi, yi) = self.state_to_idx((x,y))\n\n if self.cost_based: self.V[yi, xi] = self.OUT_OF_BOUNDS_COST\n else: self.V[yi, xi] = -1 * self.OUT_OF_BOUNDS_COST\n \n else: self.valid_states.append((x,y))\n\n # self.P = np.zeros((self.H * self.W, len(A))) # policy as probabilities\n print(self.V)\n self.P = [[0]*self.W for i in range(self.H)]\n \n\n def state_to_idx(self, state):\n (x, y) = state\n xi = int(round((x - self.xmin)/self.dx))\n yi = int(round((y - self.ymin)/self.dy))\n\n # usually leave out-of-bounds as-is, but for special case of rounding\n # up to bound, just keep within index range\n if xi == self.W: xi -= 1\n if yi == self.H: yi -= 1\n return (xi, yi)\n\n\n def solve_mdp(self):\n np.savez(\"value_policy_iter_0\", V=self.V, P=self.P)\n for self.main_iter in range(self.max_iters):\n self.update_policy()\n self.evaluate_policy()\n np.savez(\"results/value_policy_iter_%d\" % (self.main_iter+1), V=self.V, P=self.P)\n print(\"Percent complete: %.3f\" % (self.main_iter / float(self.max_iters)))\n\n \n def evaluate_policy(self):\n \"\"\"Use current policy to estimate new value function\n \"\"\"\n print(\"Evaluting Policy to Update Value Function...\")\n # new_V = np.ones_like(self.V) * self.OUT_OF_BOUNDS_COST\n num_states = float(len(self.valid_states))\n start = time.time()\n for (x,y) in self.valid_states:\n self.env.change_bottle_pos(\n new_pos=[x, y, 0.1],\n target_type=self.target_type)\n (xi, yi) = self.state_to_idx((x,y))\n best_actions = self.P[yi][xi]\n expected_value = 0\n prob = 1./float(len(best_actions)) # uniform distb for best actions\n for ai in best_actions:\n action = self.A[ai]\n # if desired x,y,z is out of reach, skip this action\n # validity depends on contact height\n target_dist = np.linalg.norm(np.array([x,y]))\n if target_dist > self.env.arm.calc_max_horiz_dist(\n action.height):\n continue\n \n value, ns = self.env.run_sim_stochastic(action)\n try:\n (nxi, nyi) = self.state_to_idx(ns)\n expected_future_value = self.V[nyi, nxi]\n except IndexError:\n if self.cost_based:\n expected_future_value = self.OUT_OF_BOUNDS_COST\n else: expected_future_value = -1 * self.OUT_OF_BOUNDS_COST\n # expected_future_cost = 0\n expected_value += prob * (\n value + self.gamma*expected_future_value)\n \n # synchronous update\n self.V[yi, xi] = expected_value\n \n # self.V = new_V\n\n end = time.time()\n print(\"Total Runtime of Eval Policy: %.3f\" % (end-start))\n\n\n def update_policy(self):\n \"\"\"Use current value function to estimate new policy.\n \"\"\"\n print(\"Updating Policy...\")\n num_states = float(len(self.valid_states))\n total_time = 0\n \n # for each state, find best action(s) to take\n for (x,y) in self.valid_states:\n start = time.time()\n self.env.change_bottle_pos(\n new_pos=[x, y, 0.1],\n target_type=self.target_type)\n (xi, yi) = self.state_to_idx((x,y))\n best_value = 0\n best_actions = []\n sim_log = []\n for ai, action in enumerate(self.A):\n # if desired x,y,z is out of reach, skip this action\n # validity depends on contact height\n target_dist = np.linalg.norm(np.array([x,y]))\n if (target_dist > self.env.arm.calc_max_horiz_dist(\n action.height)):\n continue\n \n value, ns = self.env.run_sim_stochastic(action)\n \n try:\n (nxi, nyi) = self.state_to_idx(ns)\n expected_future_value = self.V[nyi, nxi]\n except IndexError:\n # just use value at current state if next state is out of bounds\n # expected_future_cost = self.V[yi, xi]\n if self.cost_based:\n expected_future_value = self.OUT_OF_BOUNDS_COST\n else: expected_future_value = -1 * self.OUT_OF_BOUNDS_COST\n # expected_future_cost = 0\n\n total_value = value + self.gamma*expected_future_value\n sim_log.append((ns, value, expected_future_value, total_value))\n\n if self.cost_based: found_better = (total_value < best_value)\n else: found_better = (total_value > best_value)\n if len(best_actions) == 0 or found_better:\n best_value = total_value\n best_actions = [ai]\n elif math.isclose(total_value, best_value, abs_tol=1e-6):\n best_actions.append(ai)\n\n # self.plot_sim_results(sim_log)\n if self.DEBUG:\n filename = \"logs/action_costs_of_%d_%d_iter_%d\" % (xi,yi,self.main_iter)\n np.save(filename, sim_log)\n \n self.P[yi][xi] = best_actions\n end = time.time()\n print(\"Time(s) for one state: %.3f\" % (end - start))\n total_time += (end-start)\n print(\"Total Runtime of Update Policy: %.3f\" % total_time)\n\n\n def init_action_space(self, run_full_mdp):\n A = [] # need to maintain order\n self.da = math.pi/80\n if run_full_mdp:\n self.dh = 5\n self.velocities = np.arange(start=0.1, stop=0.31, step=0.1)\n self.angle_offsets = np.arange(start=-2*self.da, stop=3*self.da, step=self.da)\n else:\n self.dh = 3\n self.velocities = np.arange(start=0.1, stop=0.21, step=0.05)\n self.angle_offsets = np.arange(start=-2*self.da, stop=3*self.da, step=self.da)\n\n self.contact_heights = np.arange(\n start=self.env.bottle.height/self.dh, \n stop=self.env.bottle.height + self.env.bottle.height/self.dh, \n step=self.env.bottle.height/self.dh)\n \n self.dr = 0.25 # proportion of max reach\n self.reach_ranges = np.arange(\n start=self.dr, \n stop=1.0+self.dr,\n step=self.dr)\n\n for h in self.contact_heights:\n for v in self.velocities:\n for a in self.angle_offsets:\n for r in self.reach_ranges:\n action = Action(angle_offset=a, velocity=v, height=h, reach_p=r)\n A.append(action)\n \n return A\n\n def plot_sim_results(self, sim_log):\n # n = len(sim_log)\n # color_vals = np.random.randint(0, 0xFFFFFF, size=n) # +1 for target\n # colors = [('#%06X' % v) for v in color_vals]\n for (_, start, end) in sim_log:\n print(start, end)\n plt.plot([start[0], end[0]], [start[1], end[1]])\n \n plt.legend([str(action) for action in self.A], loc='upper left')\n plt.show()\n\n def test_reach_p(self):\n action = self.A[0]\n action.velocity = 0.2\n action.height = self.contact_heights[-1]\n for x in self.X:\n for y in self.Y:\n # if desired x,y,z is out of reach, skip this action\n # validity depends on contact height\n dist_from_base = np.linalg.norm(np.array([x,y]))\n max_reach = self.env.arm.calc_max_horiz_dist(action.height)\n too_close = (self.env.init_reach_p * dist_from_base < \n self.env.arm.min_dist)\n too_far = dist_from_base > self.env.arm.MAX_REACH\n if (too_close or too_far):\n print(\"%.2f,%.2f,%.2f out of reach, skipping..\" % (x,y,action.height))\n continue\n \n self.env.change_bottle_pos([x, y, 0.1], \n target_type=self.target_type)\n for rp in self.reach_ranges:\n print(\"%.2f, %.2f, reach: %.1f\" % (x,y,rp))\n action.reach_p = rp\n cost, ns = self.env.run_sim(action)\n\n def test_action_space(self):\n x = 0.80\n y = 0.10\n # best action was 20\n xi, yi = self.state_to_idx((x,y))\n self.env.change_bottle_pos([x, y, 0.1], target_type=self.target_type)\n for ai, action in enumerate(self.A):\n print(ai, action)\n cost, ns = self.env.run_sim(action)\n print(cost)\n\n def view_state_space(self):\n # visualize \n action= self.A[0]\n action.velocity = 0.1\n action.reach_p = 0.25\n x = self.X[5]\n y = self.Y[7]\n for x in self.X:\n for y in self.Y:\n dist_from_base = np.linalg.norm(\n np.array([x,y]) - self.env.arm.base_pos[:2])\n if (dist_from_base < self.env.arm.min_dist or \n dist_from_base > self.env.arm.MAX_REACH):\n continue\n self.env.change_bottle_pos([x, y, 0.1], target_type=self.target_type)\n cost, ns = self.env.run_sim(action)\n dist = np.linalg.norm(ns - self.env.target_bottle_pos[:2])\n print(cost, dist, self.env.target_thresh, ns, self.env.target_bottle_pos[:2])\n\ndef main():\n # initialize simulator environment\n VISUALIZE = True\n LOGGING = False\n GRAVITY = -9.81\n RUN_FULL_MDP = False\n if VISUALIZE: p.connect(p.GUI) # or p.DIRECT for nongraphical version\n else: p.connect(p.DIRECT)\n p.setAdditionalSearchPath(pybullet_data.getDataPath())\n p.setGravity(0, 0, GRAVITY)\n planeId = p.loadURDF(Environment.plane_urdf_filepath)\n kukaId = p.loadURDF(Environment.arm_filepath, basePosition=[0, 0, 0])\n if LOGGING and VISUALIZE:\n log_id = p.startStateLogging(p.STATE_LOGGING_VIDEO_MP4, \"fully_functional.mp4\")\n\n # starting end-effector pos, not base pos\n EE_start_pos = np.array([0.2, 0.2, 0.3]).astype(float)\n base_start_ori = np.array([0, 0, 0, 1]).astype(float)\n arm = Arm(ee_start_pos=EE_start_pos, start_ori=base_start_ori,\n kuka_id=kukaId)\n\n # bottle\n bottle_start_pos = np.array([0.7, 0.6, 0.1]).astype(float)\n bottle_start_ori = np.array([0, 0, 0, 1]).astype(float)\n bottle = Bottle(start_pos=bottle_start_pos, start_ori=bottle_start_ori)\n\n N = 700\n cost_based = False\n env = Environment(arm, bottle, is_viz=VISUALIZE, N=N,\n run_full_mdp=RUN_FULL_MDP, cost_based=cost_based)\n\n solver = MDP(env, RUN_FULL_MDP, DEBUG=True, target_type=\"const\", \n cost_based=cost_based)\n # solver.test_action_space()\n # solver.solve_mdp()\n # solver.test_reach_p()\n examine_results(mdp=solver)\n # solver.view_state_space()\n # check_results(env, mdp=solver)\n # test_bottle_dynamics(env)\n # A = init_action_space(env.bottle, RUN_FULL_MDP)\n # test(env, A[0])\n # test_state_to_idx(mdp=solver)\n\n if LOGGING and VISUALIZE:\n p.stopStateLogging(log_id)\n\n # to deal with the issue of bottle being pushed out of range of arm and reaching a state with some unknown value since we can't even reach, just leave V(out of bounds states) = 0, but set every state's reward to be euclidean distance from some goal\n\ndef check_results(env: Environment, mdp: MDP):\n path = \"results/value_policy_iter_%d.npz\"\n print(\"Value Functions:\")\n policies = []\n for iter in range(1, mdp.max_iters+1):\n data = np.load(path % iter, allow_pickle=True)\n V, P = data[\"V\"], data[\"P\"]\n policies.append(P)\n print(V)\n\n print(\"Policies:\")\n for P in policies: print(P)\n\n\ndef test_state_to_idx(mdp: MDP):\n # self.dx = self.dy = 0.1\n # self.xlim, self.ylim = 1.5, 1.5\n # self.X = np.arange(start=-self.xlim, stop=self.xlim, step=self.dx)\n # self.Y = np.arange(start=-self.ylim, stop=self.ylim, step=self.dy)\n x, y = mdp.xmin, mdp.ymin\n print(mdp.state_to_idx((x,y)))\n x, y = mdp.xmax, mdp.ymax\n print(mdp.state_to_idx((x,y)))\n x, y = 0, 0\n print(mdp.state_to_idx((x,y)))\n x, y = mdp.xmax + mdp.dx*0.9, mdp.ymax + mdp.dy*0.9\n print(mdp.state_to_idx((x,y)))\n\ndef plot_heat_map(mat, horiz_ticks, vert_ticks, title, xlabel, ylabel):\n df_cm = pd.DataFrame(mat, horiz_ticks, vert_ticks)\n # plt.figure(figsize=(10,7))\n sn.set(font_scale=1.4) # for label size\n cmap = sn.cm.rocket_r\n sn.heatmap(df_cm, annot=True, annot_kws={\"size\": 16}, cmap=cmap) # font size\n plt.xlabel(xlabel)\n plt.ylabel(ylabel)\n plt.title(title)\n plt.show()\n\ndef test_bottle_dynamics(env):\n # [-0.6999999999999993, -0.29999999999999893, 0.1]\n fill_prop, lat_fric = 1.0, 0.1\n env.change_bottle_pos(new_pos=[-0.7, -0.3, 0.1])\n env.bottle.set_fill_proportion(fill_prop)\n env.bottle.lat_fric = lat_fric\n action = Action(0, 0.01, env.bottle.height / 2)\n env.run_sim(action)\n\n\ndef examine_results(mdp: MDP):\n horiz_ticks = [\"%.2f\" % x for x in mdp.X]\n vert_ticks = [\"%.2f\" % y for y in mdp.Y]\n # for iter in range(1,10):\n # data = np.load(\"results/value_policy_iter_%d.npz\" % iter, allow_pickle=True)\n # if iter > 1:\n # prev_data = np.load(\"results/value_policy_iter_%d.npz\" % (iter-1), allow_pickle=True)\n # else: prev_data = mdp.V\n # V, P = data[\"V\"], data[\"P\"]\n \n # plot_heat_map(V, horiz_ticks=horiz_ticks, vert_ticks=vert_ticks, \n # title=\"Iter %d V-table\" % iter, xlabel=\"X\", ylabel=\"Y\")\n # print(np.array_str(V, suppress_small=True, precision=2))\n # print(np.array_str(P, suppress_small=True, precision=2))\n # target = np.array([0.65, 0.55, 0.1])\n # txi, tyi = mdp.state_to_idx((target[1], target[0]))\n # print(txi, tyi)\n # print(np.array_str(V, suppress_small=True, precision=2))\n # print(np.array_str(P, suppress_small=True, precision=2))\n\n iter = 9\n data = np.load(\"good_results_arm_needs_tuning/value_policy_iter_%d.npz\" % iter, allow_pickle=True)\n V, P = data[\"V\"], data[\"P\"]\n for (x, y) in mdp.valid_states:\n print(\"x:%.2f, y:%.2f:\" % (x, y))\n (xi, yi) = mdp.state_to_idx((x,y))\n best_actions = P[yi,xi]\n print(best_actions)\n if isinstance(best_actions, list):\n for ai in best_actions:\n action = mdp.A[ai]\n print(action)\n mdp.env.change_bottle_pos([x, y, 0.1], target_type=mdp.target_type)\n cost, ns = mdp.env.run_sim_stochastic(action)\n else:\n ai = best_actions\n action = mdp.A[ai]\n print(action)\n mdp.env.change_bottle_pos([x, y, 0.1], target_type=mdp.target_type)\n cost, ns = mdp.env.run_sim_stochastic(action)\n\n # try:\n # (nxi, nyi) = mdp.state_to_idx(ns)\n # expected_future_cost = V[nyi, nxi]\n # except IndexError:\n # # just use value at current state if next state is out of bounds\n # expected_future_cost = V[yi, xi]\n # # expected_future_cost = 0\n\n # total_cost = cost + mdp.gamma*expected_future_cost\n # print(\"nyi, nxi: %d, %d, Y: %d, X: %d, cost:%.2f, future:%.2f\" \n # % (nyi, nxi, mdp.H, mdp.W, cost, expected_future_cost))\n\n \ndef test(env, action):\n X = [0.5, 1.5]\n Y = np.arange(start=-0.5, stop=1.5, step=0.1)\n velocities = np.arange(start=0.1, stop=0.31, step=0.1)\n for x in X:\n for y in Y:\n for v in velocities:\n action.velocity = v\n print(v)\n action.reach_p = 1\n dist_from_base = np.linalg.norm(\n np.array([x,y]) - env.arm.base_pos[:2])\n if (dist_from_base < env.arm.min_dist or \n dist_from_base > env.arm.MAX_REACH):\n continue\n env.change_bottle_pos([x, y, 0.1])\n expected_cost = env.run_sim(action)\n\n\nif __name__=='__main__':\n main()\n\n\n\n# Two Ideas to fix:\n# make goal actually lie within reach of arm, but what about cost at each state?\n# fix velocity by trying set max number of iters and just increasing by velocity and cap max reach\n\n", "sub_path": "deprecated_reference/arm_push_mdp.py", "file_name": "arm_push_mdp.py", "file_ext": "py", "file_size_in_byte": 18967, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.arange", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 63, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 99, "usage_type": "call"}, {"api_name": "time.time", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 122, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 122, "usage_type": "call"}, {"api_name": "time.time", "line_number": 144, "usage_type": "call"}, {"api_name": "time.time", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 168, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 168, "usage_type": "call"}, {"api_name": "math.isclose", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 200, "usage_type": "call"}, {"api_name": "time.time", "line_number": 203, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 211, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 214, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 227, "usage_type": "call"}, {"api_name": "environment.Action", "line_number": 236, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 247, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 247, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 249, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 249, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 250, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 250, "usage_type": "name"}, {"api_name": "numpy.linalg.norm", "line_number": 260, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 260, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 260, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 296, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 296, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 297, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 303, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 303, "usage_type": "attribute"}, {"api_name": "pybullet.connect", "line_number": 312, "usage_type": "call"}, {"api_name": "pybullet.GUI", "line_number": 312, "usage_type": "attribute"}, {"api_name": "pybullet.connect", "line_number": 313, "usage_type": "call"}, {"api_name": "pybullet.DIRECT", "line_number": 313, "usage_type": "attribute"}, {"api_name": "pybullet.setAdditionalSearchPath", "line_number": 314, "usage_type": "call"}, {"api_name": "pybullet_data.getDataPath", "line_number": 314, "usage_type": "call"}, {"api_name": "pybullet.setGravity", "line_number": 315, "usage_type": "call"}, {"api_name": "pybullet.loadURDF", "line_number": 316, "usage_type": "call"}, {"api_name": "environment.Environment.plane_urdf_filepath", "line_number": 316, "usage_type": "attribute"}, {"api_name": "environment.Environment", "line_number": 316, "usage_type": "name"}, {"api_name": "pybullet.loadURDF", "line_number": 317, "usage_type": "call"}, {"api_name": "environment.Environment.arm_filepath", "line_number": 317, "usage_type": "attribute"}, {"api_name": "environment.Environment", "line_number": 317, "usage_type": "name"}, {"api_name": "pybullet.startStateLogging", "line_number": 319, "usage_type": "call"}, {"api_name": "pybullet.STATE_LOGGING_VIDEO_MP4", "line_number": 319, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 322, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 323, "usage_type": "call"}, {"api_name": "sim_objects.Arm", "line_number": 324, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 328, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 329, "usage_type": "call"}, {"api_name": "sim_objects.Bottle", "line_number": 330, "usage_type": "call"}, {"api_name": "environment.Environment", "line_number": 334, "usage_type": "call"}, {"api_name": "pybullet.stopStateLogging", "line_number": 351, "usage_type": "call"}, {"api_name": "environment.Environment", "line_number": 355, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 360, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 384, "usage_type": "call"}, {"api_name": "seaborn.set", "line_number": 386, "usage_type": "call"}, {"api_name": "seaborn.cm", "line_number": 387, "usage_type": "attribute"}, {"api_name": "seaborn.heatmap", "line_number": 388, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 389, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 389, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 390, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 390, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 391, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 391, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 392, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 392, "usage_type": "name"}, {"api_name": "environment.Action", "line_number": 400, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 425, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 460, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 461, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 468, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 468, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 469, "usage_type": "call"}]} +{"seq_id": "647917665", "text": "#!/usr/bin/env python\n\n# https://www.youtube.com/watch?v=HM-xG0qXaeA&&\n# ffmpeg -i R2D2\\ all\\ Sounds\\ -\\ Star\\ Wars\\ free\\ sounds.mp4 -ss 48 -t 10 R2D2.wav\n\nimport rospy\nimport time, socket, os\nimport netifaces as ni\nimport rospkg\n\nfrom sound_play.libsoundplay import SoundClient\nimport actionlib\nfrom sound_play.msg import SoundRequestAction\n\nif __name__ == \"__main__\":\n rospy.init_node(\"boot_sound\")\n sound = SoundClient()\n time.sleep(1) # ???\n ac = actionlib.SimpleActionClient('sound_play', SoundRequestAction)\n ac.wait_for_server()\n if len(ni.ifaddresses('eth0')) > 2 :\n ip = ni.ifaddresses('eth0')[2][0]['addr']\n elif len(ni.ifaddresses('wlan0')) > 2 :\n ip = ni.ifaddresses('wlan0')[2][0]['addr']\n else:\n ip = None\n\n # play sound\n rospack = rospkg.RosPack()\n wav_file = os.path.join(rospack.get_path(\"jsk_fetch_startup\"),\"data/boot_sound.wav\")\n rospy.loginfo(\"Playing {}\".format(wav_file))\n sound.playWave(wav_file)\n time.sleep(10) # make sure to topic is going out\n\n # notify ip address\n ip_text = \"My internet address is {}\".format(ip)\n rospy.loginfo(ip_text)\n ip_text = ip_text.replace('.', ', ')\n sound.say(ip_text)\n time.sleep(1) # make sure to topic is going out\n\n\n\n\n\n", "sub_path": "jsk_fetch_robot/jsk_fetch_startup/scripts/boot_sound.py", "file_name": "boot_sound.py", "file_ext": "py", "file_size_in_byte": 1261, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "rospy.init_node", "line_number": 16, "usage_type": "call"}, {"api_name": "sound_play.libsoundplay.SoundClient", "line_number": 17, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 18, "usage_type": "call"}, {"api_name": "actionlib.SimpleActionClient", "line_number": 19, "usage_type": "call"}, {"api_name": "sound_play.msg.SoundRequestAction", "line_number": 19, "usage_type": "argument"}, {"api_name": "netifaces.ifaddresses", "line_number": 21, "usage_type": "call"}, {"api_name": "netifaces.ifaddresses", "line_number": 22, "usage_type": "call"}, {"api_name": "netifaces.ifaddresses", "line_number": 23, "usage_type": "call"}, {"api_name": "netifaces.ifaddresses", "line_number": 24, "usage_type": "call"}, {"api_name": "rospkg.RosPack", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "rospy.loginfo", "line_number": 31, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 33, "usage_type": "call"}, {"api_name": "rospy.loginfo", "line_number": 37, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "65907619", "text": "import os\nfrom setuptools import setup, find_packages # 这个包没有的可以pip一下\n\nhere = os.path.abspath(os.path.dirname(__file__))\nwith open(os.path.join(here, \"README.rst\")) as f:\n DESCRIPTION = f.read()\n\nsetup(\n name=\"DobotRPC\", # 这里是pip项目发布的名称\n version=\"3.3.4\", # 版本号，数值大的会优先被pip\n keywords=[\"websocket\", \"JSON-RPC\", \"asyncio\", \"Dobot\", \"Dobotlink\"],\n description=\"Dobotlink communication module\",\n long_description=DESCRIPTION,\n long_description_content_type=\"text/markdown\",\n license=\"Apache Licence\",\n\n author=\"songlijun\",\n author_email=\"songlijun@dobot.cc\",\n\n packages=find_packages(),\n include_package_data=True,\n platforms=\"any\",\n install_requires=[\"websockets\", \"asyncio\",\n \"colorlog\", \"demjson\"]\n)\n", "sub_path": "pypi_install_script/DobotRPC-3.3.4.tar/setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 828, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "os.path.abspath", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "setuptools.setup", "line_number": 8, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "97372913", "text": "from django.conf.urls import url\nfrom django.conf import settings\nfrom haystack.query import SearchQuerySet\nfrom haystack.views import search_view_factory\n\nfrom . import views, forms \n\ndef get_sqs(facets):\n \"\"\"\n Return the SQS required by a the Haystack search view\n \"\"\"\n # Build SQS based on the OSCAR_SEARCH_FACETS settings\n sqs = SearchQuerySet()\n if facets is not None:\n for facet in facets['fields'].values():\n options = facet.get('options', {})\n sqs = sqs.facet(facet['field'], **options)\n for facet in facets['queries'].values():\n for query in facet['queries']:\n sqs = sqs.query_facet(facet['field'], query[1])\n return sqs\n\nurlpatterns = [\n url(r'^$', search_view_factory(\n view_class=views.FacetedSearchView,\n form_class=forms.SearchForm,\n searchqueryset=get_sqs(getattr(settings, 'BLOG_SEARCH_FACETS', None))),\n name='search'),\n]\n", "sub_path": "demoblog/apps/search/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 953, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "haystack.query.SearchQuerySet", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 24, "usage_type": "call"}, {"api_name": "haystack.views.search_view_factory", "line_number": 24, "usage_type": "call"}, {"api_name": "django.conf.settings", "line_number": 27, "usage_type": "argument"}]} +{"seq_id": "319710441", "text": "from django.shortcuts import render_to_response, get_object_or_404, redirect\nfrom bd.models import Alumno\nfrom grupos.models import Carrera, Grupo\n\ndef documentos(request,pk):\n grupo = Grupo.objects.filter(pk=pk).get()\n alumnos = Alumno.objects.filter(grupo=grupo)\n alumno = alumnos[0]\n\n\n data = {\n 'alumnos': alumnos,\n 'alumno': alumno,\n 'carrera': grupo.carrera,\n 'grupo': pk,\n }\n\n return render_to_response('documentos.html', data)\n\nfrom bd.forms import AlumnoForm\nfrom django.template import RequestContext\n\ndef documentos_create(request, carrera, grupo):\n mensaje = False\n\n form = AlumnoForm(request.POST or None)\n\n if 'Cancelar' in request.POST:\n return redirect('documentos', grupo)\n\n if form.is_valid():\n form.save()\n if 'Guardar' in request.POST:\n mensaje = \"Alumno guardado\"\n form = AlumnoForm()\n\n\n data = {\n 'form': form,\n 'carrera': carrera,\n 'mensaje': mensaje\n }\n\n return render_to_response(\"grupo_create.html\", data, context_instance=RequestContext(request))\n\n\ndef documentos_update(request, pk):\n alumno = get_object_or_404(Alumno, pk=pk)\n \n if 'Cancelar' in request.POST:\n return redirect('documentos', alumno.grupo.carrera, alumno.grupo.id)\n\n form = AlumnoForm(request.POST or None, instance=alumno)\n if form.is_valid():\n form.save()\n if 'Guardar' in request.POST:\n return redirect('documentos', alumno.grupo.id)\n\n return render_to_response(\"documentos_update.html\", {'form': form}, context_instance=RequestContext(request))\n\nfrom django.http import Http404\n\ndef searchStudent(request):\n query = request.GET['query']\n\n if query:\n try:\n query = int(query)\n alumnos = Alumno.objects.filter(numCtrl__contains=query)\n except:\n alumnos = Alumno.objects.filter(nombre__contains=query)\n else:\n raise Http404\n\n data = {\n 'alumnos': alumnos,\n 'query': query\n }\n\n return render_to_response(\"searchStudent.html\", data, context_instance=RequestContext(request))", "sub_path": "bd/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2131, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "grupos.models.Grupo.objects.filter", "line_number": 6, "usage_type": "call"}, {"api_name": "grupos.models.Grupo.objects", "line_number": 6, "usage_type": "attribute"}, {"api_name": "grupos.models.Grupo", "line_number": 6, "usage_type": "name"}, {"api_name": "bd.models.Alumno.objects.filter", "line_number": 7, "usage_type": "call"}, {"api_name": "bd.models.Alumno.objects", "line_number": 7, "usage_type": "attribute"}, {"api_name": "bd.models.Alumno", "line_number": 7, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 18, "usage_type": "call"}, {"api_name": "bd.forms.AlumnoForm", "line_number": 26, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 29, "usage_type": "call"}, {"api_name": "bd.forms.AlumnoForm", "line_number": 35, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 44, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 44, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 48, "usage_type": "call"}, {"api_name": "bd.models.Alumno", "line_number": 48, "usage_type": "argument"}, {"api_name": "django.shortcuts.redirect", "line_number": 51, "usage_type": "call"}, {"api_name": "bd.forms.AlumnoForm", "line_number": 53, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 57, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 59, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 59, "usage_type": "call"}, {"api_name": "bd.models.Alumno.objects.filter", "line_number": 69, "usage_type": "call"}, {"api_name": "bd.models.Alumno.objects", "line_number": 69, "usage_type": "attribute"}, {"api_name": "bd.models.Alumno", "line_number": 69, "usage_type": "name"}, {"api_name": "bd.models.Alumno.objects.filter", "line_number": 71, "usage_type": "call"}, {"api_name": "bd.models.Alumno.objects", "line_number": 71, "usage_type": "attribute"}, {"api_name": "bd.models.Alumno", "line_number": 71, "usage_type": "name"}, {"api_name": "django.http.Http404", "line_number": 73, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 80, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 80, "usage_type": "call"}]} +{"seq_id": "646322254", "text": "from django.http import HttpResponse\nfrom django.shortcuts import render, redirect \nfrom .models import Patient , Doctor , Appointment , CustomUser\nfrom .forms import CodeForm #, SymptomAppointmentForm\nfrom django.contrib.auth.decorators import login_required, permission_required\nfrom django.contrib.auth.forms import AuthenticationForm\nfrom django.contrib.auth import authenticate, login\nimport pdb\n\n# Create your views here.\n\ndef verify_view(request, *args,**kwargs):\n form = CodeForm(request.POST or None)\n request.session['pk'] = request.user.pk\n pk = request.session.get('pk')\n if pk:\n user = CustomUser.objects.get(pk=pk)\n code = user.code\n if not request.POST and user.is_active:\n print ('User is Active')\n pass\n if form.is_valid():\n num=form.cleaned_data.get('number')\n print ('CODE from DB',code,'CODE entered by', user, num)\n if str(code) == num:\n print ('OTP Validated , Proceeding for Login.')\n code.save()\n login(request, user)\n return redirect(profile_view)\n else:\n return render (request, \"auth.html\", {'form': form} )\n return render (request, \"verify.html\", {'form': form} )\n\ndef profile_view (request, *args,**kwargs):\n current_user = CustomUser.objects.get(username = request.user)\n if current_user.is_doctor:\n obj = Doctor.objects.get(username = request.user)\n aobj= Appointment.objects.get(doctor_id = request.user)\n temp = Patient.objects.get(username = aobj.patient_id) \n\n else :\n obj = Patient.objects.get(username = request.user)\n aobj= Appointment.objects.get(patient_id = request.user)\n temp = Doctor.objects.get(username = aobj.doctor_id)\n context = {\n 'object': obj ,\n 'aobject': aobj,\n 'Doctor' : temp\n }\n return render(request, \"profile.html\", context )", "sub_path": "ASMIS/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1898, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "forms.CodeForm", "line_number": 13, "usage_type": "call"}, {"api_name": "models.CustomUser.objects.get", "line_number": 17, "usage_type": "call"}, {"api_name": "models.CustomUser.objects", "line_number": 17, "usage_type": "attribute"}, {"api_name": "models.CustomUser", "line_number": 17, "usage_type": "name"}, {"api_name": "django.contrib.auth.login", "line_number": 28, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 29, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 31, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 32, "usage_type": "call"}, {"api_name": "models.CustomUser.objects.get", "line_number": 35, "usage_type": "call"}, {"api_name": "models.CustomUser.objects", "line_number": 35, "usage_type": "attribute"}, {"api_name": "models.CustomUser", "line_number": 35, "usage_type": "name"}, {"api_name": "models.Doctor.objects.get", "line_number": 37, "usage_type": "call"}, {"api_name": "models.Doctor.objects", "line_number": 37, "usage_type": "attribute"}, {"api_name": "models.Doctor", "line_number": 37, "usage_type": "name"}, {"api_name": "models.Appointment.objects.get", "line_number": 38, "usage_type": "call"}, {"api_name": "models.Appointment.objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "models.Appointment", "line_number": 38, "usage_type": "name"}, {"api_name": "models.Patient.objects.get", "line_number": 39, "usage_type": "call"}, {"api_name": "models.Patient.objects", "line_number": 39, "usage_type": "attribute"}, {"api_name": "models.Patient", "line_number": 39, "usage_type": "name"}, {"api_name": "models.Patient.objects.get", "line_number": 42, "usage_type": "call"}, {"api_name": "models.Patient.objects", "line_number": 42, "usage_type": "attribute"}, {"api_name": "models.Patient", "line_number": 42, "usage_type": "name"}, {"api_name": "models.Appointment.objects.get", "line_number": 43, "usage_type": "call"}, {"api_name": "models.Appointment.objects", "line_number": 43, "usage_type": "attribute"}, {"api_name": "models.Appointment", "line_number": 43, "usage_type": "name"}, {"api_name": "models.Doctor.objects.get", "line_number": 44, "usage_type": "call"}, {"api_name": "models.Doctor.objects", "line_number": 44, "usage_type": "attribute"}, {"api_name": "models.Doctor", "line_number": 44, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "557009611", "text": "import random\n\nfrom django import forms\nfrom django.contrib.auth.models import User\nfrom django.forms import ModelForm\n\nfrom quizes.models import *\n\n\nclass QuizAddForm(forms.Form):\n authors = []\n for author in Author.objects.all():\n authors.append((author.pk, author.first_name))\n author = forms.ChoiceField(choices=authors, label='Autor:', widget=forms.Select(\n attrs={\n 'class': 'form-control'\n }\n ))\n category = forms.ChoiceField(choices=Question.categories, label='kategoria:', widget=forms.Select(\n attrs={\n 'class': 'form-control'\n }\n ))\n question = forms.CharField(max_length=256, label='Pytanie:', widget=forms.TextInput(\n attrs={\n 'class': 'form-control'\n }\n ))\n answer_1 = forms.CharField(max_length=256, label='Odpowiedź 1:', widget=forms.TextInput(\n attrs={\n 'class': 'form-control'\n }\n ))\n answer_2 = forms.CharField(max_length=256, label='Odpowiedź 2:', widget=forms.TextInput(\n attrs={\n 'class': 'form-control'\n }\n ))\n answer_3 = forms.CharField(max_length=256, label='Odpowiedź 3:', widget=forms.TextInput(\n attrs={\n 'class': 'form-control'\n }\n ))\n answer_4 = forms.CharField(max_length=256, label='Odpowiedź 4:', widget=forms.TextInput(\n attrs={\n 'class': 'form-control'\n }\n ))\n correct_ans = forms.ChoiceField(label='Poprawna odpowiedź', choices=(\n (1, 'Odp 1'),\n (2, 'Odp 2'),\n (3, 'Odp 3'),\n (4, 'Odp 4'),\n ), widget=forms.Select(\n attrs={\n 'class': 'form-control'\n }\n ))\n\n def save(self):\n answers = []\n answers.append(self.cleaned_data['answer_1'])\n answers.append(self.cleaned_data['answer_2'])\n answers.append(self.cleaned_data['answer_3'])\n answers.append(self.cleaned_data['answer_4'])\n author = Author.objects.get(pk=int(self.cleaned_data['author']))\n que = self.cleaned_data['question']\n correct_ans = int(self.cleaned_data['correct_ans'])\n category = self.cleaned_data['category']\n question = Question.objects.create(category=int(category), question=que, author=author)\n for i in range(len(answers)):\n if i + 1 == correct_ans:\n Answer.objects.create(answer=answers[i], question=question, is_correct=True)\n else:\n Answer.objects.create(answer=answers[i], question=question)\n return True\n", "sub_path": "quizes/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 2551, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "django.forms.Form", "line_number": 10, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 10, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 14, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 14, "usage_type": "call"}, {"api_name": "django.forms.ChoiceField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 19, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 19, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 24, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 24, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 29, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 29, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 34, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 34, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 34, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 39, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 39, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 39, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 44, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 44, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 44, "usage_type": "call"}, {"api_name": "django.forms.ChoiceField", "line_number": 49, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 49, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 54, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 54, "usage_type": "name"}]} +{"seq_id": "441256253", "text": "import numpy as np\nimport enum as e\n\nclass CarConverter():\n \"\"\"This will transform the car data\"\"\"\n\n def from_csv(self, fname):\n \"Loads a dataset from two files\"\n for line in open(fname):\n sl = line.split(\",\")\n sl[-1] = sl[-1][:-1]\n self.l.append(sl)\n return self.l\n\n def apply_enums(self, line): \n fn = lambda e, cel: e.get_by_type(cel)\n return map(fn, self.enums, line)\n\n def convert(self):\n return map(self.apply_enums, self.l)\n\n def __init__(self, fname):\n self.l = []\n self.from_csv(fname)\n\n self.enums = [\n e.Enum(\"vhigh\", \"high\", \"med\", \"low\"),\n e.Enum(\"vhigh\", \"high\", \"med\", \"low\"),\n e.Enum(\"2\", \"3\", \"4\", \"5more\"),\n e.Enum(\"2\", \"4\", \"more\"),\n e.Enum(\"small\", \"med\", \"big\"),\n e.Enum(\"low\", \"med\", \"high\"),\n e.Enum(\"unacc\", \"acc\", \"good\", \"vgood\")\n ]\n\n", "sub_path": "prove02-knn/car_converter.py", "file_name": "car_converter.py", "file_ext": "py", "file_size_in_byte": 991, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "enum.get_by_type", "line_number": 16, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 27, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 28, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 29, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 30, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 31, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 32, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "88166368", "text": "from typing import (\n cast,\n AsyncIterator,\n Dict,\n FrozenSet,\n List,\n Set,\n Tuple,\n Type,\n)\n\nfrom eth_typing import (\n Hash32,\n)\n\nfrom cancel_token import CancelToken\n\nimport ssz\n\nfrom p2p import protocol\nfrom p2p.peer import (\n BasePeer,\n)\nfrom p2p.protocol import Command\n\nfrom eth.exceptions import BlockNotFound\n\nfrom eth2.beacon.chains.base import BaseBeaconChain\n\nfrom eth2.beacon.types.blocks import (\n BaseBeaconBlock,\n BeaconBlock,\n)\nfrom eth2.beacon.typing import (\n Slot,\n)\n\nfrom trinity._utils.shellart import (\n bold_red,\n)\nfrom trinity._utils.les import (\n gen_request_id,\n)\nfrom trinity.db.beacon.chain import BaseAsyncBeaconChainDB\nfrom trinity.protocol.common.servers import BaseRequestServer\nfrom trinity.protocol.bcc.commands import (\n BeaconBlocks,\n BeaconBlocksMessage,\n GetBeaconBlocks,\n GetBeaconBlocksMessage,\n NewBeaconBlock,\n NewBeaconBlockMessage,\n)\nfrom trinity.protocol.bcc.peer import (\n BCCPeer,\n BCCPeerPool,\n)\n\n\nclass BCCRequestServer(BaseRequestServer):\n subscription_msg_types: FrozenSet[Type[Command]] = frozenset({\n GetBeaconBlocks,\n })\n\n def __init__(self,\n db: BaseAsyncBeaconChainDB,\n peer_pool: BCCPeerPool,\n token: CancelToken = None) -> None:\n super().__init__(peer_pool, token)\n self.db = db\n\n async def _handle_msg(self, base_peer: BasePeer, cmd: Command,\n msg: protocol._DecodedMsgType) -> None:\n peer = cast(BCCPeer, base_peer)\n self.logger.debug(\"cmd %s\" % cmd)\n if isinstance(cmd, GetBeaconBlocks):\n await self._handle_get_beacon_blocks(peer, cast(GetBeaconBlocksMessage, msg))\n else:\n raise Exception(f\"Invariant: Only subscribed to {self.subscription_msg_types}\")\n\n async def _handle_get_beacon_blocks(self, peer: BCCPeer, msg: GetBeaconBlocksMessage) -> None:\n if not peer.is_operational:\n return\n\n request_id = msg[\"request_id\"]\n max_blocks = msg[\"max_blocks\"]\n block_slot_or_root = msg[\"block_slot_or_root\"]\n\n try:\n if isinstance(block_slot_or_root, int):\n # TODO: pass accurate `block_class: Type[BaseBeaconBlock]` under\n # per BeaconStateMachine fork\n start_block = await self.db.coro_get_canonical_block_by_slot(\n Slot(block_slot_or_root),\n BeaconBlock,\n )\n elif isinstance(block_slot_or_root, bytes):\n # TODO: pass accurate `block_class: Type[BaseBeaconBlock]` under\n # per BeaconStateMachine fork\n start_block = await self.db.coro_get_block_by_root(\n Hash32(block_slot_or_root),\n BeaconBlock,\n )\n else:\n raise TypeError(\n f\"Invariant: unexpected type for 'block_slot_or_root': \"\n f\"{type(block_slot_or_root)}\"\n )\n except BlockNotFound:\n start_block = None\n\n if start_block is not None:\n self.logger.debug2(\n \"%s requested %d blocks starting with %s\",\n peer,\n max_blocks,\n start_block,\n )\n blocks = tuple([b async for b in self._get_blocks(start_block, max_blocks)])\n\n else:\n self.logger.debug2(\"%s requested unknown block %s\", block_slot_or_root)\n blocks = ()\n\n self.logger.debug2(\"Replying to %s with %d blocks\", peer, len(blocks))\n peer.sub_proto.send_blocks(blocks, request_id)\n\n async def _get_blocks(self,\n start_block: BaseBeaconBlock,\n max_blocks: int) -> AsyncIterator[BaseBeaconBlock]:\n if max_blocks < 0:\n raise Exception(\"Invariant: max blocks cannot be negative\")\n\n if max_blocks == 0:\n return\n\n yield start_block\n\n try:\n # ensure only a connected chain is returned (breaks might occur if the start block is\n # not part of the canonical chain or if the canonical chain changes during execution)\n start = start_block.slot + 1\n end = start + max_blocks - 1\n parent = start_block\n for slot in range(start, end):\n # TODO: pass accurate `block_class: Type[BaseBeaconBlock]` under\n # per BeaconStateMachine fork\n block = await self.db.coro_get_canonical_block_by_slot(slot, BeaconBlock)\n if block.previous_block_root == parent.signing_root:\n yield block\n else:\n break\n parent = block\n except BlockNotFound:\n return\n\n\n# FIXME: `BaseReceiveServer` is the same as `BaseRequestServer`.\n# Since it's not settled that a `BaseReceiveServer` is needed and so\n# in order not to pollute /trinity/protocol/common/servers.py,\n# add the `BaseReceiveServer` here instead.\nclass BaseReceiveServer(BaseRequestServer):\n pass\n\n\nclass OrphanBlockPool:\n # TODO: can probably use lru-cache or even database\n _pool: Set[BaseBeaconBlock]\n\n def __init__(self) -> None:\n self._pool = set()\n\n def get(self, block_root: Hash32) -> BaseBeaconBlock:\n for block in self._pool:\n if block.signing_root == block_root:\n return block\n raise BlockNotFound(f\"No block with signing_root {block_root} is found\")\n\n def add(self, block: BaseBeaconBlock) -> None:\n if block in self._pool:\n return\n self._pool.add(block)\n\n def pop_children(self, block: BaseBeaconBlock) -> Tuple[BaseBeaconBlock, ...]:\n children = tuple(\n orphan_block\n for orphan_block in self._pool\n if orphan_block.previous_block_root == block.signing_root\n )\n self._pool.difference_update(children)\n return children\n\n\nclass BCCReceiveServer(BaseReceiveServer):\n subscription_msg_types: FrozenSet[Type[Command]] = frozenset({\n BeaconBlocks,\n NewBeaconBlock,\n })\n\n map_request_id_block_root: Dict[int, Hash32]\n orphan_block_pool: OrphanBlockPool\n\n def __init__(\n self,\n chain: BaseBeaconChain,\n peer_pool: BCCPeerPool,\n token: CancelToken = None) -> None:\n super().__init__(peer_pool, token)\n self.chain = chain\n self.map_request_id_block_root = {}\n self.orphan_block_pool = OrphanBlockPool()\n\n async def _handle_msg(self, base_peer: BasePeer, cmd: Command,\n msg: protocol._DecodedMsgType) -> None:\n peer = cast(BCCPeer, base_peer)\n self.logger.debug(\"cmd %s\" % cmd)\n if isinstance(cmd, NewBeaconBlock):\n await self._handle_new_beacon_block(peer, cast(NewBeaconBlockMessage, msg))\n elif isinstance(cmd, BeaconBlocks):\n await self._handle_beacon_blocks(peer, cast(BeaconBlocksMessage, msg))\n else:\n raise Exception(f\"Invariant: Only subscribed to {self.subscription_msg_types}\")\n\n async def _handle_beacon_blocks(self, peer: BCCPeer, msg: BeaconBlocksMessage) -> None:\n if not peer.is_operational:\n return\n request_id = msg[\"request_id\"]\n if request_id not in self.map_request_id_block_root:\n raise Exception(f\"request_id={request_id} is not found\")\n encoded_blocks = msg[\"encoded_blocks\"]\n # TODO: remove this condition check in the future, when we start requesting more than one\n # block at a time.\n if len(encoded_blocks) != 1:\n raise Exception(\"should only receive 1 block from our requests\")\n resp_block = ssz.decode(encoded_blocks[0], BeaconBlock)\n if resp_block.signing_root != self.map_request_id_block_root[request_id]:\n raise Exception(\n f\"block signing_root {resp_block.signing_root} does not correpond to\"\n \"the one we requested\"\n )\n self.logger.debug(f\"received request_id={request_id}, resp_block={resp_block}\")\n self._try_import_or_handle_orphan(resp_block)\n del self.map_request_id_block_root[request_id]\n\n async def _handle_new_beacon_block(self, peer: BCCPeer, msg: NewBeaconBlockMessage) -> None:\n if not peer.is_operational:\n return\n encoded_block = msg[\"encoded_block\"]\n block = ssz.decode(encoded_block, BeaconBlock)\n if self._is_block_seen(block):\n raise Exception(f\"block {block} is seen before\")\n self.logger.debug(f\"received block={block}\")\n # TODO: check the proposer signature before importing the block\n self._try_import_or_handle_orphan(block)\n # TODO: relay the block if it is valid\n\n def _try_import_or_handle_orphan(self, block: BeaconBlock) -> None:\n blocks_to_be_imported: List[BeaconBlock] = []\n\n blocks_to_be_imported.append(block)\n while len(blocks_to_be_imported) != 0:\n block = blocks_to_be_imported.pop()\n # try to import the block\n if not self._is_block_root_in_db(block.previous_block_root):\n self.logger.debug(f\"found orphan block={block}\")\n # if failed, add the block and the rest of the queue back to the pool\n self.orphan_block_pool.add(block)\n self._request_block_by_root(block_root=block.previous_block_root)\n continue\n # only import the block when its parent is in db\n self.logger.debug(f\"try to import block={block}\")\n self.chain.import_block(block)\n self.logger.debug(f\"successfully imported block={block}\")\n\n # if succeeded, handle the orphan blocks which depend on this block.\n matched_orphan_blocks = self.orphan_block_pool.pop_children(block)\n if len(matched_orphan_blocks) > 0:\n self.logger.debug(\n f\"blocks {matched_orphan_blocks} match their parent {block}\"\n )\n blocks_to_be_imported.extend(matched_orphan_blocks)\n\n def _request_block_by_root(self, block_root: Hash32) -> None:\n for peer in self._peer_pool.connected_nodes.values():\n peer = cast(BCCPeer, peer)\n request_id = gen_request_id()\n self.logger.debug(\n bold_red(f\"send block request to: request_id={request_id}, peer={peer}\")\n )\n self.map_request_id_block_root[request_id] = block_root\n peer.sub_proto.send_get_blocks(\n block_root,\n max_blocks=1,\n request_id=request_id,\n )\n\n def _is_block_root_in_orphan_block_pool(self, block_root: Hash32) -> bool:\n try:\n self.orphan_block_pool.get(block_root=block_root)\n return True\n except BlockNotFound:\n return False\n\n def _is_block_root_in_db(self, block_root: Hash32) -> bool:\n try:\n self.chain.get_block_by_root(block_root=block_root)\n return True\n except BlockNotFound:\n return False\n\n def _is_block_root_seen(self, block_root: Hash32) -> bool:\n if self._is_block_root_in_orphan_block_pool(block_root=block_root):\n return True\n return self._is_block_root_in_db(block_root=block_root)\n\n def _is_block_seen(self, block: BaseBeaconBlock) -> bool:\n return self._is_block_root_seen(block_root=block.signing_root)\n", "sub_path": "trinity/protocol/bcc/servers.py", "file_name": "servers.py", "file_ext": "py", "file_size_in_byte": 11569, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "trinity.protocol.common.servers.BaseRequestServer", "line_number": 60, "usage_type": "name"}, {"api_name": "typing.FrozenSet", "line_number": 61, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 61, "usage_type": "name"}, {"api_name": "p2p.protocol.Command", "line_number": 61, "usage_type": "name"}, {"api_name": "trinity.protocol.bcc.commands.GetBeaconBlocks", "line_number": 62, "usage_type": "name"}, {"api_name": "trinity.db.beacon.chain.BaseAsyncBeaconChainDB", "line_number": 66, "usage_type": "name"}, {"api_name": "trinity.protocol.bcc.peer.BCCPeerPool", "line_number": 67, "usage_type": "name"}, {"api_name": "cancel_token.CancelToken", "line_number": 68, "usage_type": "name"}, {"api_name": "p2p.peer.BasePeer", "line_number": 72, "usage_type": "name"}, {"api_name": "p2p.protocol.Command", "line_number": 72, "usage_type": "name"}, {"api_name": "p2p.protocol._DecodedMsgType", "line_number": 73, "usage_type": "attribute"}, {"api_name": "p2p.protocol", "line_number": 73, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 74, "usage_type": "call"}, {"api_name": "trinity.protocol.bcc.peer.BCCPeer", "line_number": 74, "usage_type": "argument"}, {"api_name": "trinity.protocol.bcc.commands.GetBeaconBlocks", "line_number": 76, "usage_type": "argument"}, {"api_name": "typing.cast", "line_number": 77, "usage_type": "call"}, {"api_name": "trinity.protocol.bcc.commands.GetBeaconBlocksMessage", "line_number": 77, "usage_type": "argument"}, {"api_name": "trinity.protocol.bcc.peer.BCCPeer", "line_number": 81, "usage_type": "name"}, {"api_name": "trinity.protocol.bcc.commands.GetBeaconBlocksMessage", "line_number": 81, "usage_type": "name"}, {"api_name": "eth2.beacon.types.blocks.BeaconBlock", "line_number": 95, "usage_type": "argument"}, {"api_name": "eth2.beacon.typing.Slot", "line_number": 94, "usage_type": "call"}, {"api_name": "eth2.beacon.types.blocks.BeaconBlock", "line_number": 102, "usage_type": "argument"}, {"api_name": "eth_typing.Hash32", "line_number": 101, "usage_type": "call"}, {"api_name": "eth.exceptions.BlockNotFound", "line_number": 109, "usage_type": "name"}, {"api_name": "eth2.beacon.types.blocks.BaseBeaconBlock", "line_number": 129, "usage_type": "name"}, {"api_name": "eth2.beacon.types.blocks.BeaconBlock", "line_number": 148, "usage_type": "argument"}, {"api_name": "eth.exceptions.BlockNotFound", "line_number": 154, "usage_type": "name"}, {"api_name": "typing.AsyncIterator", "line_number": 130, "usage_type": "name"}, {"api_name": "eth2.beacon.types.blocks.BaseBeaconBlock", "line_number": 130, "usage_type": "name"}, {"api_name": "trinity.protocol.common.servers.BaseRequestServer", "line_number": 162, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 168, "usage_type": "name"}, {"api_name": "eth2.beacon.types.blocks.BaseBeaconBlock", "line_number": 168, "usage_type": "name"}, {"api_name": "eth_typing.Hash32", "line_number": 173, "usage_type": "name"}, {"api_name": "eth.exceptions.BlockNotFound", "line_number": 177, "usage_type": "call"}, {"api_name": "eth2.beacon.types.blocks.BaseBeaconBlock", "line_number": 173, "usage_type": "name"}, {"api_name": "eth2.beacon.types.blocks.BaseBeaconBlock", "line_number": 179, "usage_type": "name"}, {"api_name": "eth2.beacon.types.blocks.BaseBeaconBlock", "line_number": 184, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 184, "usage_type": "name"}, {"api_name": "typing.FrozenSet", "line_number": 195, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 195, "usage_type": "name"}, {"api_name": "p2p.protocol.Command", "line_number": 195, "usage_type": "name"}, {"api_name": "trinity.protocol.bcc.commands.BeaconBlocks", "line_number": 196, "usage_type": "name"}, {"api_name": "trinity.protocol.bcc.commands.NewBeaconBlock", "line_number": 197, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 200, "usage_type": "name"}, {"api_name": "eth_typing.Hash32", "line_number": 200, "usage_type": "name"}, {"api_name": "eth2.beacon.chains.base.BaseBeaconChain", "line_number": 205, "usage_type": "name"}, {"api_name": "trinity.protocol.bcc.peer.BCCPeerPool", "line_number": 206, "usage_type": "name"}, {"api_name": "cancel_token.CancelToken", "line_number": 207, "usage_type": "name"}, {"api_name": "p2p.peer.BasePeer", "line_number": 213, "usage_type": "name"}, {"api_name": "p2p.protocol.Command", "line_number": 213, "usage_type": "name"}, {"api_name": "p2p.protocol._DecodedMsgType", "line_number": 214, "usage_type": "attribute"}, {"api_name": "p2p.protocol", "line_number": 214, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 215, "usage_type": "call"}, {"api_name": "trinity.protocol.bcc.peer.BCCPeer", "line_number": 215, "usage_type": "argument"}, {"api_name": "trinity.protocol.bcc.commands.NewBeaconBlock", "line_number": 217, "usage_type": "argument"}, {"api_name": "typing.cast", "line_number": 218, "usage_type": "call"}, {"api_name": "trinity.protocol.bcc.commands.NewBeaconBlockMessage", "line_number": 218, "usage_type": "argument"}, {"api_name": "trinity.protocol.bcc.commands.BeaconBlocks", "line_number": 219, "usage_type": "argument"}, {"api_name": "typing.cast", "line_number": 220, "usage_type": "call"}, {"api_name": "trinity.protocol.bcc.commands.BeaconBlocksMessage", "line_number": 220, "usage_type": "argument"}, {"api_name": "trinity.protocol.bcc.peer.BCCPeer", "line_number": 224, "usage_type": "name"}, {"api_name": "trinity.protocol.bcc.commands.BeaconBlocksMessage", "line_number": 224, "usage_type": "name"}, {"api_name": "ssz.decode", "line_number": 235, "usage_type": "call"}, {"api_name": "eth2.beacon.types.blocks.BeaconBlock", "line_number": 235, "usage_type": "argument"}, {"api_name": "trinity.protocol.bcc.peer.BCCPeer", "line_number": 245, "usage_type": "name"}, {"api_name": "trinity.protocol.bcc.commands.NewBeaconBlockMessage", "line_number": 245, "usage_type": "name"}, {"api_name": "ssz.decode", "line_number": 249, "usage_type": "call"}, {"api_name": "eth2.beacon.types.blocks.BeaconBlock", "line_number": 249, "usage_type": "argument"}, {"api_name": "eth2.beacon.types.blocks.BeaconBlock", "line_number": 257, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 258, "usage_type": "name"}, {"api_name": "eth2.beacon.types.blocks.BeaconBlock", "line_number": 258, "usage_type": "name"}, {"api_name": "eth_typing.Hash32", "line_number": 283, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 285, "usage_type": "call"}, {"api_name": "trinity.protocol.bcc.peer.BCCPeer", "line_number": 285, "usage_type": "argument"}, {"api_name": "trinity._utils.les.gen_request_id", "line_number": 286, "usage_type": "call"}, {"api_name": "trinity._utils.shellart.bold_red", "line_number": 288, "usage_type": "call"}, {"api_name": "eth_typing.Hash32", "line_number": 297, "usage_type": "name"}, {"api_name": "eth.exceptions.BlockNotFound", "line_number": 301, "usage_type": "name"}, {"api_name": "eth_typing.Hash32", "line_number": 304, "usage_type": "name"}, {"api_name": "eth.exceptions.BlockNotFound", "line_number": 308, "usage_type": "name"}, {"api_name": "eth_typing.Hash32", "line_number": 311, "usage_type": "name"}, {"api_name": "eth2.beacon.types.blocks.BaseBeaconBlock", "line_number": 316, "usage_type": "name"}]} +{"seq_id": "185873798", "text": "# Synapse\n#\n# The synapse contains axons, dendrites, and a synaptic cleft.\n\nfrom molecule import Enzymes, Molecule_IDs\nfrom axon import Axon\nfrom dendrite import Dendrite\nfrom synaptic_cleft import SynapticCleft\n\nclass Synapse:\n def __init__(self, postsynaptic_id=None, initial_enzyme_concentration=0.0,\n active_molecules=[Molecule_IDs.GLUTAMATE], verbose=False):\n \"\"\"\n Creates a synapse with an initialized synaptic cleft.\n An initial enzyme concentration can be specified.\n\n If a single molecule is provided, we assume that it is the only\n molecule that is passed through this synapse. It is passed into\n the synaptic cleft constructor, which will set itself up to save\n time and space by only checking for that molecule.\n \"\"\"\n self.postsynaptic_id = postsynaptic_id\n self.axon = None\n self.dendrites = []\n self.probe = None\n\n self.synaptic_cleft = SynapticCleft(\n enzyme_concentration=initial_enzyme_concentration,\n active_molecules = active_molecules,\n verbose=verbose)\n\n def step(self, voltage):\n return self.axon.step(voltage)\n\n def activate_dendrites(self, neuron):\n for dendrite in self.dendrites:\n dendrite.activate(neuron)\n\n def set_probe(self, probe):\n self.probe = probe\n\n def record(self, time):\n if self.probe:\n data = [\n self.axon.get_concentration(),\n self.synaptic_cleft.get_total_concentration()\n ] + [dendrite.get_bound() for dendrite in self.dendrites]\n self.probe.record(tuple(data), time)\n\n def set_enzyme_concentration(self, e_c, enzymes=range(Enzymes.size)):\n \"\"\"\n Sets the concentration of the given |enzymes| in the synaptic cleft.\n \"\"\"\n for i in enzymes: self.synaptic_cleft.enzymes[i] = e_c\n\n def create_axon(self, **args):\n \"\"\"\n Creates an axon and adds it to the synapse.\n \"\"\"\n if self.axon is not None:\n raise ValueError(\"Cannot have two axons on one synapse!\")\n axon = Axon(self.synaptic_cleft, **args)\n self.synaptic_cleft.axon = axon\n self.axon = axon\n return axon\n\n def create_dendrite(self, **args):\n \"\"\"\n Creates a dendrite and adds it to the synapse.\n \"\"\"\n dendrite = Dendrite(**args)\n self.synaptic_cleft.dendrites.append(dendrite)\n self.dendrites.append(dendrite)\n return dendrite\n", "sub_path": "new/synapse.py", "file_name": "synapse.py", "file_ext": "py", "file_size_in_byte": 2547, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "molecule.Molecule_IDs.GLUTAMATE", "line_number": 12, "usage_type": "attribute"}, {"api_name": "molecule.Molecule_IDs", "line_number": 12, "usage_type": "name"}, {"api_name": "synaptic_cleft.SynapticCleft", "line_number": 27, "usage_type": "call"}, {"api_name": "dendrite.activate", "line_number": 37, "usage_type": "call"}, {"api_name": "dendrite.get_bound", "line_number": 47, "usage_type": "call"}, {"api_name": "molecule.Enzymes.size", "line_number": 50, "usage_type": "attribute"}, {"api_name": "molecule.Enzymes", "line_number": 50, "usage_type": "name"}, {"api_name": "axon.Axon", "line_number": 62, "usage_type": "call"}, {"api_name": "dendrite.Dendrite", "line_number": 71, "usage_type": "call"}]} +{"seq_id": "278947196", "text": "import requests\nimport os\nimport json\n\ndef get_random_quote():\n\turl = os.environ.get('SPNKRDEMO_BACKEND_URL', None) \n\tif url is None:\n\t\treturn {'quote': 'Unable to locate backend url'}\n\telse:\n\t\tquote = requests.get(url).json()\n\t\treturn quote \n\n\n", "sub_path": "backend_proxy.py", "file_name": "backend_proxy.py", "file_ext": "py", "file_size_in_byte": 245, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "os.environ.get", "line_number": 6, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 6, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "253556117", "text": "import os\nimport gym\nimport pybullet_envs # register PyBullet enviroments with open ai gym\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom agent import Agent\nfrom gym import wrappers\n\n\ndef plot_scores(scores, n_episodes_to_consider, figure_file):\n avg_scores = []\n x = []\n for t in range(len(scores)):\n if t < n_episodes_to_consider:\n avg_scores.append(np.mean(scores[0: t + 1]))\n else:\n avg_scores.append(np.mean(scores[t - n_episodes_to_consider: t]))\n x.append(t)\n plt.plot(x, avg_scores)\n plt.title('Average of the previous %d scores' %(n_episodes_to_consider))\n plt.savefig(figure_file)\n\n\n# seed = (0)\n# env.seed(0)\n# np.random.seed(0)\n# random.seed(0)\n# torch.manual_seed(0)\n\n#env_name = 'LunarLanderContinuous-v2'\n# env_name = 'BipedalWalker-v3'\n# env_name = 'HalfCheetahBulletEnv-v0'\n# env_name = 'HumanoidBulletEnv-v0'\n# env_name = 'HopperBulletEnv-v0'\nenv_name = 'Walker2DBulletEnv-v0'\nenv = gym.make(env_name)\n\nn_games = 10000\nn_episodes_to_consider = 50\n\nload_checkpoint = True\n\nn_states = env.observation_space.shape[0]\nn_actions = env.action_space.shape[0]\nupdate_actor_interval = 2\nwarmup = 1000\nmem_size = 10**6\nbatch_size = 64\nn_hid1 = 400\nn_hid2 = 300\nlr_alpha = 1e-3\nlr_beta = 1e-3\ngamma = 0.99\ntau = 0.005\nnoise_mean = 0\nnoise_sigma = 0.1\n\nfname = 'ngames' + str(n_games) + '_memsize' + str(mem_size) + '_batchsize' + str(batch_size) + '_nhid1' + str(n_hid1)\\\n + '_nhid2' + str(n_hid2) + '_lralpha' + str(lr_alpha) + '_lrbeta' + str(lr_beta) + '_gamma' + str(gamma) +\\\n '_tau' + str(tau)\n\n\nfigure_file = env_name + '/plots/' + fname + '.png'\ncheckpoint_file = env_name + '/models/' + fname\nagent = Agent(load_checkpoint, checkpoint_file, env, n_states, n_actions, update_actor_interval, warmup, mem_size, batch_size,\n n_hid1, n_hid2, lr_alpha, lr_beta, gamma, tau, noise_mean, noise_sigma)\nif load_checkpoint:\n agent.load_models()\nif __name__=='__main__':\n if not os.path.exists(env_name):\n os.mkdir(env_name)\n paths = ['plots', 'videos', 'models']\n for path_name in paths:\n path = os.path.join(env_name, path_name)\n if not os.path.exists(path):\n os.mkdir(path)\n\n if load_checkpoint:\n env = wrappers.Monitor(env, env_name + '/videos', video_callable=lambda episode_id: True,\n force=True) #  force overwrites previous video\n figure_file = env_name + '/plots/' + fname + '_eval' + '.png'\n agent.actor.eval()\n n_games = 10\n n_episodes_to_consider = 5\n\n assert n_games >= n_episodes_to_consider\n scores = []\n best_score = env.reward_range[0] # 0 is the lowest reward\n timesteps_count = 0\n for i in range(n_games):\n obs = env.reset()\n done = False\n score = 0\n while not done:\n timesteps_count += 1\n if load_checkpoint:\n action = agent.choose_action_eval(obs)\n else:\n action = agent.choose_action(obs)\n obs_, reward, done, info = env.step(action)\n if not load_checkpoint:\n agent.store_transition(obs, action, reward, obs_, done)\n agent.learn()\n obs = obs_\n score += reward\n scores.append(score)\n\n avg_score = np.mean(scores[-n_episodes_to_consider:])\n if score > best_score and not load_checkpoint:\n best_score = score\n agent.save_models()\n # if i > 0 and i % n_to_consider == 0:\n if not load_checkpoint:\n if i % 100 == 0:\n print('Epoch %d, %d timesteps, score %.3f - best score %.3f -- %d games avg: %.3f' % (i, timesteps_count, score, best_score, n_episodes_to_consider, avg_score))\n if i > 0 and i % 200 == 0:\n plot_scores(scores, n_episodes_to_consider, figure_file)\n else:\n print('Epoch %d, %d timesteps, score %.3f - best score %.3f -- %d games avg: %.3f' % (\n i, timesteps_count, score, best_score, n_episodes_to_consider, avg_score))\n\n plot_scores(scores, n_episodes_to_consider, figure_file)\n\n\n\n", "sub_path": "37-48_twin_delayed_deep_deterministic_policy_gradient/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4151, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.mean", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "gym.make", "line_number": 36, "usage_type": "call"}, {"api_name": "agent.Agent", "line_number": 65, "usage_type": "call"}, {"api_name": "agent.load_models", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 76, "usage_type": "call"}, {"api_name": "gym.wrappers.Monitor", "line_number": 79, "usage_type": "call"}, {"api_name": "gym.wrappers", "line_number": 79, "usage_type": "name"}, {"api_name": "agent.actor.eval", "line_number": 82, "usage_type": "call"}, {"api_name": "agent.actor", "line_number": 82, "usage_type": "attribute"}, {"api_name": "agent.choose_action_eval", "line_number": 97, "usage_type": "call"}, {"api_name": "agent.choose_action", "line_number": 99, "usage_type": "call"}, {"api_name": "agent.store_transition", "line_number": 102, "usage_type": "call"}, {"api_name": "agent.learn", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 108, "usage_type": "call"}, {"api_name": "agent.save_models", "line_number": 111, "usage_type": "call"}]} +{"seq_id": "355173971", "text": "from patent_city.formater import flatten, unique\nfrom spacy.tokens.doc import Doc\nfrom spacy.tokens.span import Span\nfrom wasabi import Printer\n\nmsg = Printer()\n\n\ndef matcher2dict(doc, matcher, nlp, parse_meth: str = \"full\"):\n \"\"\"\n Return a dict where the keys are the rules of the matcher (e.g: STATE, COUNTY) and the values\n are lists of matches (text)\n :param doc: spacy.tokens.doc.Doc\n :param matcher: spacy.matcher.matcher.Matcher\n :param nlp: spacy.lang.en.English\n :param parse_meth: str to be taken in ['full', 'following', 'inbetween', 'pos']\n :return: dict. Ex: {'STATE':[], 'COUNTY':[], 'CITY':[]}\n \"\"\"\n\n assert isinstance(doc, (Doc, Span))\n assert parse_meth in [\"full\", \"following\", \"inbetween\", \"pos\", \"before\"]\n\n matches = matcher(doc)\n out = {}\n for pattern_id in matcher._patterns.keys():\n if parse_meth == \"following\":\n matches_ = [\n list(doc[end:].noun_chunks)[0].text\n for match_id, start, end in matches\n if (match_id == pattern_id and list(doc[end:].noun_chunks))\n ]\n elif parse_meth == \"full\":\n matches_ = [\n doc[start:end].text\n for match_id, start, end in matches\n if match_id == pattern_id\n ]\n elif parse_meth == \"inbetween\":\n matches_ = [\n doc[start + 1 : end - 1].text\n for match_id, start, end in matches\n if match_id == pattern_id\n ]\n elif parse_meth == \"before\":\n matches_ = [\n doc[start: end - 3].text\n for match_id, start, end in matches\n if match_id == pattern_id\n ]\n elif parse_meth == \"pos\":\n matches_ = [\n [t.text for t in doc[start:end] if t.pos_ in [\"NOUN\", \"PROPN\"]]\n for match_id, start, end in matches\n ]\n matches_ += [\n [nc.text for nc in doc[start:end].noun_chunks]\n for match_id, start, end in matches\n ]\n matches_ = flatten(matches_)\n out.update({nlp.vocab.strings[pattern_id]: matches_})\n return out\n\n\ndef phrasematcher2dict(doc, phrasematcher, nlp):\n \"\"\"\n Return a dict where the keys are the rules of the phrasematcher (e.g: STATE, COUNTY) and the\n values are lists of matches (text)\n :param doc: spacy.tokens.doc.Doc\n :param phrasematcher: spacy.matcher.matcher.PhraseMatcher\n :param nlp: spacy.lang.en.English\n :param type: str to be taken in ['full', 'last']\n :return: dict. Ex: {'STATE':[], 'COUNTY':[], 'CITY':[]}\n \"\"\"\n\n assert isinstance(doc, (Doc, Span))\n\n matches = phrasematcher(doc)\n out = {}\n for pattern_id in phrasematcher._docs.keys():\n matches_ = [\n doc[start:end].text\n for match_id, start, end in matches\n if match_id == pattern_id\n ]\n out.update({nlp.vocab.strings[pattern_id]: matches_})\n return out\n", "sub_path": "patent_city/io.py", "file_name": "io.py", "file_ext": "py", "file_size_in_byte": 3034, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "wasabi.Printer", "line_number": 6, "usage_type": "call"}, {"api_name": "spacy.tokens.doc.Doc", "line_number": 20, "usage_type": "name"}, {"api_name": "spacy.tokens.span.Span", "line_number": 20, "usage_type": "name"}, {"api_name": "patent_city.formater.flatten", "line_number": 59, "usage_type": "call"}, {"api_name": "spacy.tokens.doc.Doc", "line_number": 75, "usage_type": "name"}, {"api_name": "spacy.tokens.span.Span", "line_number": 75, "usage_type": "name"}]} +{"seq_id": "196903014", "text": "# #! /usr/bin/env python\r\n\r\n# imports of external packages to use in our code\r\nimport sys\r\nimport numpy as np\r\nimport re\r\nimport json\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib as mpl\r\nfrom sklearn import mixture\r\nimport itertools\r\nfrom scipy import linalg\r\n\r\n# main function for our coin toss Python code\r\nif __name__ == \"__main__\":\r\n\t# if no args passed (need at least the input file), dump the help message\r\n\t# if the user includes the flag -h or --help print the options\r\n\tif '-h' in sys.argv or '--help' in sys.argv or len(sys.argv) == 1:\r\n\t\tprint (\"Usage: %s [-input0 string] [-input1 string] [-Ndice int]\" % sys.argv[0])\r\n\t\tprint (\"-input: (mandatory) the name of the file which holds the sample data\")\r\n\t\tprint (\"-K:\t \t\t (optional) the number of clusters to attempt to find in the data, default 2. must be >= 1\")\r\n\t\tprint\r\n\t\tsys.exit(1)\r\n\r\n\r\n\t# number of clusters to try and find\r\n\tK = 2\r\n\r\n\tmaxiter = 100 # maximum number of iterations to do before quitting\r\n\r\n\t# read the user-provided arguments from the command line (if there)\r\n\tif '-input' in sys.argv:\r\n\t\tp = sys.argv.index('-input')\r\n\t\ttry:\r\n\t\t\tinput0 = sys.argv[p+1]\r\n\t\texcept IndexError as e:\r\n\t\t\tprint(\"Must pass input filename\")\r\n\telse:\r\n\t\tprint(\"Must pass input filename\")\r\n\tif '-K' in sys.argv:\r\n\t\tp = sys.argv.index('-K')\r\n\t\tK = int(sys.argv[p+1])\r\n\t\tif K < 1:\r\n\t\t\tprint(\"K must be >= 1\")\r\n\t\t\tsys.exit(1)\r\n\r\n\t# load json data from file\r\n\tres0 = \"\"\r\n\twith open(input0) as f:\r\n\t\tres0 = json.load(f)\r\n\t\tf.close()\r\n\ttrue_mus = np.array(res0[\"meth\"])\r\n\r\n\tx = []\r\n\ty = []\r\n\r\n\tgmm_data = []\r\n\r\n\tfor i in range(len(res0[\"x\"])):\r\n\t\tif np.isnan(res0[\"x\"][i]) or np.isnan(res0[\"y\"][i]):\r\n\t\t\tpass\r\n\t\telse:\r\n\t\t\tx.append(res0[\"x\"][i])\r\n\t\t\ty.append(res0[\"y\"][i])\r\n\t\t\tgmm_data.append(np.array([res0[\"x\"][i], res0[\"y\"][i]]))\r\n\r\n\tx = np.array(x)#np.log10(np.array(x))#\r\n\ty = np.array(y)#np.log10(np.array(y))#\r\n\r\n\tgmm_data = np.array(gmm_data)\r\n\r\n\t# get the number of points\r\n\tnum_points = len(x)\r\n\r\n\tprint(\"Running k-means clustering\")\r\n\t# keep track of how many iterations we're doing\r\n\titeration = 0\r\n\r\n\t# need initial domain of data\r\n\tdmn = [[np.min(x), np.max(x)],[np.min(y), np.max(y)]]\r\n\t#print(dmn)\r\n\r\n\t# get K initial uniformly random guesses\r\n\tmeans = np.random.uniform([dmn[0][0], dmn[1][0]], [dmn[0][1], dmn[1][1]], (K,2))\r\n\r\n\t#print(means)\r\n\r\n\t# keep track of last guess to compare to, something outside of domain at first\r\n\tlast = np.random.uniform([dmn[0][1], dmn[1][1]], [dmn[0][1] + 1, dmn[1][1] + 1], (K,2))\r\n\r\n\t# iteratively do K-means\r\n\twhile iteration < maxiter:\r\n\t\tdists = []\r\n\r\n\t\t# find distances of all points to all guessed means\r\n\t\tfor k in range(K):\r\n\t\t\tdist = np.sqrt((x - means[k][0])**2 + (y - means[k][1])**2)\r\n\t\t\tdists.append(np.array(dist))\r\n\t\tdists = np.array(dists)\r\n\r\n\r\n\t\t# keep track of the clusters \r\n\t\tclusters = [ [[], []] for _ in range(K)] # K empty arrays to hold clusters\r\n\r\n\t\t# associate each point with its closest mean\r\n\t\tfor i in range(num_points):\r\n\t\t\tind = np.argmin(dists[:,i])\r\n\t\t\tclusters[ind][0].append(x[i])\r\n\t\t\tclusters[ind][1].append(y[i])\r\n\r\n\r\n\t\t# if any of the clusters are empty, quit. this happens sometimes when there are \"too many\" means for the number of points, or one guess is really bad and is far away\r\n\t\t# not sure how to handle this, so just die\r\n\t\tfor k in range(K):\r\n\t\t\tif len(clusters[k][0]) == 0:\r\n\t\t\t\tprint(\"empty cluster\")\r\n\t\t\t\tsys.exit(1)\r\n\r\n\t\t# find centroids of clusters to see where to step to next\r\n\t\tcentroids = []\r\n\t\tfor k in range(K):\r\n\t\t\tcx = np.mean(clusters[k][0])\r\n\t\t\tcy = np.mean(clusters[k][1])\r\n\t\t\tcentroids.append(np.array([cx, cy]))\r\n\t\tcentroids = np.array(centroids)\r\n\r\n\t\t# check if new centroids are in the same place as the old means\r\n\t\tcdists = []\r\n\t\tfor k in range(K):\r\n\t\t\tcdists.append(np.sqrt((centroids[k][0] - means[k][0])**2 + (centroids[k][1] - means[k][1])**2))\r\n\r\n\t\t# if the new cluster centroids and the old means are the same, stop iterating because we're done\r\n\t\tif np.array_equal(cdists, np.zeros(K)):\r\n\t\t\tbreak;\r\n\r\n\t\t# swap centers around for next iteration\r\n\t\tlast = means\r\n\t\tmeans = centroids\r\n\r\n\t\t# increment iteration\r\n\t\titeration = iteration + 1\r\n\r\n\t# print the final centers:\r\n\tprint(\"The final k-means cluster centers are:\")\r\n\tprint(means)\r\n\r\n\t# make dual plot to handle both final results\r\n\tfig, ax = plt.subplots(1, 2, sharey=False, figsize=(12, 5))\r\n\r\n\t# plot k-means side\r\n\tfor k in range(K):\r\n\t\tax[0].plot(clusters[k][0], clusters[k][1], \".\", markersize=2, c=\"C{}\".format(k))\r\n\t\tax[0].plot(means[k][0], means[k][1], \"*\", markersize=10, mec=\"k\", c=\"C{}\".format(k))\r\n\r\n\tax[0].set_xlabel(\"log10 Period (days)\")\r\n\tax[0].set_ylabel(r\"log10 Mass ($M_{Jup}$)\")\r\n\t#ax[0].set_ylabel(r\"log10 Radius ($R_{Jup}$)\")\r\n\tax[0].set_title(\"Exoplanet K-means Results\")\r\n\r\n\t# run gaussian mixture modeling, following the sklearn example: https://scikit-learn.org/stable/auto_examples/mixture/plot_gmm.html#sphx-glr-auto-examples-mixture-plot-gmm-py\r\n\t# in this case, just do normal expectation maximization\r\n\tcolor_iter = itertools.cycle(['C0', 'C1', 'C2', 'C3', 'C4', 'C5'])\r\n\tdef plot_results(X, Y_, means, covariances, index, title):\r\n\t\t# loop through each cluster\r\n\t\tprint(\"The Gaussian Mixture clusters are:\")\r\n\t\tfor i, (mean, covar, color) in enumerate(zip(means, covariances, color_iter)):\r\n\t\t\t# get semiaxes and rotation angle from fit gaussian ellipsoids\r\n\t\t\tv, w = linalg.eigh(covar)\r\n\t\t\tv = 2. * np.sqrt(2.) * np.sqrt(v)\r\n\t\t\tu = w[0] / linalg.norm(w[0])\r\n\r\n\t\t\t# as the DP will not use every component it has access to\r\n\t\t\t# unless it needs it, we shouldn't plot the redundant\r\n\t\t\t# components.\r\n\t\t\tif not np.any(Y_ == i):\r\n\t\t\t\tcontinue\r\n\t\t\tax[1].scatter(X[Y_ == i, 0], X[Y_ == i, 1], .8, color=color)\r\n\r\n\t\t\t# Plot an ellipse to show the Gaussian component\r\n\t\t\tangle = np.arctan(u[1] / u[0])\r\n\t\t\tangle = 180. * angle / np.pi # convert to degrees\r\n\t\t\tell = mpl.patches.Ellipse(mean, v[0], v[1], 180. + angle, color=color)\r\n\t\t\tax[1].plot(mean[0], mean[1], \"*\", markersize=10, mec=\"k\", c=color)\r\n\t\t\tprint(i)\r\n\t\t\tprint([mean[0], mean[1]])\r\n\t\t\tprint([v[0], v[1]])\r\n\t\t\tprint(angle)\r\n\r\n\t\t\tell.set_alpha(0.5)\r\n\t\t\tax[1].add_artist(ell)\r\n\r\n\t\tax[1].set_xlabel(\"log10 Period (days)\")\r\n\t\t#ax[1].set_ylabel(r\"log10 Radius ($R_{Jup}$)\")\r\n\t\tax[1].set_ylabel(\"log10 Mass (Mjup)\")\r\n\t\tax[1].set_title(title)\r\n\r\n\r\n\r\n\tprint(\"Running Gaussian Mixture Modeling - Expectation Maximization \")\r\n\tgmm = mixture.GaussianMixture(n_components=K, covariance_type=\"full\").fit(gmm_data)\r\n\r\n\tplot_results(gmm_data, gmm.predict(gmm_data), gmm.means_, gmm.covariances_, 0, 'Exoplanet Gaussian Mixture')\r\n\tfig.savefig(\"plots/mp_dual_plot.jpg\", dpi=200)\r\n\t#fig.savefig(\"plots/rp_dual_plot.jpg\", dpi=200)\r\n\tplt.show()\r\n\tplt.close()\r\n", "sub_path": "python/Cluster.py", "file_name": "Cluster.py", "file_ext": "py", "file_size_in_byte": 6676, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "sys.argv", "line_number": 18, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 19, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 23, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 32, "usage_type": "attribute"}, {"api_name": "sys.argv.index", "line_number": 33, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 33, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 35, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 40, "usage_type": "attribute"}, {"api_name": "sys.argv.index", "line_number": 41, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 41, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 42, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 45, "usage_type": "call"}, {"api_name": "json.load", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 84, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 89, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 107, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.array_equal", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 148, "usage_type": "name"}, {"api_name": "itertools.cycle", "line_number": 162, "usage_type": "call"}, {"api_name": "scipy.linalg.eigh", "line_number": 168, "usage_type": "call"}, {"api_name": "scipy.linalg", "line_number": 168, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 169, "usage_type": "call"}, {"api_name": "scipy.linalg.norm", "line_number": 170, "usage_type": "call"}, {"api_name": "scipy.linalg", "line_number": 170, "usage_type": "name"}, {"api_name": "numpy.any", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.arctan", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 181, "usage_type": "attribute"}, {"api_name": "matplotlib.patches.Ellipse", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 182, "usage_type": "attribute"}, {"api_name": "sklearn.mixture.GaussianMixture", "line_number": 200, "usage_type": "call"}, {"api_name": "sklearn.mixture", "line_number": 200, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 205, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 205, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 206, "usage_type": "name"}]} +{"seq_id": "458241615", "text": "import os\n\nimport pymysql\nimport sqlalchemy\nimport sqlalchemy.orm\nimport sqlalchemy.ext.declarative\nimport sqlalchemy.ext.mutable\nimport flask_jsontools\nimport sqlalchemy_jsonfield\n\nclass MySQL(object):\n \"\"\"\n Main class for interacting with Nandy in MySQL\n \"\"\"\n\n def __init__(self, host=None, port=None):\n\n self.engine = sqlalchemy.create_engine(f\"mysql+pymysql://root@{host or os.environ['MYSQL_HOST']}:{port or os.environ['MYSQL_PORT']}/nandy\")\n Session = sqlalchemy.orm.sessionmaker(bind=self.engine)\n self.session = Session()\n\n\nBase = sqlalchemy.ext.declarative.declarative_base(cls=(flask_jsontools.JsonSerializableBase))\n\n\nclass Person(Base):\n\n __tablename__ = \"person\"\n \n person_id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True, autoincrement=True)\n name = sqlalchemy.Column(sqlalchemy.String(64), nullable=False)\n email = sqlalchemy.Column(sqlalchemy.String(128), nullable=False)\n\n sqlalchemy.schema.UniqueConstraint('name', name='label')\n sqlalchemy.schema.UniqueConstraint('email', name='email')\n\n def __repr__(self):\n return \"\" % (self.name)\n\n\nclass Area(Base):\n\n __tablename__ = \"area\"\n \n area_id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True, autoincrement=True)\n\n name = sqlalchemy.Column(sqlalchemy.String(64), nullable=False)\n status = sqlalchemy.Column(sqlalchemy.String(32), nullable=False)\n updated = sqlalchemy.Column(sqlalchemy.Integer)\n data = sqlalchemy.Column(\n sqlalchemy.ext.mutable.MutableDict.as_mutable(\n sqlalchemy_jsonfield.JSONField(enforce_string=True,enforce_unicode=False)\n ), \n nullable=False\n )\n\n sqlalchemy.schema.UniqueConstraint('name', name='label')\n\n def __repr__(self):\n return \"\" % (self.name)\n\n\nclass Template(Base):\n\n __tablename__ = \"template\"\n \n template_id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True, autoincrement=True)\n name = sqlalchemy.Column(sqlalchemy.String(128), nullable=False)\n kind = sqlalchemy.Column(sqlalchemy.Enum(\"chore\", \"act\"))\n data = sqlalchemy.Column(\n sqlalchemy.ext.mutable.MutableDict.as_mutable(\n sqlalchemy_jsonfield.JSONField(enforce_string=True,enforce_unicode=False)\n ), \n nullable=False\n )\n\n sqlalchemy.schema.UniqueConstraint('name', 'kind', name='label')\n\n def __repr__(self):\n return \"\" % (self.name, self.kind)\n\n\nclass Chore(Base):\n\n __tablename__ = \"chore\"\n \n chore_id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True, autoincrement=True)\n person_id = sqlalchemy.Column(sqlalchemy.Integer, sqlalchemy.ForeignKey(\"person.person_id\"), nullable=False)\n name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)\n status = sqlalchemy.Column(sqlalchemy.Enum(\"started\", \"ended\"))\n created = sqlalchemy.Column(sqlalchemy.Integer)\n updated = sqlalchemy.Column(sqlalchemy.Integer)\n data = sqlalchemy.Column(\n sqlalchemy.ext.mutable.MutableDict.as_mutable(\n sqlalchemy_jsonfield.JSONField(enforce_string=True,enforce_unicode=False)\n ), \n nullable=False\n )\n\n person = sqlalchemy.orm.relationship(\"Person\") \n\n sqlalchemy.schema.UniqueConstraint('name', 'person_id', 'created', name='label')\n\n def __repr__(self):\n return \"\" % (self.name, self.person.name, self.created)\n\n\nclass Act(Base):\n\n __tablename__ = \"act\"\n \n act_id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True, autoincrement=True)\n person_id = sqlalchemy.Column(sqlalchemy.Integer, sqlalchemy.ForeignKey(\"person.person_id\"), nullable=False)\n name = sqlalchemy.Column(sqlalchemy.String(128), nullable=False)\n value = sqlalchemy.Column(sqlalchemy.Enum(\"positive\", \"negative\"))\n created = sqlalchemy.Column(sqlalchemy.Integer)\n data = sqlalchemy.Column(\n sqlalchemy.ext.mutable.MutableDict.as_mutable(\n sqlalchemy_jsonfield.JSONField(enforce_string=True,enforce_unicode=False)\n ), \n nullable=False\n )\n\n person = sqlalchemy.orm.relationship(\"Person\") \n\n sqlalchemy.schema.UniqueConstraint('name', 'person_id', 'created', name='label')\n\n def __repr__(self):\n return \"\" % (self.name, self.person.name, self.created)\n\n\ndef create_database():\n\n connection = pymysql.connect(host='mysql', user='root')\n\n try:\n\n with connection.cursor() as cursor:\n cursor.execute(\"DROP DATABASE IF EXISTS nandy\")\n cursor.execute(\"CREATE DATABASE nandy\")\n\n connection.commit()\n\n finally:\n\n connection.close()\n\n\nclass Sample:\n\n def __init__(self, session):\n\n self.session = session\n\n def person(self, name, email=None):\n\n if email is None:\n email = name\n\n person = Person(name=name, email=email)\n self.session.add(person)\n self.session.commit()\n\n return person\n\n def area(self, name, status=None, updated=7, data=None):\n\n if status is None:\n status = name\n\n if data is None:\n data = {}\n\n area = Area(name=name, status=status, updated=updated, data=data)\n self.session.add(area)\n self.session.commit()\n\n return area\n\n def template(self, name, kind, data=None):\n\n if data is None:\n data = {}\n\n template = Template(name=name, kind=kind, data=data)\n self.session.add(template)\n self.session.commit()\n\n return template\n\n def chore(self, person, name=\"Unit\", status=\"started\", created=7, updated=8, data=None, tasks=None):\n\n if data is None:\n data = {}\n\n base = {\n \"text\": \"chore it\",\n \"language\": \"en-us\"\n }\n\n base.update(data)\n\n if tasks is not None:\n base[\"tasks\"] = tasks\n\n chore = Chore(\n person_id=self.person(person).person_id,\n name=name,\n status=status,\n created=created,\n updated=updated,\n data=base\n )\n\n self.session.add(chore)\n self.session.commit()\n\n return chore\n\n def act(self, person, name=\"Unit\", value=\"positive\", created=7, data=None):\n\n if data is None:\n data = {}\n\n act = Act(\n person_id=self.person(person).person_id,\n name=name,\n value=value,\n created=created,\n data=data\n )\n self.session.add(act)\n self.session.commit()\n\n return act\n", "sub_path": "lib/nandy/store/mysql.py", "file_name": "mysql.py", "file_ext": "py", "file_size_in_byte": 6635, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "sqlalchemy.create_engine", "line_number": 18, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 18, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 19, "usage_type": "call"}, {"api_name": "sqlalchemy.orm", "line_number": 19, "usage_type": "attribute"}, {"api_name": "sqlalchemy.ext.declarative.declarative_base", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.ext", "line_number": 23, "usage_type": "attribute"}, {"api_name": "flask_jsontools.JsonSerializableBase", "line_number": 23, "usage_type": "attribute"}, {"api_name": "sqlalchemy.Column", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 30, "usage_type": "attribute"}, {"api_name": "sqlalchemy.Column", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 32, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 32, "usage_type": "call"}, {"api_name": "sqlalchemy.schema.UniqueConstraint", "line_number": 34, "usage_type": "call"}, {"api_name": "sqlalchemy.schema", "line_number": 34, "usage_type": "attribute"}, {"api_name": "sqlalchemy.schema.UniqueConstraint", "line_number": 35, "usage_type": "call"}, {"api_name": "sqlalchemy.schema", "line_number": 35, "usage_type": "attribute"}, {"api_name": "sqlalchemy.Column", "line_number": 45, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 45, "usage_type": "attribute"}, {"api_name": "sqlalchemy.Column", "line_number": 47, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 47, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 48, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 48, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 49, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 49, "usage_type": "attribute"}, {"api_name": "sqlalchemy.Column", "line_number": 50, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.mutable.MutableDict.as_mutable", "line_number": 51, "usage_type": "call"}, {"api_name": "sqlalchemy.ext", "line_number": 51, "usage_type": "attribute"}, {"api_name": "sqlalchemy_jsonfield.JSONField", "line_number": 52, "usage_type": "call"}, {"api_name": "sqlalchemy.schema.UniqueConstraint", "line_number": 57, "usage_type": "call"}, {"api_name": "sqlalchemy.schema", "line_number": 57, "usage_type": "attribute"}, {"api_name": "sqlalchemy.Column", "line_number": 67, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 67, "usage_type": "attribute"}, {"api_name": "sqlalchemy.Column", "line_number": 68, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 68, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 69, "usage_type": "call"}, {"api_name": "sqlalchemy.Enum", "line_number": 69, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 70, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.mutable.MutableDict.as_mutable", "line_number": 71, "usage_type": "call"}, {"api_name": "sqlalchemy.ext", "line_number": 71, "usage_type": "attribute"}, {"api_name": "sqlalchemy_jsonfield.JSONField", "line_number": 72, "usage_type": "call"}, {"api_name": "sqlalchemy.schema.UniqueConstraint", "line_number": 77, "usage_type": "call"}, {"api_name": "sqlalchemy.schema", "line_number": 77, "usage_type": "attribute"}, {"api_name": "sqlalchemy.Column", "line_number": 87, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 87, "usage_type": "attribute"}, {"api_name": "sqlalchemy.Column", "line_number": 88, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 88, "usage_type": "attribute"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 88, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 89, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 89, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 90, "usage_type": "call"}, {"api_name": "sqlalchemy.Enum", "line_number": 90, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 91, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 91, "usage_type": "attribute"}, {"api_name": "sqlalchemy.Column", "line_number": 92, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 92, "usage_type": "attribute"}, {"api_name": "sqlalchemy.Column", "line_number": 93, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.mutable.MutableDict.as_mutable", "line_number": 94, "usage_type": "call"}, {"api_name": "sqlalchemy.ext", "line_number": 94, "usage_type": "attribute"}, {"api_name": "sqlalchemy_jsonfield.JSONField", "line_number": 95, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 100, "usage_type": "call"}, {"api_name": "sqlalchemy.orm", "line_number": 100, "usage_type": "attribute"}, {"api_name": "sqlalchemy.schema.UniqueConstraint", "line_number": 102, "usage_type": "call"}, {"api_name": "sqlalchemy.schema", "line_number": 102, "usage_type": "attribute"}, {"api_name": "sqlalchemy.Column", "line_number": 112, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 112, "usage_type": "attribute"}, {"api_name": "sqlalchemy.Column", "line_number": 113, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 113, "usage_type": "attribute"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 113, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 114, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 114, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 115, "usage_type": "call"}, {"api_name": "sqlalchemy.Enum", "line_number": 115, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 116, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 116, "usage_type": "attribute"}, {"api_name": "sqlalchemy.Column", "line_number": 117, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.mutable.MutableDict.as_mutable", "line_number": 118, "usage_type": "call"}, {"api_name": "sqlalchemy.ext", "line_number": 118, "usage_type": "attribute"}, {"api_name": "sqlalchemy_jsonfield.JSONField", "line_number": 119, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 124, "usage_type": "call"}, {"api_name": "sqlalchemy.orm", "line_number": 124, "usage_type": "attribute"}, {"api_name": "sqlalchemy.schema.UniqueConstraint", "line_number": 126, "usage_type": "call"}, {"api_name": "sqlalchemy.schema", "line_number": 126, "usage_type": "attribute"}, {"api_name": "pymysql.connect", "line_number": 134, "usage_type": "call"}]} +{"seq_id": "129038652", "text": "#!/usr/bin/python3\n# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import, division, print_function\n\nfrom sys import version_info\nif version_info.major == 3:\n pass\nelif version_info.major == 2:\n input = raw_input\nelse:\n print (\"Unknown python version - input function not safe\")\n\nfrom os import environ\nfrom collections import deque\n\n#from sys import setrecursionlimit\n#setrecursionlimit (11000)\n\n\"\"\"\nzztqooauhujtmxnsbzpykwlvpfyqijvdhuhiroodmuxiobyvwwxupqwydkpeebxmfvxhgicuzdealkgxlfmjiucasokrdznmtlwh\nexpected out:\ntqauhujtmxnsbzpykwlvpfyqijvdhuhirdmuxiobyvxupqwydkpbxmfvxhgicuzdealkgxlfmjiucasokrdznmtlwh\n\"\"\"\ndef index (c):\n return ord (c) - ord ('a')\n\ndef superReducedString_ (s):\n ln = len (s)\n emp = \"Empty String\"\n if ln < 2: return emp\n i = 0\n while i < ln - 1:\n print (i, s)\n c = s [i]\n cn = s [i + 1]\n if c == cn:\n s = s [ : i] + s [i + 2 : ]\n ln -= 2\n if i > 0: i -= 1\n else:\n i += 1\n return s if s else emp\n\ndef superReducedString__ (s1):\n # faster variant using deque\n s = deque (s1)\n ln = len (s)\n emp = \"Empty String\"\n if ln < 2: return emp\n res = deque ()\n while ln > 0:\n# print (ln, res, s)\n c = s [0]\n cn = s [1] if ln > 1 else '&'\n if c == cn:\n for i in range (2):\n ln -= 1\n if s: s.popleft ()\n elif res and res [-1] == c:\n res.pop ()\n s.popleft ()\n ln -= 1\n else:\n s.popleft ()\n res.append (c)\n ln -= 1\n r = \"\".join (res)\n return r if r else emp\n\ndef superReducedString (s):\n # even faster variant using list as stack\n ln = len (s)\n emp = \"Empty String\"\n if ln < 2: return emp\n res = []\n for i in range (ln):\n# print (res, i, s [i], end = \", \")\n if not res or s [i] != res [-1]:\n res.append (s [i])\n else: res.pop ()\n r = \"\".join (res)\n return r if r else emp\n\ndef main ():\n fptr = open (environ ['OUTPUT_PATH'], 'w')\n s = input ()\n result = superReducedString (s)\n print (result)\n fptr.write (result + '\\n')\n fptr.close ()\n\nif __name__ == '__main__':\n main ()\n", "sub_path": "2017/hackerrank/superReducedString.py", "file_name": "superReducedString.py", "file_ext": "py", "file_size_in_byte": 2250, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "sys.version_info.major", "line_number": 6, "usage_type": "attribute"}, {"api_name": "sys.version_info", "line_number": 6, "usage_type": "name"}, {"api_name": "sys.version_info.major", "line_number": 8, "usage_type": "attribute"}, {"api_name": "sys.version_info", "line_number": 8, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 46, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 50, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 85, "usage_type": "name"}]} +{"seq_id": "160312908", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.shortcuts import render, get_object_or_404\nfrom .models import SystemInfo\nfrom django.core.paginator import Paginator\nfrom django.http import HttpResponseRedirect\nfrom .form import SystemInfoForm\nfrom oms.settings import OMS_MYSQL\nfrom tools.mysql import db_operate\nfrom account.models import OperationMessage\nfrom django.core.urlresolvers import reverse\n\n# Create your views here.\n\ndef system_list(request):\n\tuser = request.user\n\tall_systems_info = SystemInfo.objects.all()\n\tpaginator = Paginator(all_systems_info,10)\n\ttry:\n\t\tpage = int(request.GET.get('page',1))\n\texcept ValueError:\n\t\tpage = 1\n\t\n\ttry:\n\t\tall_systems_info = paginator.page(page)\n\texcept:\n\t\tall_systems_info = paginator.page(paginator.num_pages)\n\n\treturn render(request, 'system_info_list.html', {'all_systems_info':all_systems_info, 'page':page, 'paginator':paginator})\n\ndef system_info_manager(request,id=None):\n\tuser = request.user\n\tif id:\n\t\tsystem_list = get_object_or_404(SystemInfo, pk=id) \n\t\taction = 'edit'\n\t\tpage_name = 'Edit-system'\n\t\tdb = db_operate()\n\t\tsql = 'select system_id from SystemInfo where id=%s' % (id)\n\t\tret = db.mysql_command(OMS_MYSQL, sql)\n\telse:\n\t\tsystem_list = SystemInfo()\n\t\taction = 'add'\n\t\tpage_name = 'Add-system'\n\t\tret = []\n\n\tif request.method == 'GET':\n\t\tdelete = request.GET.get('delete')\n\t\tid = request.GET.get('id')\n\t\tif delete:\n\t\t\tOperationMessage.objects.create(operation_type='systems', operation_action='delete', operation_object=ret, remark='delete system info')\n\t\t\tsystem_list = get_object_or_404(SystemInfo, pk=id)\n\t\t\tsystem_list.delete()\n\t\t\treturn HttpResponseRedirect(reverse('system_list'))\n\n\tif request.method == 'POST':\n\t\tform = SystemInfoForm(request.POST, instance=system_list)\n\t\toperate = request.POST.get('operate')\n\t\tif form.is_valid():\n\t\t\tif action == 'add':\n\t\t\t\tform.save()\n\t\t\t\tret.append(form.cleaned_data['system_id'])\n\t\t\t\tOperationMessage.objects.create(operation_type='systems', operation_action='add', operation_object=ret, remark='add system info')\n\t\t\t\treturn HttpResponseRedirect(reverse('system_list'))\n\t\t\tif operate:\n\t\t\t\tif operate == 'update':\n\t\t\t\t\tform.save()\n\t\t\t\t\tOperationMessage.objects.create(operation_type='systems', operation_action='update', operation_object=ret, remark='update system info')\n\t\t\t\t\treturn HttpResponseRedirect(reverse('system_list'))\n\t\t\t\telse:\n\t\t\t\t\tpass\n\telse:\n\t\tform = SystemInfoForm(instance=system_list)\n\n\treturn render(request, 'system_info_manager.html', {\"form\": form, \"action\":action, })\n\n", "sub_path": "systems/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2530, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "models.SystemInfo.objects.all", "line_number": 18, "usage_type": "call"}, {"api_name": "models.SystemInfo.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "models.SystemInfo", "line_number": 18, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 19, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 30, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 35, "usage_type": "call"}, {"api_name": "models.SystemInfo", "line_number": 35, "usage_type": "argument"}, {"api_name": "tools.mysql.db_operate", "line_number": 38, "usage_type": "call"}, {"api_name": "oms.settings.OMS_MYSQL", "line_number": 40, "usage_type": "argument"}, {"api_name": "models.SystemInfo", "line_number": 42, "usage_type": "call"}, {"api_name": "account.models.OperationMessage.objects.create", "line_number": 51, "usage_type": "call"}, {"api_name": "account.models.OperationMessage.objects", "line_number": 51, "usage_type": "attribute"}, {"api_name": "account.models.OperationMessage", "line_number": 51, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 52, "usage_type": "call"}, {"api_name": "models.SystemInfo", "line_number": 52, "usage_type": "argument"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 54, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 54, "usage_type": "call"}, {"api_name": "form.SystemInfoForm", "line_number": 57, "usage_type": "call"}, {"api_name": "form.is_valid", "line_number": 59, "usage_type": "call"}, {"api_name": "form.save", "line_number": 61, "usage_type": "call"}, {"api_name": "form.cleaned_data", "line_number": 62, "usage_type": "attribute"}, {"api_name": "account.models.OperationMessage.objects.create", "line_number": 63, "usage_type": "call"}, {"api_name": "account.models.OperationMessage.objects", "line_number": 63, "usage_type": "attribute"}, {"api_name": "account.models.OperationMessage", "line_number": 63, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 64, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 64, "usage_type": "call"}, {"api_name": "form.save", "line_number": 67, "usage_type": "call"}, {"api_name": "account.models.OperationMessage.objects.create", "line_number": 68, "usage_type": "call"}, {"api_name": "account.models.OperationMessage.objects", "line_number": 68, "usage_type": "attribute"}, {"api_name": "account.models.OperationMessage", "line_number": 68, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 69, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 69, "usage_type": "call"}, {"api_name": "form.SystemInfoForm", "line_number": 73, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 75, "usage_type": "call"}]} +{"seq_id": "150165935", "text": "import sys\nimport logging\nfrom datetime import datetime\n\nfrom app.common.utils.app_logger import initialize_logging\nfrom app.data_warehouse.main import pipelines as dw_pipelines\nfrom app.staging.main import pipelines as stg_pipelines\n\n# python main.py \n# add credentials config ini files to system path using sys.path.append('')\n\nif __name__ == '__main__':\n args = sys.argv[1:]\n initialize_logging(app_name=args[0],\n local_log_path=args[3],\n log_file_name='-'.join([args[0], args[1], str(datetime.now().date()), '.log']),\n log_level=logging.DEBUG)\n\n pipelines = {\n \"STG_PIPELINE\":stg_pipelines,\n \"DW_PIPELINE\":dw_pipelines,\n \"DR_PIPELINE\" : None\n }\n\n if not pipelines.get(args[0]):\n raise ValueError(\n \"Invalid first argument passed {0}, should be one of these 'STG_PIPELINE','DW_PIPELINE','DR_PIPELINE'\")\n\n exec_log = logging.getLogger(\"Execution\")\n\n try:\n pipelines[args[0]][args[1]](args[2]).start()\n except Exception as e:\n exec_log.info(str(e))\n exec_log.info(str(e.__class__))", "sub_path": "Projects/PycharmProjects/Nagesh/enterprise-data-platform/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1220, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "sys.argv", "line_number": 13, "usage_type": "attribute"}, {"api_name": "app.common.utils.app_logger.initialize_logging", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 16, "usage_type": "name"}, {"api_name": "logging.DEBUG", "line_number": 17, "usage_type": "attribute"}, {"api_name": "app.staging.main.pipelines", "line_number": 20, "usage_type": "name"}, {"api_name": "app.data_warehouse.main.pipelines", "line_number": 21, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "491084434", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# vim: autoindent shiftwidth=4 expandtab textwidth=80 tabstop=4 softtabstop=4\n\n###############################################################################\n# The MIT License (MIT)\n#\n# Copyright (c) 2016 Stacey Sharp (github.com/ssharpjr)\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n###############################################################################\n\n\nimport os\nimport sys\nimport requests\nfrom time import sleep\nimport json\n\nimport Adafruit_CharLCD as LCD\nimport Adafruit_GPIO.MCP230xx as MCP\nimport RPi.GPIO as IO # For standard GPIO methods.\n\n\n# Variables\nDEBUG = True\napi_url = 'http://10.130.0.42' # Web API URL\n\n# GPIO Setup\nrst_btn = 18 # INPUT - Manually restart the program.\nir_pin = 23 # INPUT - Reads the IR sensor state.\nssr_pin = 24 # OUTPUT - Turns on the Solid State Relay.\n\nIO.setmode(IO.BCM)\nIO.setup(ssr_pin, IO.OUT, initial=0)\n\n# Wire IR sensor from PIN to GND. Default state = False.\n# The edge will RISE when a signal is present.\n# IO.setup(ir_pin, IO.IN, pull_up_down=IO.PUD_UP)\n\n# The Banner sensor sends a voltage signal so pull down.\nIO.setup(ir_pin, IO.IN, pull_up_down=IO.PUD_DOWN)\n\n# Wire the restart button from PIN to 3V3. Default state = True.\n# The edge will FALL when pressed.\nIO.setup(rst_btn, IO.IN, pull_up_down=IO.PUD_DOWN)\n\n\n###############################################################################\n# Setup the LCD and MCP.\n###############################################################################\n# Define the MCP pins connected to the LCD.\n# Note: These are MCP pins, not RPI pins.\nlcd_rs = 0\nlcd_en = 1\nlcd_d4 = 2\nlcd_d5 = 3\nlcd_d6 = 4\nlcd_d7 = 5\nlcd_red = 6\nlcd_green = 7\nlcd_blue = 8\nlcd_columns = 20\nlcd_rows = 4\n\n# Initialize MCP23017 device using its default 0x20 I2C address.\ngpio = MCP.MCP23017()\n\n# Initialize the LCD using the pins.\nlcd = LCD.Adafruit_RGBCharLCD(lcd_rs, lcd_en, lcd_d4, lcd_d5, lcd_d6, lcd_d7,\n lcd_columns, lcd_rows, lcd_red, lcd_green,\n lcd_blue, gpio=gpio)\n###############################################################################\n\n\ndef lcd_ctrl(msg, color, clear=True):\n # Send instructions to the LCD.\n # Colors are Red, Green, Blue values.\n # all zeros equals off, all ones equals white\n if clear:\n lcd.clear()\n\n colors = {\n 'red': (1.0, 0.0, 0.0),\n 'green': (0.0, 1.0, 0.0),\n 'blue': (0.0, 0.0, 1.0),\n 'white': (1.0, 1.0, 1.0),\n 'off': (0.0, 0.0, 0.0)\n }\n\n c = colors.get(color)\n lcd.set_color(*c)\n lcd.message(msg)\n\n\ndef get_press_id():\n # Get PRESS_ID from /boot/PRESS_ID file\n # Close the program if no PRESS_ID is found\n press_id_file = \"/boot/PRESS_ID\"\n try:\n with open(press_id_file) as f:\n PRESS_ID = f.read().replace('\\n', '')\n if len(PRESS_ID) >= 3:\n return PRESS_ID\n else:\n raise ValueError(\"PRESS_ID is Not Assigned!\\nExiting\")\n sys.exit()\n except IOError:\n print(press_id_file + \" Not Found!\\nExiting\")\n sys.exit()\n except BaseException as e:\n print(e)\n sys.exit()\n\n\ndef network_fail():\n if DEBUG:\n print(\"Failed to get data from API\")\n print(\"System will restart in 10 seconds.\")\n if lcd:\n lcd_ctrl(\"NETWORK FAILURE\\nIf this persists\\ncontact TPI IT Dept.\\n \\\n Restarting...\", 'red')\n sleep(5)\n run_or_exit_program('run')\n\n\ndef get_wo_scan():\n if lcd:\n lcd_ctrl(\"SCAN\\n\\nWORKORDER NUMBER\", 'white')\n # wo_scan = '9934386' # Should be 9934386 for test.\n wo_scan = input(\"Scan Workorder: \")\n # wo_scan = sys.stdin.readline().rstrip()\n return wo_scan\n\n\ndef wo_api_request(wo_id):\n # Notify user of potential pause\n if lcd:\n lcd_ctrl(\"GETTING\\nWORKORDER\\nINFORMATION...\", 'blue')\n\n url = api_url + '/wo/' + wo_id\n resp = requests.get(url=url, timeout=10)\n data = json.loads(resp.text)\n\n try:\n if data['error']:\n if lcd:\n lcd_ctrl(\"INVALID WORKORDER!\", 'red')\n if DEBUG:\n print(\"Invalid Workorder! (data = error)\")\n sleep(2) # Pause so the user can read the error.\n run_or_exit_program('run')\n except:\n pass\n try:\n press_from_api_wo = data['press']\n rmat_from_api_wo = data['rmat']\n return press_from_api_wo, rmat_from_api_wo\n except:\n pass\n\n\ndef serial_api_request(sn):\n # Notify user of the potential pause\n if lcd:\n lcd_ctrl(\"GETTING\\nRAW MATERIAL\\nSERIAL NUMBER\\nINFORMATION...\",\n 'blue')\n\n url = api_url + '/serial/' + sn\n resp = requests.get(url=url, timeout=10)\n data = json.loads(resp.text)\n\n try:\n if data['error']:\n if lcd:\n lcd_ctrl(\"INVALID SERIAL\\nNUMBER!\", 'red')\n if DEBUG:\n print(\"Invalid Serial Number! (data = error)\")\n sleep(2) # Pause so the user can read the error.\n run_or_exit_program('run')\n except:\n pass\n try:\n rmat_from_api = data['itemno']\n except:\n pass\n return rmat_from_api\n\n\ndef get_rmat_scan():\n # Get the Raw Material Serial Number.\n # Check for the \"S\" qualifier.\n # Strip the qualifier is present and return the serial number.\n if lcd:\n lcd_ctrl(\"SCAN\\nRAW MATERIAL\\nSERIAL NUMBER\", 'white')\n rmat_scan = str(input(\"Scan Raw Material Serial Number: \"))\n if not rmat_scan.startswith('S'):\n if lcd:\n lcd_ctrl(\"NOT A VALID\\nSERIAL NUMBER!\", 'red')\n if DEBUG:\n print(\"Not a Serial Number! (missing \\\"S\\\" qualifier)\")\n sleep(2) # Pause so the user can read the error.\n run_or_exit_program('run')\n rmat_scan = rmat_scan[1:] # Strip off the \"S\" Qualifier.\n return rmat_scan\n\n\ndef wo_monitor(PRESS_ID, wo_id_from_wo):\n # Check if the workorder number changes (RT workorder unloaded).\n if DEBUG:\n print(\"Checking loaded workorder\")\n url = api_url + '/press/' + PRESS_ID\n resp = requests.get(url=url, timeout=10)\n data = json.loads(resp.text)\n\n# if data['error']:\n# lcd_ctrl(\"WORKORDER CHANGED!\\n\\nRESTARTING\", 'red')\n# if DEBUG:\n# print(\"Workorder changed! (data = error)\")\n# sleep(2) # Pause so the user can read the error.\n# run_or_exit_program('run')\n#\n try:\n press_id_from_api = data['press_id']\n wo_id_from_api = data['wo_id']\n itemno_from_api = data['itemno']\n descrip_from_api = data['descrip']\n itemno_mat_from_api = data['itemno_mat']\n descrip_mat_from_api = data['descrip_mat']\n if DEBUG:\n print(\"WO from API: \" + wo_id_from_api)\n except:\n if DEBUG:\n print(\"\\nAPI Data incomplete\")\n print(press_id_from_api)\n print(wo_id_from_api)\n print(itemno_from_api)\n print(descrip_from_api)\n print(itemno_mat_from_api)\n print(descrip_mat_from_api)\n print(\"\\n\")\n\n if wo_id_from_wo != wo_id_from_api:\n if DEBUG:\n print(\"Workorders do not match. Restarting\")\n run_or_exit_program('run')\n else:\n if DEBUG:\n print(\"WO looks good, restarting run_mode() loop\")\n\n\ndef sensor_monitor():\n # Check to see if the IR beam is broken (0).\n # A broken beam means there is a pallet present.\n if DEBUG == 2:\n print(\"sensor_monitor() running\")\n if IO.input(ir_pin) == 1:\n if DEBUG:\n print(\"Sensor detected. Pallet moved\")\n if lcd:\n lcd_ctrl(\"NO PALLET DETECTED\\n\\nRESTARTING\", 'red')\n sleep(2)\n run_or_exit_program('run')\n return\n\n\ndef sensor_startup_check():\n # Check the pallet sensor on startup.\n # Keep checking until it is present.\n if DEBUG:\n print(\"Checking Pallet Sensor\")\n while IO.input(ir_pin) == 1:\n # if IO.input(ir_pin) == 1:\n if DEBUG == 2:\n print(\"No pallet detected.\")\n if lcd:\n lcd_ctrl(\"NO PALLET DETECTED!\\n\\nCHECKING AGAIN\\nIN 10 SECS\",\n 'red')\n sleep(10)\n if lcd:\n lcd_ctrl(\"PALLET DETECTED\\n\\nCONTINUING\", 'white')\n sleep(2)\n\n\ndef start_loader():\n if DEBUG:\n print(\"\\nEnergizing Loader\")\n sleep(0.5)\n IO.output(ssr_pin, 1) # Turn on the Solid State Relay.\n\n\ndef stop_loader():\n if DEBUG:\n print(\"\\nDe-energizing Loader\")\n sleep(0.5)\n IO.output(ssr_pin, 0) # Turn off the Solid State Relay.\n\n\ndef restart_program():\n print(\"\\nRestarting program\")\n # sleep(1)\n IO.cleanup()\n os.execv(__file__, sys.argv)\n\n\ndef reboot_system():\n if lcd:\n lcd.clear()\n lcd_ctrl(\"REBOOTING SYSTEM\\n\\nSTANDBY...\", 'blue')\n IO.cleanup()\n os.system('sudo reboot')\n\n\ndef run_or_exit_program(status):\n if status == 'run':\n restart_program()\n elif status == 'exit':\n print(\"\\nExiting\")\n lcd.set_color(0, 0, 0) # Turn off backlight\n lcd.clear()\n IO.cleanup()\n sys.exit()\n\n\n# Interrupt Callback function\n# def beam_cb(channel):\n# if DEBUG:\n# print(\"beam_cb() callback called\")\n# sleep(0.1)\n# stop_loader()\n# check_outlet_beam()\n\n\n# def rst_btn_cb(channel):\n# if DEBUG:\n# print(\"rst_btn_cb() callback called\")\n# sleep(0.1)\n# stop_loader()\n# lcd_ctrl(\"RESETTING\\nLOADER\\nCONTROLLER\", 'white')\n# sleep(1)\n# restart_program()\n\n\ndef run_mode(PRESS_ID, wo_id_from_wo):\n # Run a timed loop, checking the IR sensor and API\n if DEBUG == 2:\n print(\"run_mode() running\")\n c = 0 # Reset counter\n while True:\n if DEBUG == 2:\n print(\"Counter: \" + str(c))\n c = c + 1\n sleep(1)\n if c % 10 == 0: # Check the sensor every 10 seconds\n if DEBUG == 2:\n print(\"Counter hit 10\")\n sensor_monitor()\n if c % 300 == 0: # Check the API every 5 minutes\n if DEBUG == 2:\n print(\"Counter hit 60\")\n wo_monitor(PRESS_ID, wo_id_from_wo)\n if DEBUG:\n print(\"Resetting run_mode() counter\")\n c = 0 # Reset counter\n\n\n###############################################################################\n# Interrupts\n# If the reset button is pressed, restart the program\n# IO.add_event_detect(rst_btn, IO.RISING, callback=rst_btn_cb, bouncetime=300)\n###############################################################################\n\n\n###############################################################################\n# Main\n###############################################################################\n\ndef main():\n # Get the PRESS_ID before doing anything else\n PRESS_ID = get_press_id()\n\n print(\"\\nStarting Loader Controller Program\")\n print(\"For Press \" + PRESS_ID)\n if lcd:\n lcd_msg = \"LOADER CONTROLLER\\n\\n\\nPRESS \" + PRESS_ID\n lcd_ctrl(lcd_msg, 'white')\n sleep(2)\n\n # Check if the Pallet Sensor is open (a Pallet is present).\n sensor_startup_check()\n\n # Request the Workorder Number (ID) Barcode.\n wo_id_from_wo = get_wo_scan()\n if DEBUG:\n print(\"Scanned Work Order: \" + wo_id_from_wo)\n\n # Request Press Number and Raw Material Item Number from the API.\n if DEBUG:\n print(\"Requesting data from API\")\n\n try:\n press_from_api_wo, rmat_from_api_wo = wo_api_request(wo_id_from_wo)\n except:\n network_fail()\n\n if DEBUG:\n print(\"Press Number from API: \" + press_from_api_wo)\n print(\"RM Item Number from API: \" + rmat_from_api_wo)\n\n # Verify the Press Number.\n if DEBUG:\n print(\"Checking if workorder is currently running on this press...\")\n if press_from_api_wo == PRESS_ID:\n if DEBUG:\n print(\"Match. Workorder: \" + wo_id_from_wo +\n \" is running on Press #\" + PRESS_ID)\n print(\"Good Workorder. Continuing...\")\n else:\n if lcd:\n lcd_ctrl(\"INCORRECT\\nWORKORDER!\", 'red')\n if DEBUG:\n print(\"Incorrect Workorder!\")\n print(\"This Workorder is for press: \" + press_from_api_wo)\n sleep(2) # Pause so the user can see the error.\n run_or_exit_program('run')\n\n # Scan the Raw Material Serial Number Barcode.\n serial_from_label = get_rmat_scan()\n if DEBUG:\n print(\"Serial Number from Label: \" + serial_from_label)\n\n # Request Raw Material Item Number from the API.\n rmat_from_api_inv = serial_api_request(serial_from_label)\n if DEBUG:\n print(\"RM Item Number from API: \" + rmat_from_api_inv)\n\n # Verify the Raw Material Item Number.\n if DEBUG:\n print(\"Checking if raw material matches this workorder...\")\n if rmat_from_api_wo == rmat_from_api_inv:\n if DEBUG:\n print(\"Material matches workorder. Continuing...\")\n print(\"Starting the Loader!\")\n\n start_loader() # Looks good, turn on the loader.\n if lcd:\n lcd_msg = \"PRESS: \" + PRESS_ID + \"\\nWORKORDER: \" + wo_id_from_wo +\\\n \"\\n\\nLOADER RUNNING\"\n lcd_ctrl(lcd_msg, 'green')\n run_mode(PRESS_ID, wo_id_from_wo) # Start the monitors\n else:\n if DEBUG:\n print(\"Invalid Material!\")\n if lcd:\n lcd_ctrl(\"INCORRECT\\nMATERIAL!\", 'red')\n sleep(2) # Pause so the user can see the error.\n run_or_exit_program('run')\n\n\ndef run():\n while True:\n try:\n main()\n except KeyboardInterrupt:\n run_or_exit_program('exit')\n except BaseException as e:\n print(e)\n run_or_exit_program('exit')\n\nif __name__ == '__main__':\n run()\n", "sub_path": "loader-controller.py", "file_name": "loader-controller.py", "file_ext": "py", "file_size_in_byte": 14799, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "RPi.GPIO.setmode", "line_number": 50, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 50, "usage_type": "name"}, {"api_name": "RPi.GPIO.BCM", "line_number": 50, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 51, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 51, "usage_type": "name"}, {"api_name": "RPi.GPIO.OUT", "line_number": 51, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 58, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 58, "usage_type": "name"}, {"api_name": "RPi.GPIO.IN", "line_number": 58, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.PUD_DOWN", "line_number": 58, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 62, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 62, "usage_type": "name"}, {"api_name": "RPi.GPIO.IN", "line_number": 62, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.PUD_DOWN", "line_number": 62, "usage_type": "attribute"}, {"api_name": "Adafruit_GPIO.MCP230xx.MCP23017", "line_number": 83, "usage_type": "call"}, {"api_name": "Adafruit_GPIO.MCP230xx", "line_number": 83, "usage_type": "name"}, {"api_name": "Adafruit_CharLCD.Adafruit_RGBCharLCD", "line_number": 86, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 123, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 126, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 129, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 139, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 158, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 159, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 167, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 186, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 187, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 195, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 218, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 229, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 230, "usage_type": "call"}, {"api_name": "RPi.GPIO.input", "line_number": 273, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 273, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 278, "usage_type": "call"}, {"api_name": "RPi.GPIO.input", "line_number": 288, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 288, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 295, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 298, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 304, "usage_type": "call"}, {"api_name": "RPi.GPIO.output", "line_number": 305, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 305, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 311, "usage_type": "call"}, {"api_name": "RPi.GPIO.output", "line_number": 312, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 312, "usage_type": "name"}, {"api_name": "RPi.GPIO.cleanup", "line_number": 318, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 318, "usage_type": "name"}, {"api_name": "os.execv", "line_number": 319, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 319, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.cleanup", "line_number": 326, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 326, "usage_type": "name"}, {"api_name": "os.system", "line_number": 327, "usage_type": "call"}, {"api_name": "RPi.GPIO.cleanup", "line_number": 337, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 337, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 338, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 369, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 403, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 440, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 472, "usage_type": "call"}]} +{"seq_id": "496230762", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Dec 20 22:32:43 2020\n\n@author: antoine\n\"\"\"\n\nimport detection\nimport flux_images\nimport numpy as np\nimport time\n \nimport matplotlib.pyplot as plt\n\nfrom multiprocessing import Process,Queue\n\n\n\ndef stage_reception(q_in,q_out):\n while(True):\n t_start = time.perf_counter() \n if(not q_in.empty()):\n if q_in.get().kill:\n return\n q_out.put(PipeData(flux_images.get_image()))\n print('reception: ' + str(time.perf_counter() - t_start))\n\n \ndef stage_filter(q_in,q_out,window_half_size):\n while(True):\n to_process = q_in.get()\n if to_process.kill:\n return\n q_out.put(detection.median_filter(to_process.data,window_half_size,window_half_size))\n \ndef stage_main(q_in,q_out,num_process,window_half_size,tresh_limit):\n old_image=[]\n new_image=[]\n while(True):\n old_image = new_image\n new_val = q_in.get()\n t_start = time.perf_counter()\n if new_val.kill:\n return\n new_image_rgb = new_val.data\n new_image = detection.rgb_to_wb(new_image_rgb)\n if(len(old_image)!=0):\n diff_of_images = detection.diff_of_images(old_image,new_image).astype(np.uint8)\n #num_ligne = diff_of_images.shape[0]\n #for i in range(num_process):\n # inf_limit = int(min(np.round(i*num_ligne/num_process - window_half_size),0))\n # sup_limit = int( max(np.round(num_ligne*((i+1)/num_process) + window_half_size),num_ligne))\n # qs_in_filter[i].put(PipeData( diff_of_images[inf_limit:sup_limit]))\n \n #filtered_image = qs_out_filter[0].get()[window_half_size+1:]\n #for i in range(1,num_process-1):\n # filtered_image = np.append(filtered_image,\n # qs_out_filter[i].get()[window_half_size+1:-window_half_size],axis=0)\n #filtered_image = np.append(filtered_image,\n # qs_out_filter[num_process-1].get()[:-window_half_size],axis=0) \n filtered_image = detection.median_filter(diff_of_images,window_half_size,window_half_size)\n tresh_image = detection.tresh(filtered_image,tresh_limit)\n contour_image = detection.contour_bool(tresh_image)\n alert = tresh_image.any()\n contour_et_image = detection.display_contour(new_image_rgb,contour_image)\n q_out.put(PipeData(contour_et_image,alert=alert))\n else:\n q_out.put(PipeData(new_image_rgb))\n print('detection: ' + str(time.perf_counter() - t_start))\n \nclass PipeData:\n def __init__(self,data=[],kill=False,alert=False):\n self.alert=alert\n self.data = data\n self.kill = kill\n \n \nclass Pipeline:\n def __init__(self,num_process_filter=1,window_half_size=2,tresh_level=40):\n self.num_process_filter = num_process_filter\n self.queue_pour_detection = Queue()\n self.queue_pour_affichage = Queue()\n self.queue_pour_reception = Queue()\n #self.queue_de_filtre = []\n #self.queue_pour_filtre = []\n #for i in range(num_process_filter):\n # self.queue_de_filtre.append(Queue())\n # self.queue_pour_filtre.append(Queue())\n \n self.stage_reception = Process(target=stage_reception, args=(self.queue_pour_reception,self.queue_pour_detection,))\n self.stage_main = Process(target=stage_main, args=(self.queue_pour_detection,\n self.queue_pour_affichage,\n #self.queue_pour_filtre,\n #self.queue_de_filtre,\n num_process_filter,\n window_half_size,\n tresh_level,))\n #self.stages_filter = []\n #for i in range(num_process_filter):\n # self.stages_filter.append(Process(target=stage_filter, args=(self.queue_pour_filtre[i],\n # self.queue_de_filtre[i],\n # window_half_size,)))\n \n def start(self):\n self.stage_reception.start()\n #for i in range(self.num_process_filter):\n # self.stages_filter[i].start()\n self.stage_main.start()\n return self.queue_pour_affichage\n def kill(self):\n \n self.queue_pour_detection.put(PipeData(kill=True))\n #for i in range(self.num_process_filter):\n # self.queue_pour_filtre[i].put(PipeData(kill=True))\n \n self.queue_pour_reception.put(PipeData(kill=True))\n \n \n \nif __name__=='__main__':\n num = 20 \n \n test = Pipeline()\n queu_test = test.start()\n \n t_start= time.perf_counter()\n for i in range(num):\n plt.imshow(queu_test.get().data)\n plt.text(0,0,\"test\")\n plt.show()\n \n t_stop = time.perf_counter()\n t_total = t_stop-t_start\n print(t_total/num)\n test.kill()\n ", "sub_path": "http_detection_pipeline.py", "file_name": "http_detection_pipeline.py", "file_ext": "py", "file_size_in_byte": 5309, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "time.perf_counter", "line_number": 22, "usage_type": "call"}, {"api_name": "flux_images.get_image", "line_number": 26, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 27, "usage_type": "call"}, {"api_name": "detection.median_filter", "line_number": 35, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 43, "usage_type": "call"}, {"api_name": "detection.rgb_to_wb", "line_number": 47, "usage_type": "call"}, {"api_name": "detection.diff_of_images", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 49, "usage_type": "attribute"}, {"api_name": "detection.median_filter", "line_number": 62, "usage_type": "call"}, {"api_name": "detection.tresh", "line_number": 63, "usage_type": "call"}, {"api_name": "detection.contour_bool", "line_number": 64, "usage_type": "call"}, {"api_name": "detection.display_contour", "line_number": 66, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 70, "usage_type": "call"}, {"api_name": "multiprocessing.Queue", "line_number": 82, "usage_type": "call"}, {"api_name": "multiprocessing.Queue", "line_number": 83, "usage_type": "call"}, {"api_name": "multiprocessing.Queue", "line_number": 84, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 91, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 92, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "time.perf_counter", "line_number": 133, "usage_type": "call"}]} +{"seq_id": "46882101", "text": "###############################################################################\n# File : q_q_plot.py\n# Author : Neil Massey\n# Created : 11/08/11\n# Purpose : Plot non-conditional quantiles\n###############################################################################\n\nimport matplotlib.pyplot as plt\nfrom scipy import stats\nimport numpy\nimport random\n\n###############################################################################\n\ndef q_q_plot(obs, ens, p_tiles):\n\tsp = plt.subplot(111)\n\t# plot the (non-conditional) quantiles\n\tobs_quantiles = []\n\tens_quantiles = []\n\tfor i in range(1, 99):\n\t\tobs_quantiles.append(stats.scoreatpercentile(obs.flatten(), i))\n\t\tens_quantiles.append(stats.scoreatpercentile(ens.flatten(), i))\n\tsp.plot(obs_quantiles, ens_quantiles, 'k--', lw=1.5, zorder=1)\n\tens_range = ens_quantiles[-1] - ens_quantiles[0]\n\t# plot the percentiles in the p_tiles list)\n\tfor p in p_tiles:\n\t\tobs_p = stats.scoreatpercentile(obs.flatten(), p)\n\t\tens_p = stats.scoreatpercentile(ens.flatten(), p)\n\t\tsp.plot(obs_p, ens_p, 'k+', ms=12, zorder=1)\n\t\tsp.text(obs_p, ens_p-0.05*ens_range, str(p), ha='center', va='bottom',\n\t\t\t\tzorder=1)\n\n\tsmallest_x = numpy.min([obs_quantiles[0], ens_quantiles[0]])\n\tlargest_x = numpy.max([obs_quantiles[-1], ens_quantiles[-1]])\n\n\t# 1:1 line - lowest z order\n\tsp.plot([smallest_x, largest_x], [smallest_x, largest_x], 'k', lw=2.0,\n\t\t\tzorder=0)\n\t# limits\n\tsp.set_xlim([smallest_x, largest_x])\n\tsp.set_ylim([smallest_x, largest_x])\n\tsp.set_aspect(1.0)\n\treturn sp\n\n###############################################################################\n\ndef multi_q_q_plot(plt_obj, obs, ens, line_shp=['--', '-', '-.-'], colors=['r','b','c'], p_tiles=[5,10,50,90,95], lw=1.0):\n\t# multiple data version of q-q plot\n\t# number of observations and ensemble members\n\tn_dsets = len(obs)\n\tif n_dsets != len(ens):\n\t\traise(\"Observations and Ensembles do not have the same number of datasets to plot\")\n\n\t# calculate the percentiles\n\tmin_val = 2e20\n\tmax_val = -2e20\n\tlines = []\n\tfor n in range(0, n_dsets):\n\t\tobs_quantiles = []\n\t\tens_quantiles = []\n\t\tfor i in range(1, 99):\n\t\t\tobs_quantiles.append(stats.scoreatpercentile(obs[n].flatten(), i))\n\t\t\tens_quantiles.append(stats.scoreatpercentile(ens[n].flatten(), i))\n\t\tl = plt_obj.plot(obs_quantiles, ens_quantiles, color=colors[n], ls=line_shp[n], \n\t\t\t\t\t\t lw=lw, zorder=1)\n\t\tlines.append(l[0])\n\t\tens_range = ens_quantiles[-1] - ens_quantiles[0]\n\t\t# plot the percentiles in the p_tiles list)\n\t\tif n == 1:\n\t\t\tfor p in p_tiles:\n\t\t\t\tobs_p = stats.scoreatpercentile(obs[n].flatten(), p)\n\t\t\t\tens_p = stats.scoreatpercentile(ens[n].flatten(), p)\n\t\t\t\tplt_obj.plot(obs_p, ens_p, color= colors[n], marker='s', ms=4, \n\t\t\t\t\t\t\t mec=colors[n], zorder=1)\n\t\t\t\tplt_obj.text(obs_p, ens_p+0.05*ens_range, str(p), ha='center', va='bottom', \n\t\t\t\t\t\t\t color='k', zorder=1)#colors[n])\n\t\t# keep a record of the min and max value\n\t\tmin_val = numpy.min([obs_quantiles[0], ens_quantiles[0], min_val])\n\t\tmax_val = numpy.max([obs_quantiles[-1], ens_quantiles[-1], max_val])\n\n\t# 1:1 line - lowest z order\n\tplt_obj.plot([min_val, max_val], [min_val, max_val], 'k', lw=2.0, \n\t\t\t\t zorder=0)\n\t# limits\n\tplt_obj.set_xlim([min_val, max_val])\n\tplt_obj.set_ylim([min_val, max_val])\n\tplt_obj.set_aspect(1.0)\n\treturn lines\n\n###############################################################################\n\ndef q_q_plot_confidence(obs, ens, p_tiles, colors=['r'], bsn=1e5):\n\t# produce a quantile quantile plot with confidence limits between 5th and 95th\n\t# percentile of the percentile value\n\t# ens can be a list for multiple values\n\t# bsn = boot strap sample number\n\t# create the plot\n\tsp = plt.subplot(111)\n\n\t# calculate the observed percentiles first\n\tobs_quantiles = []\n\tobs_f = obs.flatten()\n\tobs_f.sort()\n\t# loop through the 1st to 99th percentile\n\tfor pt in range(1, 99):\n\t\t# get the observed scoreatpercentile for this percentile\n\t\tobs_quantiles.append(stats.scoreatpercentile(obs_f, pt))\n\n\tc = 0\n\tlegend_lines = []\n\tfor n in range(0, len(ens)):\n\t\te = ens[n]\n\t\t# create the storage\n\t\tens_quantiles = []\n\t\tens_quantiles_5th = []\n\t\tens_quantiles_50th = []\n\t\tens_quantiles_95th = []\n\n\t\t# flattened observation and ensemble\n\t\tens_f = e.flatten()\n\t\tens_f.sort()\n\t\t# number of ensemble members\n\t\tens_n = e.shape[0]\n\n\t\t# create the sample indices\n\t\t# this ensures we sample the same ensemble member for each percentile\n\t\tsample_idx = []\n\t\tfor bn in range(0, bsn):\n\t\t\tsi = int(random.uniform(0, ens_n))\n\t\t\tsample_idx.append(si)\n\n\t\t# loop through the 1st to 99th percentile\n\t\tfor pt in range(1, 99):\n\t\t\t# build up a sample set by sampling each ensemble member - with replacement\n\t\t\tens_samples = []\n\t\t\tfor bn in range(0, bsn):\n\t\t\t\tidx = sample_idx[bn]\t\t# get a random ensemble member\n\t\t\t\tmember_data = e[idx]\n\t\t\t\t# get the percentile score and add it to the ens_samples\n\t\t\t\tv = stats.scoreatpercentile(member_data, pt)\n\t\t\t\tens_samples.append(v)\n\t\t\t# get the 5th, 50th and 95th percentile of these values\n\t\t\tens_quantiles_5th.append(stats.scoreatpercentile(ens_samples, 5))\n\t\t\tens_quantiles_50th.append(stats.scoreatpercentile(ens_samples, 50))\n\t\t\tens_quantiles_95th.append(stats.scoreatpercentile(ens_samples, 95))\n\t\t\t# add the actual ensemble variable\n\t\t\tens_quantiles.append(stats.scoreatpercentile(ens_f, pt))\n\n\t\t# plot the actual ensemble vs observations quantiles\n\t\tl = sp.plot(obs_quantiles, ens_quantiles, ls='-', c=colors[c], lw=1.5, zorder=1)\n\t\tlegend_lines.append(l[0])\n\n\t\tsp.fill_between(obs_quantiles, ens_quantiles_5th, ens_quantiles_95th, \n\t\t\t\t\t\tfacecolor=colors[c], edgecolor=colors[c], alpha=0.75)\n\t\t# plot the percentiles in the p_tiles list)\n\t\tens_range = ens_quantiles[-1] - ens_quantiles[0]\n\t\tif n == 1:\t# only do one\n\t\t\tfor p in p_tiles:\n\t\t\t\tobs_p = stats.scoreatpercentile(obs.flatten(), p)\n\t\t\t\tens_p = stats.scoreatpercentile(e.flatten(), p)\n\t\t\t\tsp.plot(obs_p, ens_p, mfc=colors[c], mec=colors[c], marker='s', ms=4, zorder=1)\n\t\t\t\tif c % 2 == 0:\n\t\t\t\t\tvp = ens_p - 0.035*ens_range\n\t\t\t\telse:\n\t\t\t\t\tvp = ens_p + 0.035*ens_range\n\t\t\t\tsp.text(obs_p, vp, str(p), ha='center', va='bottom',\n\t\t\t\t\t\tzorder=1)\n\n\t\t# next color\n\t\tc+=1\n\n\tsmallest_x = numpy.min([obs_quantiles[0], ens_quantiles[0]])\n\tlargest_x = numpy.max([obs_quantiles[-1], ens_quantiles[-1]])\n\n\t# 1:1 line - lowest z order\n\tsp.plot([smallest_x, largest_x], [smallest_x, largest_x], 'k', lw=2.0,\n zorder=0)\n\t# limits\n\tsp.set_xlim([smallest_x, largest_x])\n\tsp.set_ylim([smallest_x, largest_x])\n\tsp.set_aspect(1.0)\n\treturn sp, legend_lines\n", "sub_path": "q_q_plot.py", "file_name": "q_q_plot.py", "file_ext": "py", "file_size_in_byte": 6469, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "matplotlib.pyplot.subplot", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "scipy.stats.scoreatpercentile", "line_number": 21, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 21, "usage_type": "name"}, {"api_name": "scipy.stats.scoreatpercentile", "line_number": 22, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 22, "usage_type": "name"}, {"api_name": "scipy.stats.scoreatpercentile", "line_number": 27, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 27, "usage_type": "name"}, {"api_name": "scipy.stats.scoreatpercentile", "line_number": 28, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 28, "usage_type": "name"}, {"api_name": "numpy.min", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 34, "usage_type": "call"}, {"api_name": "scipy.stats.scoreatpercentile", "line_number": 62, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 62, "usage_type": "name"}, {"api_name": "scipy.stats.scoreatpercentile", "line_number": 63, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 63, "usage_type": "name"}, {"api_name": "scipy.stats.scoreatpercentile", "line_number": 71, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 71, "usage_type": "name"}, {"api_name": "scipy.stats.scoreatpercentile", "line_number": 72, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 72, "usage_type": "name"}, {"api_name": "numpy.min", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "scipy.stats.scoreatpercentile", "line_number": 107, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 107, "usage_type": "name"}, {"api_name": "random.uniform", "line_number": 129, "usage_type": "call"}, {"api_name": "scipy.stats.scoreatpercentile", "line_number": 140, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 140, "usage_type": "name"}, {"api_name": "scipy.stats.scoreatpercentile", "line_number": 143, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 143, "usage_type": "name"}, {"api_name": "scipy.stats.scoreatpercentile", "line_number": 144, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 144, "usage_type": "name"}, {"api_name": "scipy.stats.scoreatpercentile", "line_number": 145, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 145, "usage_type": "name"}, {"api_name": "scipy.stats.scoreatpercentile", "line_number": 147, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 147, "usage_type": "name"}, {"api_name": "scipy.stats.scoreatpercentile", "line_number": 159, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 159, "usage_type": "name"}, {"api_name": "scipy.stats.scoreatpercentile", "line_number": 160, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 160, "usage_type": "name"}, {"api_name": "numpy.min", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 173, "usage_type": "call"}]} +{"seq_id": "221074399", "text": "\"\"\"\nThis module contains the actions used in the Toolbox (lower left section\nof the main window.\n\nThe Toolbox is bound to a diagram. When a diagram page (tab) is switched,\nthe actions bound to the toolbuttons should change as well.\n\"\"\"\n\nfrom typing import Callable, NamedTuple, Optional, Sequence, Tuple\n\nfrom gaphas.item import SE\n\nfrom gaphor import UML, diagram\nfrom gaphor.core import gettext\nfrom gaphor.diagram.diagramtools import DefaultTool, PlacementTool\nfrom gaphor.UML.event import DiagramItemCreated\n\n__all__ = [\"TOOLBOX_ACTIONS\"]\n\nItemFactory = Callable[[UML.Diagram, Optional[UML.Presentation]], UML.Presentation]\n\n\nclass ToolDef(NamedTuple):\n id: str\n name: str\n icon_name: str\n shortcut: Optional[str]\n item_factory: Optional[ItemFactory]\n handle_index: int = -1\n\n\ndef namespace_config(new_item):\n subject = new_item.subject\n diagram = new_item.canvas.diagram\n subject.package = diagram.namespace\n subject.name = f\"New{type(subject).__name__}\"\n\n\ndef initial_pseudostate_config(new_item):\n new_item.subject.kind = \"initial\"\n\n\ndef history_pseudostate_config(new_item):\n new_item.subject.kind = \"shallowHistory\"\n\n\ndef metaclass_config(new_item):\n namespace_config(new_item)\n new_item.subject.name = \"Class\"\n\n\n# Actions: ((section (name, label, icon_name, shortcut)), ...)\nTOOLBOX_ACTIONS: Sequence[Tuple[str, Sequence[ToolDef]]] = (\n (\n gettext(\"General\"),\n (\n ToolDef(\n \"toolbox-pointer\",\n gettext(\"Pointer\"),\n \"gaphor-pointer-symbolic\",\n \"Escape\",\n item_factory=None,\n ),\n ToolDef(\n \"toolbox-line\",\n gettext(\"Line\"),\n \"gaphor-line-symbolic\",\n \"l\",\n PlacementTool.new_item_factory(diagram.general.Line),\n ),\n ToolDef(\n \"toolbox-box\",\n gettext(\"Box\"),\n \"gaphor-box-symbolic\",\n \"b\",\n PlacementTool.new_item_factory(diagram.general.Box),\n SE,\n ),\n ToolDef(\n \"toolbox-ellipse\",\n gettext(\"Ellipse\"),\n \"gaphor-ellipse-symbolic\",\n \"e\",\n PlacementTool.new_item_factory(diagram.general.Ellipse),\n SE,\n ),\n ToolDef(\n \"toolbox-comment\",\n gettext(\"Comment\"),\n \"gaphor-comment-symbolic\",\n \"k\",\n PlacementTool.new_item_factory(\n diagram.general.CommentItem, UML.Comment\n ),\n handle_index=SE,\n ),\n ToolDef(\n \"toolbox-comment-line\",\n gettext(\"Comment line\"),\n \"gaphor-comment-line-symbolic\",\n \"K\",\n PlacementTool.new_item_factory(diagram.general.CommentLineItem),\n ),\n ),\n ),\n (\n gettext(\"Classes\"),\n (\n ToolDef(\n \"toolbox-class\",\n gettext(\"Class\"),\n \"gaphor-class-symbolic\",\n \"c\",\n item_factory=PlacementTool.new_item_factory(\n diagram.classes.ClassItem, UML.Class, config_func=namespace_config\n ),\n handle_index=SE,\n ),\n ToolDef(\n \"toolbox-interface\",\n gettext(\"Interface\"),\n \"gaphor-interface-symbolic\",\n \"i\",\n item_factory=PlacementTool.new_item_factory(\n diagram.classes.InterfaceItem,\n UML.Interface,\n config_func=namespace_config,\n ),\n handle_index=SE,\n ),\n ToolDef(\n \"toolbox-package\",\n gettext(\"Package\"),\n \"gaphor-package-symbolic\",\n \"p\",\n PlacementTool.new_item_factory(\n diagram.classes.PackageItem,\n UML.Package,\n config_func=namespace_config,\n ),\n handle_index=SE,\n ),\n ToolDef(\n \"toolbox-association\",\n gettext(\"Association\"),\n \"gaphor-association-symbolic\",\n \"A\",\n PlacementTool.new_item_factory(diagram.classes.AssociationItem),\n ),\n ToolDef(\n \"toolbox-dependency\",\n gettext(\"Dependency\"),\n \"gaphor-dependency-symbolic\",\n \"D\",\n PlacementTool.new_item_factory(diagram.classes.DependencyItem),\n ),\n ToolDef(\n \"toolbox-generalization\",\n gettext(\"Generalization\"),\n \"gaphor-generalization-symbolic\",\n \"G\",\n PlacementTool.new_item_factory(diagram.classes.GeneralizationItem),\n ),\n ToolDef(\n \"toolbox-implementation\",\n gettext(\"Implementation\"),\n \"gaphor-implementation-symbolic\",\n \"I\",\n PlacementTool.new_item_factory(diagram.classes.ImplementationItem),\n ),\n ),\n ),\n (\n gettext(\"Components\"),\n (\n ToolDef(\n \"toolbox-component\",\n gettext(\"Component\"),\n \"gaphor-component-symbolic\",\n \"o\",\n PlacementTool.new_item_factory(\n diagram.components.ComponentItem,\n UML.Component,\n config_func=namespace_config,\n ),\n handle_index=SE,\n ),\n ToolDef(\n \"toolbox-artifact\",\n gettext(\"Artifact\"),\n \"gaphor-artifact-symbolic\",\n \"h\",\n PlacementTool.new_item_factory(\n diagram.components.ArtifactItem,\n UML.Artifact,\n config_func=namespace_config,\n ),\n handle_index=SE,\n ),\n ToolDef(\n \"toolbox-node\",\n gettext(\"Node\"),\n \"gaphor-node-symbolic\",\n \"n\",\n PlacementTool.new_item_factory(\n diagram.components.NodeItem, UML.Node, config_func=namespace_config,\n ),\n handle_index=SE,\n ),\n ToolDef(\n \"toolbox-device\",\n gettext(\"Device\"),\n \"gaphor-device-symbolic\",\n \"d\",\n PlacementTool.new_item_factory(\n diagram.components.NodeItem,\n UML.Device,\n config_func=namespace_config,\n ),\n handle_index=SE,\n ),\n ToolDef(\n \"toolbox-connector\",\n gettext(\"Connector\"),\n \"gaphor-connector-symbolic\",\n \"C\",\n PlacementTool.new_item_factory(diagram.components.ConnectorItem),\n ),\n ),\n ),\n (\n gettext(\"Actions\"),\n (\n ToolDef(\n \"toolbox-action\",\n gettext(\"Action\"),\n \"gaphor-action-symbolic\",\n \"a\",\n item_factory=PlacementTool.new_item_factory(\n diagram.actions.ActionItem,\n UML.Action,\n config_func=namespace_config,\n ),\n handle_index=SE,\n ),\n ToolDef(\n \"toolbox-initial-node\",\n gettext(\"Initial node\"),\n \"gaphor-initial-node-symbolic\",\n \"j\",\n item_factory=PlacementTool.new_item_factory(\n diagram.actions.InitialNodeItem, UML.InitialNode\n ),\n handle_index=SE,\n ),\n ToolDef(\n \"toolbox-activity-final-node\",\n gettext(\"Activity final node\"),\n \"gaphor-activity-final-node-symbolic\",\n \"f\",\n item_factory=PlacementTool.new_item_factory(\n diagram.actions.ActivityFinalNodeItem, UML.ActivityFinalNode\n ),\n handle_index=SE,\n ),\n ToolDef(\n \"toolbox-flow-final-node\",\n gettext(\"Flow final node\"),\n \"gaphor-flow-final-node-symbolic\",\n \"w\",\n item_factory=PlacementTool.new_item_factory(\n diagram.actions.FlowFinalNodeItem, UML.FlowFinalNode\n ),\n handle_index=SE,\n ),\n ToolDef(\n \"toolbox-decision-node\",\n gettext(\"Decision/merge node\"),\n \"gaphor-decision-node-symbolic\",\n \"g\",\n item_factory=PlacementTool.new_item_factory(\n diagram.actions.DecisionNodeItem, UML.DecisionNode\n ),\n handle_index=SE,\n ),\n ToolDef(\n \"toolbox-fork-node\",\n gettext(\"Fork/join node\"),\n \"gaphor-fork-node-symbolic\",\n \"R\",\n item_factory=PlacementTool.new_item_factory(\n diagram.actions.ForkNodeItem, UML.JoinNode\n ),\n handle_index=1,\n ),\n ToolDef(\n \"toolbox-object-node\",\n gettext(\"Object node\"),\n \"gaphor-object-node-symbolic\",\n \"O\",\n item_factory=PlacementTool.new_item_factory(\n diagram.actions.ObjectNodeItem,\n UML.ObjectNode,\n config_func=namespace_config,\n ),\n handle_index=SE,\n ),\n ToolDef(\n \"toolbox-partition\",\n gettext(\"Partition\"),\n \"gaphor-partition-symbolic\",\n \"P\",\n item_factory=PlacementTool.new_item_factory(\n diagram.actions.PartitionItem\n ),\n handle_index=SE,\n ),\n ToolDef(\n \"toolbox-flow\",\n gettext(\"Control/object flow\"),\n \"gaphor-control-flow-symbolic\",\n \"F\",\n item_factory=PlacementTool.new_item_factory(diagram.actions.FlowItem),\n ),\n ToolDef(\n \"toolbox-send-signal-action\",\n gettext(\"Send signal action\"),\n \"gaphor-send-signal-action-symbolic\",\n None,\n item_factory=PlacementTool.new_item_factory(\n diagram.actions.SendSignalActionItem,\n UML.SendSignalAction,\n config_func=namespace_config,\n ),\n handle_index=SE,\n ),\n ToolDef(\n \"toolbox-accept-event-action\",\n gettext(\"Accept event action\"),\n \"gaphor-accept-event-action-symbolic\",\n None,\n item_factory=PlacementTool.new_item_factory(\n diagram.actions.AcceptEventActionItem,\n UML.AcceptEventAction,\n config_func=namespace_config,\n ),\n handle_index=SE,\n ),\n ),\n ),\n (\n gettext(\"Interactions\"),\n (\n ToolDef(\n \"toolbox-lifeline\",\n gettext(\"Lifeline\"),\n \"gaphor-lifeline-symbolic\",\n \"v\",\n item_factory=PlacementTool.new_item_factory(\n diagram.interactions.LifelineItem,\n UML.Lifeline,\n config_func=namespace_config,\n ),\n handle_index=SE,\n ),\n ToolDef(\n \"toolbox-message\",\n gettext(\"Message\"),\n \"gaphor-message-symbolic\",\n \"M\",\n item_factory=PlacementTool.new_item_factory(\n diagram.interactions.MessageItem\n ),\n ),\n ToolDef(\n \"toolbox-execution-specification\",\n gettext(\"Execution Specification\"),\n \"gaphor-execution-specification-symbolic\",\n None,\n item_factory=PlacementTool.new_item_factory(\n diagram.interactions.ExecutionSpecificationItem\n ),\n handle_index=0,\n ),\n ToolDef(\n \"toolbox-interaction\",\n gettext(\"Interaction\"),\n \"gaphor-interaction-symbolic\",\n \"N\",\n item_factory=PlacementTool.new_item_factory(\n diagram.interactions.InteractionItem,\n UML.Interaction,\n config_func=namespace_config,\n ),\n handle_index=SE,\n ),\n ),\n ),\n (\n gettext(\"States\"),\n (\n ToolDef(\n \"toolbox-state\",\n gettext(\"State\"),\n \"gaphor-state-symbolic\",\n \"s\",\n item_factory=PlacementTool.new_item_factory(\n diagram.states.StateItem, UML.State, config_func=namespace_config\n ),\n handle_index=SE,\n ),\n ToolDef(\n \"toolbox-initial-pseudostate\",\n gettext(\"Initial Pseudostate\"),\n \"gaphor-initial-pseudostate-symbolic\",\n \"S\",\n item_factory=PlacementTool.new_item_factory(\n diagram.states.InitialPseudostateItem,\n UML.Pseudostate,\n initial_pseudostate_config,\n ),\n handle_index=SE,\n ),\n ToolDef(\n \"toolbox-final-state\",\n gettext(\"Final State\"),\n \"gaphor-final-state-symbolic\",\n \"x\",\n item_factory=PlacementTool.new_item_factory(\n diagram.states.FinalStateItem, UML.FinalState\n ),\n handle_index=SE,\n ),\n ToolDef(\n \"toolbox-history-pseudostate\",\n gettext(\"History Pseudostate\"),\n \"gaphor-pseudostate-symbolic\",\n \"q\",\n item_factory=PlacementTool.new_item_factory(\n diagram.states.HistoryPseudostateItem,\n UML.Pseudostate,\n history_pseudostate_config,\n ),\n handle_index=SE,\n ),\n ToolDef(\n \"toolbox-transition\",\n gettext(\"Transition\"),\n \"gaphor-transition-symbolic\",\n \"T\",\n item_factory=PlacementTool.new_item_factory(\n diagram.states.TransitionItem\n ),\n ),\n ),\n ),\n (\n gettext(\"Use Cases\"),\n (\n ToolDef(\n \"toolbox-use-case\",\n gettext(\"Use case\"),\n \"gaphor-use-case-symbolic\",\n \"u\",\n item_factory=PlacementTool.new_item_factory(\n diagram.usecases.UseCaseItem,\n UML.UseCase,\n config_func=namespace_config,\n ),\n handle_index=SE,\n ),\n ToolDef(\n \"toolbox-actor\",\n gettext(\"Actor\"),\n \"gaphor-actor-symbolic\",\n \"t\",\n item_factory=PlacementTool.new_item_factory(\n diagram.usecases.ActorItem, UML.Actor, config_func=namespace_config,\n ),\n handle_index=SE,\n ),\n ToolDef(\n \"toolbox-use-case-association\",\n gettext(\"Association\"),\n \"gaphor-association-symbolic\",\n \"B\",\n item_factory=PlacementTool.new_item_factory(\n diagram.classes.AssociationItem\n ),\n ),\n ToolDef(\n \"toolbox-include\",\n gettext(\"Include\"),\n \"gaphor-include-symbolic\",\n \"U\",\n item_factory=PlacementTool.new_item_factory(\n diagram.usecases.IncludeItem\n ),\n ),\n ToolDef(\n \"toolbox-extend\",\n gettext(\"Extend\"),\n \"gaphor-extend-symbolic\",\n \"X\",\n item_factory=PlacementTool.new_item_factory(\n diagram.usecases.ExtendItem\n ),\n ),\n ),\n ),\n (\n gettext(\"Profiles\"),\n (\n ToolDef(\n \"toolbox-profile\",\n gettext(\"Profile\"),\n \"gaphor-profile-symbolic\",\n \"r\",\n item_factory=PlacementTool.new_item_factory(\n diagram.classes.PackageItem,\n UML.Profile,\n config_func=namespace_config,\n ),\n handle_index=SE,\n ),\n ToolDef(\n \"toolbox-metaclass\",\n gettext(\"Metaclass\"),\n \"gaphor-metaclass-symbolic\",\n \"m\",\n item_factory=PlacementTool.new_item_factory(\n diagram.classes.ClassItem, UML.Class, config_func=metaclass_config\n ),\n handle_index=SE,\n ),\n ToolDef(\n \"toolbox-stereotype\",\n gettext(\"Stereotype\"),\n \"gaphor-stereotype-symbolic\",\n \"z\",\n item_factory=PlacementTool.new_item_factory(\n diagram.classes.ClassItem,\n UML.Stereotype,\n config_func=namespace_config,\n ),\n handle_index=SE,\n ),\n ToolDef(\n \"toolbox-extension\",\n gettext(\"Extension\"),\n \"gaphor-extension-symbolic\",\n \"E\",\n item_factory=PlacementTool.new_item_factory(\n diagram.profiles.ExtensionItem\n ),\n ),\n ),\n ),\n)\n", "sub_path": "gaphor/diagram/diagramtoolbox.py", "file_name": "diagramtoolbox.py", "file_ext": "py", "file_size_in_byte": 18720, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "typing.Callable", "line_number": 20, "usage_type": "name"}, {"api_name": "gaphor.UML.Diagram", "line_number": 20, "usage_type": "attribute"}, {"api_name": "gaphor.UML", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 20, "usage_type": "name"}, {"api_name": "gaphor.UML.Presentation", "line_number": 20, "usage_type": "attribute"}, {"api_name": "typing.NamedTuple", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 27, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 28, "usage_type": "name"}, {"api_name": "gaphor.diagram", "line_number": 34, "usage_type": "name"}, {"api_name": "gaphor.diagram.namespace", "line_number": 35, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 35, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 53, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 53, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 55, "usage_type": "call"}, {"api_name": "gaphor.core.gettext", "line_number": 59, "usage_type": "call"}, {"api_name": "gaphor.core.gettext", "line_number": 66, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 69, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 69, "usage_type": "name"}, {"api_name": "gaphor.diagram.general", "line_number": 69, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 69, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 77, "usage_type": "argument"}, {"api_name": "gaphor.core.gettext", "line_number": 73, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 76, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 76, "usage_type": "name"}, {"api_name": "gaphor.diagram.general", "line_number": 76, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 76, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 85, "usage_type": "argument"}, {"api_name": "gaphor.core.gettext", "line_number": 81, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 84, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 84, "usage_type": "name"}, {"api_name": "gaphor.diagram.general", "line_number": 84, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 84, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 89, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 92, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 92, "usage_type": "name"}, {"api_name": "gaphor.diagram.general", "line_number": 93, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 93, "usage_type": "name"}, {"api_name": "gaphor.UML.Comment", "line_number": 93, "usage_type": "attribute"}, {"api_name": "gaphor.UML", "line_number": 93, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 95, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 99, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 102, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 102, "usage_type": "name"}, {"api_name": "gaphor.diagram.general", "line_number": 102, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 102, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 107, "usage_type": "call"}, {"api_name": "gaphor.core.gettext", "line_number": 111, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 114, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 114, "usage_type": "name"}, {"api_name": "gaphor.diagram.classes", "line_number": 115, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 115, "usage_type": "name"}, {"api_name": "gaphor.UML.Class", "line_number": 115, "usage_type": "attribute"}, {"api_name": "gaphor.UML", "line_number": 115, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 117, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 121, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 124, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 124, "usage_type": "name"}, {"api_name": "gaphor.diagram.classes", "line_number": 125, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 125, "usage_type": "name"}, {"api_name": "gaphor.UML.Interface", "line_number": 126, "usage_type": "attribute"}, {"api_name": "gaphor.UML", "line_number": 126, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 129, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 133, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 136, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 136, "usage_type": "name"}, {"api_name": "gaphor.diagram.classes", "line_number": 137, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 137, "usage_type": "name"}, {"api_name": "gaphor.UML.Package", "line_number": 138, "usage_type": "attribute"}, {"api_name": "gaphor.UML", "line_number": 138, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 141, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 145, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 148, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 148, "usage_type": "name"}, {"api_name": "gaphor.diagram.classes", "line_number": 148, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 148, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 152, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 155, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 155, "usage_type": "name"}, {"api_name": "gaphor.diagram.classes", "line_number": 155, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 155, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 159, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 162, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 162, "usage_type": "name"}, {"api_name": "gaphor.diagram.classes", "line_number": 162, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 162, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 166, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 169, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 169, "usage_type": "name"}, {"api_name": "gaphor.diagram.classes", "line_number": 169, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 169, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 174, "usage_type": "call"}, {"api_name": "gaphor.core.gettext", "line_number": 178, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 181, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 181, "usage_type": "name"}, {"api_name": "gaphor.diagram.components", "line_number": 182, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 182, "usage_type": "name"}, {"api_name": "gaphor.UML.Component", "line_number": 183, "usage_type": "attribute"}, {"api_name": "gaphor.UML", "line_number": 183, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 186, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 190, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 193, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 193, "usage_type": "name"}, {"api_name": "gaphor.diagram.components", "line_number": 194, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 194, "usage_type": "name"}, {"api_name": "gaphor.UML.Artifact", "line_number": 195, "usage_type": "attribute"}, {"api_name": "gaphor.UML", "line_number": 195, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 198, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 202, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 205, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 205, "usage_type": "name"}, {"api_name": "gaphor.diagram.components", "line_number": 206, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 206, "usage_type": "name"}, {"api_name": "gaphor.UML.Node", "line_number": 206, "usage_type": "attribute"}, {"api_name": "gaphor.UML", "line_number": 206, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 208, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 212, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 215, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 215, "usage_type": "name"}, {"api_name": "gaphor.diagram.components", "line_number": 216, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 216, "usage_type": "name"}, {"api_name": "gaphor.UML.Device", "line_number": 217, "usage_type": "attribute"}, {"api_name": "gaphor.UML", "line_number": 217, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 220, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 224, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 227, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 227, "usage_type": "name"}, {"api_name": "gaphor.diagram.components", "line_number": 227, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 227, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 232, "usage_type": "call"}, {"api_name": "gaphor.core.gettext", "line_number": 236, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 239, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 239, "usage_type": "name"}, {"api_name": "gaphor.diagram.actions", "line_number": 240, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 240, "usage_type": "name"}, {"api_name": "gaphor.UML.Action", "line_number": 241, "usage_type": "attribute"}, {"api_name": "gaphor.UML", "line_number": 241, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 244, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 248, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 251, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 251, "usage_type": "name"}, {"api_name": "gaphor.diagram.actions", "line_number": 252, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 252, "usage_type": "name"}, {"api_name": "gaphor.UML.InitialNode", "line_number": 252, "usage_type": "attribute"}, {"api_name": "gaphor.UML", "line_number": 252, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 254, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 258, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 261, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 261, "usage_type": "name"}, {"api_name": "gaphor.diagram.actions", "line_number": 262, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 262, "usage_type": "name"}, {"api_name": "gaphor.UML.ActivityFinalNode", "line_number": 262, "usage_type": "attribute"}, {"api_name": "gaphor.UML", "line_number": 262, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 264, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 268, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 271, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 271, "usage_type": "name"}, {"api_name": "gaphor.diagram.actions", "line_number": 272, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 272, "usage_type": "name"}, {"api_name": "gaphor.UML.FlowFinalNode", "line_number": 272, "usage_type": "attribute"}, {"api_name": "gaphor.UML", "line_number": 272, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 274, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 278, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 281, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 281, "usage_type": "name"}, {"api_name": "gaphor.diagram.actions", "line_number": 282, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 282, "usage_type": "name"}, {"api_name": "gaphor.UML.DecisionNode", "line_number": 282, "usage_type": "attribute"}, {"api_name": "gaphor.UML", "line_number": 282, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 284, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 288, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 291, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 291, "usage_type": "name"}, {"api_name": "gaphor.diagram.actions", "line_number": 292, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 292, "usage_type": "name"}, {"api_name": "gaphor.UML.JoinNode", "line_number": 292, "usage_type": "attribute"}, {"api_name": "gaphor.UML", "line_number": 292, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 298, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 301, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 301, "usage_type": "name"}, {"api_name": "gaphor.diagram.actions", "line_number": 302, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 302, "usage_type": "name"}, {"api_name": "gaphor.UML.ObjectNode", "line_number": 303, "usage_type": "attribute"}, {"api_name": "gaphor.UML", "line_number": 303, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 306, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 310, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 313, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 313, "usage_type": "name"}, {"api_name": "gaphor.diagram.actions", "line_number": 314, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 314, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 316, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 320, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 323, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 323, "usage_type": "name"}, {"api_name": "gaphor.diagram.actions", "line_number": 323, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 323, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 327, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 330, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 330, "usage_type": "name"}, {"api_name": "gaphor.diagram.actions", "line_number": 331, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 331, "usage_type": "name"}, {"api_name": "gaphor.UML.SendSignalAction", "line_number": 332, "usage_type": "attribute"}, {"api_name": "gaphor.UML", "line_number": 332, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 335, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 339, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 342, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 342, "usage_type": "name"}, {"api_name": "gaphor.diagram.actions", "line_number": 343, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 343, "usage_type": "name"}, {"api_name": "gaphor.UML.AcceptEventAction", "line_number": 344, "usage_type": "attribute"}, {"api_name": "gaphor.UML", "line_number": 344, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 347, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 352, "usage_type": "call"}, {"api_name": "gaphor.core.gettext", "line_number": 356, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 359, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 359, "usage_type": "name"}, {"api_name": "gaphor.diagram.interactions", "line_number": 360, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 360, "usage_type": "name"}, {"api_name": "gaphor.UML.Lifeline", "line_number": 361, "usage_type": "attribute"}, {"api_name": "gaphor.UML", "line_number": 361, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 364, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 368, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 371, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 371, "usage_type": "name"}, {"api_name": "gaphor.diagram.interactions", "line_number": 372, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 372, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 377, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 380, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 380, "usage_type": "name"}, {"api_name": "gaphor.diagram.interactions", "line_number": 381, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 381, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 387, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 390, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 390, "usage_type": "name"}, {"api_name": "gaphor.diagram.interactions", "line_number": 391, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 391, "usage_type": "name"}, {"api_name": "gaphor.UML.Interaction", "line_number": 392, "usage_type": "attribute"}, {"api_name": "gaphor.UML", "line_number": 392, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 395, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 400, "usage_type": "call"}, {"api_name": "gaphor.core.gettext", "line_number": 404, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 407, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 407, "usage_type": "name"}, {"api_name": "gaphor.diagram.states", "line_number": 408, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 408, "usage_type": "name"}, {"api_name": "gaphor.UML.State", "line_number": 408, "usage_type": "attribute"}, {"api_name": "gaphor.UML", "line_number": 408, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 410, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 414, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 417, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 417, "usage_type": "name"}, {"api_name": "gaphor.diagram.states", "line_number": 418, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 418, "usage_type": "name"}, {"api_name": "gaphor.UML.Pseudostate", "line_number": 419, "usage_type": "attribute"}, {"api_name": "gaphor.UML", "line_number": 419, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 422, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 426, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 429, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 429, "usage_type": "name"}, {"api_name": "gaphor.diagram.states", "line_number": 430, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 430, "usage_type": "name"}, {"api_name": "gaphor.UML.FinalState", "line_number": 430, "usage_type": "attribute"}, {"api_name": "gaphor.UML", "line_number": 430, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 432, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 436, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 439, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 439, "usage_type": "name"}, {"api_name": "gaphor.diagram.states", "line_number": 440, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 440, "usage_type": "name"}, {"api_name": "gaphor.UML.Pseudostate", "line_number": 441, "usage_type": "attribute"}, {"api_name": "gaphor.UML", "line_number": 441, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 444, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 448, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 451, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 451, "usage_type": "name"}, {"api_name": "gaphor.diagram.states", "line_number": 452, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 452, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 458, "usage_type": "call"}, {"api_name": "gaphor.core.gettext", "line_number": 462, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 465, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 465, "usage_type": "name"}, {"api_name": "gaphor.diagram.usecases", "line_number": 466, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 466, "usage_type": "name"}, {"api_name": "gaphor.UML.UseCase", "line_number": 467, "usage_type": "attribute"}, {"api_name": "gaphor.UML", "line_number": 467, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 470, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 474, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 477, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 477, "usage_type": "name"}, {"api_name": "gaphor.diagram.usecases", "line_number": 478, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 478, "usage_type": "name"}, {"api_name": "gaphor.UML.Actor", "line_number": 478, "usage_type": "attribute"}, {"api_name": "gaphor.UML", "line_number": 478, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 480, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 484, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 487, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 487, "usage_type": "name"}, {"api_name": "gaphor.diagram.classes", "line_number": 488, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 488, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 493, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 496, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 496, "usage_type": "name"}, {"api_name": "gaphor.diagram.usecases", "line_number": 497, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 497, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 502, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 505, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 505, "usage_type": "name"}, {"api_name": "gaphor.diagram.usecases", "line_number": 506, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 506, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 512, "usage_type": "call"}, {"api_name": "gaphor.core.gettext", "line_number": 516, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 519, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 519, "usage_type": "name"}, {"api_name": "gaphor.diagram.classes", "line_number": 520, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 520, "usage_type": "name"}, {"api_name": "gaphor.UML.Profile", "line_number": 521, "usage_type": "attribute"}, {"api_name": "gaphor.UML", "line_number": 521, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 524, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 528, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 531, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 531, "usage_type": "name"}, {"api_name": "gaphor.diagram.classes", "line_number": 532, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 532, "usage_type": "name"}, {"api_name": "gaphor.UML.Class", "line_number": 532, "usage_type": "attribute"}, {"api_name": "gaphor.UML", "line_number": 532, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 534, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 538, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 541, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 541, "usage_type": "name"}, {"api_name": "gaphor.diagram.classes", "line_number": 542, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 542, "usage_type": "name"}, {"api_name": "gaphor.UML.Stereotype", "line_number": 543, "usage_type": "attribute"}, {"api_name": "gaphor.UML", "line_number": 543, "usage_type": "name"}, {"api_name": "gaphas.item.SE", "line_number": 546, "usage_type": "name"}, {"api_name": "gaphor.core.gettext", "line_number": 550, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool.new_item_factory", "line_number": 553, "usage_type": "call"}, {"api_name": "gaphor.diagram.diagramtools.PlacementTool", "line_number": 553, "usage_type": "name"}, {"api_name": "gaphor.diagram.profiles", "line_number": 554, "usage_type": "attribute"}, {"api_name": "gaphor.diagram", "line_number": 554, "usage_type": "name"}]} +{"seq_id": "513811436", "text": "\"\"\"'notice'\n\nRevision ID: d6c756737ca7\nRevises: 4ce9f0a1485a\nCreate Date: 2020-03-14 11:15:15.169459\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'd6c756737ca7'\ndown_revision = '4ce9f0a1485a'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_table('notice',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('title', sa.String(length=50), nullable=True),\n sa.Column('message', sa.Text(), nullable=True),\n sa.Column('pubtime', sa.DateTime(), nullable=True),\n sa.Column('user_type', sa.String(length=100), nullable=True),\n sa.Column('type_id', sa.Integer(), nullable=True),\n sa.PrimaryKeyConstraint('id')\n )\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_table('notice')\n # ### end Alembic commands ###\n", "sub_path": "代码/Management/migrations/versions/d6c756737ca7_notice.py", "file_name": "d6c756737ca7_notice.py", "file_ext": "py", "file_size_in_byte": 969, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "alembic.op.create_table", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.Text", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 28, "usage_type": "call"}, {"api_name": "alembic.op.drop_table", "line_number": 35, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 35, "usage_type": "name"}]} +{"seq_id": "237926978", "text": "import tkinter\nfrom tkinter import Toplevel, ttk\nfrom tkinter.ttk import Label\n\nfrom dependency_injector.wiring import Provide\n\nfrom app_tools.config_loader import ConfigLoader\nfrom app_tools.config_updater import ConfigUpdater\nfrom app_tools.os_utils import restart_program\n\nSCAN_MODES = ['barcode_scanner', 'webcam']\nSCANNER_SEPARATORS = ['enter', 'tab']\nLOG_LEVELS = ['critical', 'error', 'warning', 'info', 'debug']\n\n\nclass SettingsWindow:\n\n def __init__(self, parent, config_loader: ConfigLoader = Provide['config_loader']):\n current_scan_mode = config_loader.get_general(\"scan_mode\")\n current_word_separator = config_loader.get_barcode_scanner(\"word_separator\")\n\n self.root = Toplevel(parent)\n\n self.root.title(\"Settings\")\n\n self.root.geometry(\"300x300\")\n self.root.resizable(False, False)\n\n main_container = tkinter.Frame(self.root)\n\n scan_mode_frame = tkinter.Frame(main_container, pady=15)\n Label(scan_mode_frame, text=\"Scan mode\").pack()\n self.scan_mode_combobox = ttk.Combobox(scan_mode_frame, state=\"readonly\", values=SCAN_MODES)\n self.scan_mode_combobox.current(SCAN_MODES.index(current_scan_mode))\n self.scan_mode_combobox.pack()\n scan_mode_frame.pack()\n\n scanner_separator_frame = tkinter.Frame(main_container, pady=15)\n Label(scanner_separator_frame, text=\"Scanner word separator\").pack()\n self.scanner_separator_combobox = ttk.Combobox(scanner_separator_frame, state=\"readonly\",\n values=SCANNER_SEPARATORS)\n self.scanner_separator_combobox.current(SCANNER_SEPARATORS.index(current_word_separator))\n self.scanner_separator_combobox.pack()\n scanner_separator_frame.pack()\n\n log_levels_frame = tkinter.Frame(main_container, pady=15)\n Label(log_levels_frame, text=\"Log level\").pack()\n self.log_levels_combobox = ttk.Combobox(log_levels_frame, state=\"readonly\", values=LOG_LEVELS)\n current_log_level = ConfigUpdater.get_property('logger_root', 'level', file_path='logging.ini')\n self.log_levels_combobox.current(LOG_LEVELS.index(current_log_level.lower()))\n self.log_levels_combobox.pack()\n log_levels_frame.pack()\n\n btn_frame = tkinter.Frame(main_container, pady=15)\n save_btn = tkinter.Button(btn_frame, text=\"Save & Restart\", width=20, height=2)\n save_btn.config(command=self.on_save_clicked)\n save_btn.pack()\n btn_frame.pack()\n\n main_container.pack()\n\n def on_save_clicked(self):\n ConfigUpdater.set_values_in_property_file([\n ('general', 'scan_mode', self.scan_mode_combobox.get()),\n ('barcode_scanner', 'word_separator', self.scanner_separator_combobox.get())\n ])\n ConfigUpdater.set_values_in_property_file([\n ('logger_root', 'level', self.log_levels_combobox.get().upper()),\n ], file_path='logging.ini')\n restart_program()\n", "sub_path": "ui/settings_window.py", "file_name": "settings_window.py", "file_ext": "py", "file_size_in_byte": 2993, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "app_tools.config_loader.ConfigLoader", "line_number": 18, "usage_type": "name"}, {"api_name": "dependency_injector.wiring.Provide", "line_number": 18, "usage_type": "name"}, {"api_name": "tkinter.Toplevel", "line_number": 22, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 29, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 31, "usage_type": "call"}, {"api_name": "tkinter.ttk.Label", "line_number": 32, "usage_type": "call"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 33, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 33, "usage_type": "name"}, {"api_name": "tkinter.Frame", "line_number": 38, "usage_type": "call"}, {"api_name": "tkinter.ttk.Label", "line_number": 39, "usage_type": "call"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 40, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 40, "usage_type": "name"}, {"api_name": "tkinter.Frame", "line_number": 46, "usage_type": "call"}, {"api_name": "tkinter.ttk.Label", "line_number": 47, "usage_type": "call"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 48, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 48, "usage_type": "name"}, {"api_name": "app_tools.config_updater.ConfigUpdater.get_property", "line_number": 49, "usage_type": "call"}, {"api_name": "app_tools.config_updater.ConfigUpdater", "line_number": 49, "usage_type": "name"}, {"api_name": "tkinter.Frame", "line_number": 54, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 55, "usage_type": "call"}, {"api_name": "app_tools.config_updater.ConfigUpdater.set_values_in_property_file", "line_number": 63, "usage_type": "call"}, {"api_name": "app_tools.config_updater.ConfigUpdater", "line_number": 63, "usage_type": "name"}, {"api_name": "app_tools.config_updater.ConfigUpdater.set_values_in_property_file", "line_number": 67, "usage_type": "call"}, {"api_name": "app_tools.config_updater.ConfigUpdater", "line_number": 67, "usage_type": "name"}, {"api_name": "app_tools.os_utils.restart_program", "line_number": 70, "usage_type": "call"}]} +{"seq_id": "459142734", "text": "#Get rid of warning from gensim about the fact we're using Windows\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n#warnings.filterwarnings(action='ignore', category=UserWarning, module='gensim')\n\nimport gensim\nfrom gensim import utils\nfrom gensim.models.doc2vec import LabeledSentence\nfrom gensim.models import Doc2Vec\nfrom random import shuffle\n\n# if this assert fails, environment needs to install cpython\nassert gensim.models.doc2vec.FAST_VERSION > -1\nprint ('Gensim enviroment should be compatible...')\n\n\n# Labeled Sentence Class from the Github\nclass LabeledLineSentence(object):\n def __init__(self, sources):\n self.sources = sources\n\n flipped = {}\n\n # make sure that keys are unique\n for key, value in sources.items():\n if value not in flipped:\n flipped[value] = [key]\n else:\n raise Exception('Non-unique prefix encountered')\n\n def __iter__(self):\n for source, prefix in self.sources.items():\n with utils.smart_open(source) as fin:\n for item_no, line in enumerate(fin):\n yield LabeledSentence(utils.to_unicode(line).split(), [prefix + '_%s' % item_no])\n\n def to_array(self):\n self.sentences = []\n for source, prefix in self.sources.items():\n with utils.smart_open(source) as fin:\n for item_no, line in enumerate(fin):\n self.sentences.append(LabeledSentence(utils.to_unicode(line).split(), [prefix + '_%s' % item_no]))\n return self.sentences\n\n def sentences_perm(self):\n shuffle(self.sentences)\n return self.sentences\n\n\nin_dict = {'train_pos.txt':'train_pos','train_neg.txt':'train_neg', 'test_pos.txt':'test_pos', \\\n 'test_neg.txt':'test_neg', 'unsup.txt':'unsup'}\ndata = LabeledLineSentence(in_dict)\nprint('Data Labeled...')\n\nmodel = Doc2Vec(min_count=10, window=10, size=200, sample=1e-4, negative=5, workers=8)\nmodel.build_vocab(data.to_array())\nprint('Model Initialized...')\n\ni = 1\nfor epoch in range(20):\n print('Model Training...Epoch ', i)\n model.train(data.sentences_perm(),total_examples=model.corpus_count,epochs=model.epochs)\n print('Model Training Finished...Epoch ',i)\n i += 1\n\nmodel.save('./imdb.d2v')\n\nprint(model.most_similar('good'))\nprint(model.most_similar('terrible'))\nprint(model.most_similar('movie'))\nprint(model.most_similar('action'))\nprint(model.most_similar('think'))\nprint(model.most_similar('learn'))\nprint(model.most_similar('fight'))\nprint(model.most_similar('stiller'))\nprint(model.most_similar('00smile00'))\nprint(model.most_similar('00frown00'))", "sub_path": "src/word2vec-sentiments-repo.py", "file_name": "word2vec-sentiments-repo.py", "file_ext": "py", "file_size_in_byte": 2634, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "warnings.filterwarnings", "line_number": 3, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 13, "usage_type": "attribute"}, {"api_name": "gensim.utils.smart_open", "line_number": 33, "usage_type": "call"}, {"api_name": "gensim.utils", "line_number": 33, "usage_type": "name"}, {"api_name": "gensim.models.doc2vec.LabeledSentence", "line_number": 35, "usage_type": "call"}, {"api_name": "gensim.utils.to_unicode", "line_number": 35, "usage_type": "call"}, {"api_name": "gensim.utils", "line_number": 35, "usage_type": "name"}, {"api_name": "gensim.utils.smart_open", "line_number": 40, "usage_type": "call"}, {"api_name": "gensim.utils", "line_number": 40, "usage_type": "name"}, {"api_name": "gensim.models.doc2vec.LabeledSentence", "line_number": 42, "usage_type": "call"}, {"api_name": "gensim.utils.to_unicode", "line_number": 42, "usage_type": "call"}, {"api_name": "gensim.utils", "line_number": 42, "usage_type": "name"}, {"api_name": "random.shuffle", "line_number": 46, "usage_type": "call"}, {"api_name": "gensim.models.Doc2Vec", "line_number": 55, "usage_type": "call"}]} +{"seq_id": "281198974", "text": "import logging\n\nfrom django.conf import settings\nfrom django.core.mail import send_mail\nfrom django.core.management import BaseCommand\n\nlogger = logging.getLogger(__name__)\n\n\nclass Command(BaseCommand):\n def add_arguments(self, parser):\n parser.add_argument(\"--message\", type=str)\n parser.add_argument(\"--to\", type=str, required=True)\n\n def handle(self, *args, **options):\n message = options.get(\"message\", \"Test email from Servicing API.\")\n to_email = options.get(\"to\")\n\n send_mail(\n \"Test email\",\n message,\n settings.EMAIL_HOST_USER,\n [to_email],\n fail_silently=False,\n )\n\n logger.info(\"Email sent\")\n", "sub_path": "apps/authentication/management/commands/send_email.py", "file_name": "send_email.py", "file_ext": "py", "file_size_in_byte": 712, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 7, "usage_type": "call"}, {"api_name": "django.core.management.BaseCommand", "line_number": 10, "usage_type": "name"}, {"api_name": "django.core.mail.send_mail", "line_number": 19, "usage_type": "call"}, {"api_name": "django.conf.settings.EMAIL_HOST_USER", "line_number": 22, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 22, "usage_type": "name"}]} +{"seq_id": "55299672", "text": "from flask import Flask, escape, url_for\nimport requests\n\napp = Flask(__name__)\n\n\n@app.route('/pokemon/')\ndef pokemon_identify(entry):\n req = requests.get('https://pokeapi.co/api/v2/pokemon/{}'.format(entry))\n data = req.json()\n # return '{}\\'s profile'.format(escape(entry))\n \n # entered int\n if entry.isdigit(): \n name = data['name']\n return 'The pokemon with id {} is {}'.format(entry, name)\n # entered name\n else: \n pokemonID = data['id']\n return '{} has id {}'.format(entry, pokemonID)\n\n\nif __name__ == '__main__':\n app.run()\n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 608, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "flask.Flask", "line_number": 4, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "210405609", "text": "#!python3\nimport sys\nimport argparse\nimport requests\nimport urllib3\n\nfrom panopto_folders import PanoptoFolders\n\nfrom os.path import dirname, join, abspath\nsys.path.insert(0, abspath(join(dirname(__file__), '..', 'common')))\nfrom panopto_oauth2 import PanoptoOAuth2\n\n\ndef parse_argument():\n parser = argparse.ArgumentParser(description='Sample of Folders API')\n parser.add_argument('--server', dest='server', required=True, help='Server name as FQDN')\n parser.add_argument('--client-id', dest='client_id', required=True, help='Client ID of OAuth2 client')\n parser.add_argument('--client-secret', dest='client_secret', required=True, help='Client Secret of OAuth2 client')\n parser.add_argument('--skip-verify', dest='skip_verify', action='store_true', required=False, help='Skip SSL certificate verification. (Never apply to the production code)')\n return parser.parse_args()\n\n\ndef main():\n args = parse_argument()\n\n if args.skip_verify:\n # This line is needed to suppress annoying warning message.\n urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n\n # Use requests module's Session object in this example.\n # ref. https://2.python-requests.org/en/master/user/advanced/#session-objects\n requests_session = requests.Session()\n requests_session.verify = not args.skip_verify\n\n # Load OAuth2 logic\n oauth2 = PanoptoOAuth2(args.server, args.client_id, args.client_secret, not args.skip_verify)\n\n # Load Folders API logic\n folders = PanoptoFolders(args.server, not args.skip_verify, oauth2)\n\n current_folder_id = 'c6d51db2-b319-44ae-a190-2c5b06e926be'\n list_sessions(folders, current_folder_id)\n\n\ndef list_sessions(folders, folder_id):\n print('Sessions in the folder:')\n for entry in folders.get_sessions(folder_id):\n print(' {0}: {1}'.format(entry['Id'], entry['Name']))\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "folders-cli/list_folder_sessions.py", "file_name": "list_folder_sessions.py", "file_ext": "py", "file_size_in_byte": 1907, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sys.path.insert", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 10, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 15, "usage_type": "call"}, {"api_name": "urllib3.disable_warnings", "line_number": 28, "usage_type": "call"}, {"api_name": "urllib3.exceptions", "line_number": 28, "usage_type": "attribute"}, {"api_name": "requests.Session", "line_number": 32, "usage_type": "call"}, {"api_name": "panopto_oauth2.PanoptoOAuth2", "line_number": 36, "usage_type": "call"}, {"api_name": "panopto_folders.PanoptoFolders", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "383371486", "text": "from tqdm import tqdm\nimport numpy as np\nimport random\nimport torch\n\n\nclass Trainer:\n def __init__(self,\n environment, num_env, algorithm, num_rewards,\n optimizer, agent_tr, agent_fr, combatants,\n log_writer):\n self.environment = environment\n self.num_env = num_env\n self.algorithm = algorithm # i. e. A2C\n self.num_rewards = num_rewards # number of rewards components, usually 2\n self.available_combatants = combatants\n\n # for trainable agent:\n self.optimizer = optimizer\n self.agent_tr = agent_tr\n self.position_indices_tr = np.array([0, 1] * (num_env // 2))\n # combatant_ids will be sampled and initialized after reset, for now it is just 'fake' value\n self.combatant_ids_tr = np.full(num_env, -1, dtype=np.int32)\n self.hidden_tr = None\n self.last_action_tr = np.zeros((num_env, 12), dtype=np.float32) # no action has been performed yet\n\n # same for frozen agent:\n self.agent_fr = agent_fr\n self.position_indices_fr = np.array([1, 0] * (num_env // 2))\n self.combatant_ids_fr = np.full(num_env, -1, dtype=np.int32)\n self.hidden_fr = None\n self.last_action_fr = np.zeros((num_env, 12), dtype=np.float32)\n\n # it is bad practice to hard-code values such as observation shape or number of available actions\n self.last_observation = np.zeros((num_env, 3, 108, 60))\n self.episode_rewards = np.array([0] * num_env, dtype=np.float32)\n self.train_steps = 0\n\n self.log_writer = log_writer\n\n # reset all environments\n self._reset_environments(list(range(num_env)))\n\n @staticmethod\n def _reset_lstm_hidden(hidden, idx):\n # hidden = (h_n, c_n),\n # h_n = tensor of shape [1, batch, hidden_size],\n # c_n of the same shape\n if hidden is not None:\n h_n, c_n = hidden\n h_n[:, idx, :] *= 0\n c_n[:, idx, :] *= 0\n hidden = (h_n, c_n)\n return hidden\n\n def _reset_environments(self, ids_to_reset):\n # tested [x]\n # reset all environment once at the beginning of training\n # 2 combatants per environment\n render = False\n\n combatants = [\n random.choice(self.available_combatants)\n for _ in range(len(ids_to_reset) * 2)\n ]\n combatant_ids, combatant_names = zip(*combatants)\n env_init_names = []\n for i, idx in enumerate(ids_to_reset):\n tr_id, fr_id = self.position_indices_tr[idx], self.position_indices_fr[idx] # ok\n env_init_names.append((combatant_names[2 * i], combatant_names[2 * i + 1], render)) # ok\n self.combatant_ids_tr[idx] = combatant_ids[2 * i + tr_id]\n self.combatant_ids_fr[idx] = combatant_ids[2 * i + fr_id]\n\n reset_observation = self.environment.reset_ids(ids_to_reset, env_init_names) # shape [B, *obs_shape]\n # TODO: vectorize if possible\n for i, idx in enumerate(ids_to_reset):\n self.last_observation[idx] = reset_observation[i]\n self.last_action_tr[idx] = np.zeros(12)\n self.last_action_fr[idx] = np.zeros(12)\n self.hidden_tr = self._reset_lstm_hidden(self.hidden_tr, idx)\n self.hidden_fr = self._reset_lstm_hidden(self.hidden_fr, idx)\n\n def _act(self):\n action_tr, _, self.hidden_tr = self.agent_tr.act(\n observation=self.last_observation,\n action=self.last_action_tr,\n position=self.position_indices_tr,\n combatant_number=self.combatant_ids_tr,\n lstm_hidden=self.hidden_tr,\n greedy=False\n )\n action_fr, _, self.hidden_fr = self.agent_tr.act(\n observation=self.last_observation,\n action=self.last_action_fr,\n position=self.position_indices_fr,\n combatant_number=self.combatant_ids_fr,\n lstm_hidden=self.hidden_fr,\n greedy=True\n )\n action_env = np.zeros((self.num_env, 12 * 2), dtype=np.int)\n for i, position in enumerate(self.position_indices_tr):\n if position: # 1, i.e. right:\n left, right = action_fr[i], action_tr[i]\n else:\n left, right = action_tr[i], action_fr[i]\n action_env[i] = np.concatenate([left, right])\n return action_tr, action_env\n\n def _optimize_loss(self, loss):\n self.optimizer.zero_grad()\n loss.backward()\n self.optimizer.step()\n return loss.item()\n\n # TODO: transpose all tensors from [B, T, ...] shape to [T, B, ...], because it is more convenient\n def _gather_rollout_and_train(self, rollout_len):\n # observations, actions, rewards, done\n observations = np.zeros((self.num_env, rollout_len + 1, 3, 108, 60), dtype=np.float32)\n observations[:, 0] = self.last_observation\n actions = np.zeros((self.num_env, rollout_len + 1, 12), dtype=np.float32)\n actions[:, 0] = self.last_action_tr\n rewards_tr = np.zeros((self.num_env, rollout_len, self.num_rewards), dtype=np.float32)\n rewards_fr = np.zeros((self.num_env, rollout_len, self.num_rewards), dtype=np.float32)\n is_done = np.zeros((self.num_env, rollout_len), dtype=np.float32)\n hidden_state = self.hidden_tr # train on __starting__ hidden state\n\n for i in range(rollout_len):\n # obs, action, position, combatant_num, hidden, greedy\n action_tr, action_env = self._act()\n observation, reward, done, _ = self.environment.step(action_env)\n\n observations[:, i + 1] = observation\n actions[:, i + 1] = action_tr\n for idx in self.position_indices_tr:\n if idx == 0:\n rewards_tr[:, i] = (1.0 - done)[:, None] * reward[:, 0]\n rewards_fr[:, i] = (1.0 - done)[:, None] * reward[:, 1]\n else:\n rewards_tr[:, i] = (1.0 - done)[:, None] * reward[:, 1]\n rewards_fr[:, i] = (1.0 - done)[:, None] * reward[:, 0]\n is_done[:, i] = done\n rollout = list(map(np.array, [observations, actions, rewards_tr, is_done]))\n\n algorithm_out = self.algorithm.loss(\n rollout, self.position_indices_tr, self.combatant_ids_tr, hidden_state\n )\n self.train_steps += 1\n loss, value_loss, entropy_loss, policy_loss = algorithm_out\n loss = self._optimize_loss(loss)\n self._write_train_logs(loss, value_loss, entropy_loss, policy_loss, rewards_tr, rewards_fr)\n\n # we have env.reset_ids(...) method now\n reset_ids = [i for i, d in enumerate(done) if d]\n if len(reset_ids) > 0:\n self._reset_environments(reset_ids)\n\n def _write_train_logs(self, loss, value_loss, entropy_loss, policy_loss, rewards_tr, rewards_fr):\n self.log_writer.add_scalar('train/loss', loss, self.train_steps)\n self.log_writer.add_scalar('train/value_loss', value_loss, self.train_steps)\n self.log_writer.add_scalar('train/entropy_loss', entropy_loss, self.train_steps)\n self.log_writer.add_scalar('train/policy_loss', policy_loss, self.train_steps)\n self.log_writer.add_scalar('train/reward_train', rewards_tr.mean(), self.train_steps)\n self.log_writer.add_scalar('train/reward_frozen', rewards_fr.mean(), self.train_steps)\n # TODO: 'damage_dealt', 'hp_loosed'\n\n def _test_agent(self):\n pass\n\n def train(self, num_epochs, epoch_size, rollout_len):\n for epoch in range(num_epochs):\n progress_bar = tqdm(range(epoch_size), f'Epoch_{epoch}', total=epoch_size, ncols=80)\n for _ in progress_bar:\n self._gather_rollout_and_train(rollout_len)\n # TODO: test agent here!\n self.agent_fr.nn.load_state_dict(self.agent_tr.nn.state_dict())\n torch.save(\n {'agent': self.agent_tr.nn.state_dict()},\n self.log_writer.logdir + f'checkpoint_{epoch}.pth'\n )\n", "sub_path": "src/trainer.py", "file_name": "trainer.py", "file_ext": "py", "file_size_in_byte": 8080, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.array", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 30, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 36, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 100, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 118, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 120, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 122, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 123, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 124, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 142, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 171, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 176, "usage_type": "call"}]} +{"seq_id": "392476427", "text": "# -*- coding:utf-8 -*-\n\nimport os,django\nos.environ[\"DJANGO_SETTINGS_MODULE\"] = \"BioDesignVer.settings\"\ndjango.setup()\nfrom geneRelationship.models import Paper_Gene\nimport traceback\nimport json\n\ndef savePaperClassToDB():\n\treadFile = file('julei.json')\n\ts = json.load(readFile)\n\tpaper_classes = s['rows']\n\tfor paper_class in paper_classes:\n\t\ttry:\n\t\t\tpaper_id = str(paper_class[2][3:-4])\n\t\t\tgene_id = str(paper_class[1])\n\t\t\tpaper_gene = Paper_Gene.objects.filter(paper_id=paper_id, gene_id=gene_id).first()\n\t\t\tif paper_gene:\n\t\t\t\tpaper_gene.paper_class = paper_class[3]\n\t\t\t\tpaper_gene.save()\n\t\texcept:\n\t\t\ttraceback.print_exc()\nif __name__ == '__main__':\n\tsavePaperClassToDB()", "sub_path": "iGEM2016-BioDesigner/utils/savePaperClassToDB/savePaperClassToDB.py", "file_name": "savePaperClassToDB.py", "file_ext": "py", "file_size_in_byte": 673, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.environ", "line_number": 4, "usage_type": "attribute"}, {"api_name": "django.setup", "line_number": 5, "usage_type": "call"}, {"api_name": "json.load", "line_number": 12, "usage_type": "call"}, {"api_name": "geneRelationship.models.Paper_Gene.objects.filter", "line_number": 18, "usage_type": "call"}, {"api_name": "geneRelationship.models.Paper_Gene.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "geneRelationship.models.Paper_Gene", "line_number": 18, "usage_type": "name"}, {"api_name": "traceback.print_exc", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "166789735", "text": "# -*- coding: utf-8 -*-\n\n# Copyright 2015 Red Hat, Inc.\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, 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 voluptuous as v\n\nfrom grafana_dashboards.schema.panel.base import Base\n\n\nclass Logs(Base):\n def get_schema(self):\n\n alert_format = {\n # could enforce \"evaulator\"/\"operator\"/\"query\" on this...\n v.Required(\"conditions\"): v.All(list),\n v.Required(\"frequency\", default=\"60s\"): v.All(str),\n v.Required(\"name\"): v.All(str),\n v.Required(\"executionErrorState\", default=\"alerting\"): (\n v.Any(\"alerting\", \"keep_state\")\n ),\n v.Required(\"noDataState\", default=\"no_data\"): (\n v.Any(\"no_data\", \"alerting\", \"ok\", \"keep_state\")\n ),\n v.Optional(\"notifications\", default=[]): v.All(list),\n }\n\n yaxes_format = [\n {\n v.Optional(\"decimals\"): v.All(int),\n v.Optional(\"format\", default=\"short\"): Base.formats,\n v.Optional(\"label\", default=\"\"): v.All(str),\n v.Optional(\"logBase\", default=1): v.All(int, v.Range(min=1)),\n v.Optional(\"max\"): v.All(int),\n v.Optional(\"min\"): v.All(int),\n v.Optional(\"show\", default=True): v.All(bool),\n }\n ]\n\n legend = {\n v.Optional(\"alignAsTable\", default=False): v.All(bool),\n v.Optional(\"avg\", default=False): v.All(bool),\n v.Optional(\"current\", default=False): v.All(bool),\n v.Optional(\"max\", default=False): v.All(bool),\n v.Optional(\"min\", default=False): v.All(bool),\n v.Optional(\"rightSide\", default=False): v.All(bool),\n v.Optional(\"show\", default=False): v.All(bool),\n v.Optional(\"total\", default=False): v.All(bool),\n v.Optional(\"values\", default=False): v.All(bool),\n }\n\n null_point_modes = v.Any(\"connected\", \"null\", \"null as zero\")\n value_types = v.Any(\"individual\", \"cumulative\")\n\n tooltip = {\n v.Required(\"query_as_alias\", default=True): v.All(bool),\n v.Required(\"shared\", default=True): v.All(bool),\n v.Required(\"value_type\", default=\"cumulative\"): v.All(value_types),\n v.Optional(\"sort\"): v.Range(min=0, max=2),\n }\n\n series_override = {\n v.Required(\"alias\"): v.All(str, v.Length(min=1)),\n v.Optional(\"bars\"): v.All(bool),\n v.Optional(\"lines\"): v.All(bool),\n v.Optional(\"fill\"): v.All(int, v.Range(min=0, max=10)),\n v.Optional(\"width\"): v.All(int, v.Range(min=1, max=10)),\n v.Optional(\"nullPointMode\"): v.All(null_point_modes),\n v.Optional(\"fillBelowTo\"): v.All(str),\n v.Optional(\"steppedLine\"): v.All(bool),\n v.Optional(\"points\"): v.All(bool),\n v.Optional(\"pointsradius\"): v.All(int, v.Range(min=1, max=5)),\n v.Optional(\"stack\"): v.All(v.Any(bool, \"A\", \"B\", \"C\", \"D\")),\n v.Optional(\"color\"): v.All(str),\n v.Optional(\"yaxis\"): v.All(int, v.Range(min=1, max=2)),\n v.Optional(\"zindex\"): v.All(int, v.Range(min=-3, max=3)),\n v.Optional(\"transform\"): v.All(v.Any(\"negative-Y\")),\n v.Optional(\"legend\"): v.All(bool),\n }\n series_overrides = [series_override]\n\n logs = {\n v.Optional(\"alert\"): v.All(alert_format),\n v.Required(\"bars\", default=False): v.All(bool),\n v.Optional(\"datasource\"): v.All(str),\n v.Optional(\"decimals\"): v.All(int),\n v.Required(\"fill\", default=1): v.All(int),\n v.Optional(\"hideTimeOverride\"): v.All(bool),\n v.Optional(\"leftYAxisLabel\"): v.All(str, v.Length(min=1)),\n v.Optional(\"legend\"): v.All(legend),\n v.Required(\"lines\", default=True): v.All(bool),\n v.Required(\"linewidth\", default=2): v.All(int),\n v.Optional(\"minSpan\"): v.All(int, v.Range(min=0, max=12)),\n v.Optional(\"nullPointMode\"): v.All(null_point_modes),\n v.Required(\"percentage\", default=False): v.All(bool),\n v.Required(\"pointradius\", default=5): v.All(int),\n v.Required(\"points\", default=False): v.All(bool),\n v.Optional(\"repeat\"): v.All(str),\n v.Optional(\"rightYAxisLabel\"): v.All(str, v.Length(min=1)),\n v.Optional(\"seriesOverrides\"): v.All(series_overrides, v.Length(min=1)),\n v.Required(\"stack\", default=False): v.All(bool),\n v.Required(\"steppedLine\", default=False): v.All(bool),\n v.Required(\"targets\", default=[]): v.All(list),\n v.Optional(\"timeFrom\"): v.All(v.Match(r\"[1-9]+[0-9]*[smhdw]\")),\n v.Optional(\"timeShift\"): v.All(v.Match(r\"[1-9]+[0-9]*[smhdw]\")),\n v.Optional(\"tooltip\"): v.All(tooltip),\n v.Required(\"x-axis\", default=True): v.All(bool),\n v.Required(\"y-axis\", default=True): v.All(bool),\n v.Optional(\"y_formats\"): v.All([Base.formats], v.Length(min=2, max=2)),\n v.Optional(\"yaxes\"): v.All(yaxes_format, v.Length(min=2, max=2)),\n }\n logs.update(self.base)\n return v.Schema(logs)\n", "sub_path": "grafana_dashboards/schema/panel/logs.py", "file_name": "logs.py", "file_ext": "py", "file_size_in_byte": 5708, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "grafana_dashboards.schema.panel.base.Base", "line_number": 22, "usage_type": "name"}, {"api_name": "voluptuous.Required", "line_number": 27, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 28, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 29, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 30, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 33, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 36, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 27, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 28, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 29, "usage_type": "call"}, {"api_name": "voluptuous.Any", "line_number": 31, "usage_type": "call"}, {"api_name": "voluptuous.Any", "line_number": 34, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 36, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 41, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 42, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 43, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 44, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 45, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 46, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 47, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 41, "usage_type": "call"}, {"api_name": "grafana_dashboards.schema.panel.base.Base.formats", "line_number": 42, "usage_type": "attribute"}, {"api_name": "grafana_dashboards.schema.panel.base.Base", "line_number": 42, "usage_type": "name"}, {"api_name": "voluptuous.All", "line_number": 43, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 44, "usage_type": "call"}, {"api_name": "voluptuous.Range", "line_number": 44, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 45, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 46, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 47, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 52, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 53, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 54, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 55, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 56, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 57, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 58, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 59, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 60, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 52, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 53, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 54, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 55, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 56, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 57, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 58, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 59, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 60, "usage_type": "call"}, {"api_name": "voluptuous.Any", "line_number": 63, "usage_type": "call"}, {"api_name": "voluptuous.Any", "line_number": 64, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 67, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 68, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 69, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 70, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 67, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 68, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 69, "usage_type": "call"}, {"api_name": "voluptuous.Range", "line_number": 70, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 74, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 75, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 76, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 77, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 78, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 79, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 80, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 81, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 82, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 83, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 84, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 85, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 86, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 87, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 88, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 89, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 74, "usage_type": "call"}, {"api_name": "voluptuous.Length", "line_number": 74, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 75, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 76, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 77, "usage_type": "call"}, {"api_name": "voluptuous.Range", "line_number": 77, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 78, "usage_type": "call"}, {"api_name": "voluptuous.Range", "line_number": 78, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 79, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 80, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 81, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 82, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 83, "usage_type": "call"}, {"api_name": "voluptuous.Range", "line_number": 83, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 84, "usage_type": "call"}, {"api_name": "voluptuous.Any", "line_number": 84, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 85, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 86, "usage_type": "call"}, {"api_name": "voluptuous.Range", "line_number": 86, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 87, "usage_type": "call"}, {"api_name": "voluptuous.Range", "line_number": 87, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 88, "usage_type": "call"}, {"api_name": "voluptuous.Any", "line_number": 88, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 89, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 94, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 95, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 96, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 97, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 98, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 99, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 100, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 101, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 102, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 103, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 104, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 105, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 106, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 107, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 108, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 109, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 110, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 111, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 112, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 113, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 114, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 115, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 116, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 117, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 118, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 119, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 120, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 121, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 94, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 95, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 96, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 97, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 98, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 99, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 100, "usage_type": "call"}, {"api_name": "voluptuous.Length", "line_number": 100, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 101, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 102, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 103, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 104, "usage_type": "call"}, {"api_name": "voluptuous.Range", "line_number": 104, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 105, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 106, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 107, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 108, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 109, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 110, "usage_type": "call"}, {"api_name": "voluptuous.Length", "line_number": 110, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 111, "usage_type": "call"}, {"api_name": "voluptuous.Length", "line_number": 111, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 112, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 113, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 114, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 115, "usage_type": "call"}, {"api_name": "voluptuous.Match", "line_number": 115, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 116, "usage_type": "call"}, {"api_name": "voluptuous.Match", "line_number": 116, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 117, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 118, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 119, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 120, "usage_type": "call"}, {"api_name": "grafana_dashboards.schema.panel.base.Base.formats", "line_number": 120, "usage_type": "attribute"}, {"api_name": "grafana_dashboards.schema.panel.base.Base", "line_number": 120, "usage_type": "name"}, {"api_name": "voluptuous.Length", "line_number": 120, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 121, "usage_type": "call"}, {"api_name": "voluptuous.Length", "line_number": 121, "usage_type": "call"}, {"api_name": "voluptuous.Schema", "line_number": 124, "usage_type": "call"}]} +{"seq_id": "253648154", "text": "import random\n\nfrom faker import Faker\nfrom faker.providers import BaseProvider\n\nfake = Faker()\n\n\nclass FakeProvider(BaseProvider):\n @staticmethod\n def username_kaffa():\n return fake.first_name() + str(fake.password(length=9, digits=True))\n\n @staticmethod\n def product_name():\n return FakeProvider.grocery() + str(fake.password(length=9, digits=True))\n\n @staticmethod\n def grocery():\n groceries = [\n 'Café expresso',\n 'Capuccino',\n 'Irish coffee',\n 'Caffè latte',\n 'Macchiato',\n 'Mocha',\n 'Duplo',\n 'Coca-cola',\n 'Chocolate 50% Cacau',\n 'Croissant',\n 'Biscoito de nata',\n 'Torta holandesa',\n 'Folhado de frango',\n 'Waffle',\n 'Pão de queijo',\n 'Bolo de fubá',\n ]\n\n return random.choice(groceries)\n\n @staticmethod\n def payment_method():\n payment_methods = [\n 'Cartão crédito',\n 'Cartão débito',\n 'Dinheiro',\n ]\n\n return random.choice(payment_methods)\n", "sub_path": "app/configs/fake_generator.py", "file_name": "fake_generator.py", "file_ext": "py", "file_size_in_byte": 1146, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "faker.Faker", "line_number": 6, "usage_type": "call"}, {"api_name": "faker.providers.BaseProvider", "line_number": 9, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 39, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "286220470", "text": "# Copyright 2015-2017 Applatix, Inc. All rights reserved.\n\nimport logging\nimport requests\nimport time\nfrom concurrent.futures import ThreadPoolExecutor, as_completed\n\nlogger = logging.getLogger(__name__)\nlogging.getLogger('requests.packages.urllib3.connectionpool').setLevel(logging.ERROR)\n\n\ndef test_get_branches(gateway, concurrency, max_request):\n \"\"\"Stress test against branch API on gateway\n\n :param gateway:\n :param concurrency:\n :param max_request:\n :returns:\n \"\"\"\n\n def get_branches(sn):\n \"\"\"Get branches\n\n :param sn:\n :returns:\n \"\"\"\n logger.info('Retrieving branches (sn: %s) ...', sn)\n start_time = time.time()\n try:\n resp = requests.get('{}/v1/scm/branches'.format(gateway))\n except Exception as e:\n logger.error('Failed to retrieve branches (sn: %s): %s', sn, str(e))\n else:\n branches = resp.json()['data']\n end_time = time.time()\n logger.info('Successfully retrieved %s branches (sn: %s) in %s seconds', len(branches), sn, end_time - start_time)\n return branches\n\n start_time = time.time()\n\n count = 0\n with ThreadPoolExecutor(max_workers=concurrency) as executor:\n futures = []\n for i in range(max_request):\n futures.append(executor.submit(get_branches, i))\n for future in as_completed(futures):\n if future.result():\n count += 1\n\n end_time = time.time()\n logger.info('Totally spent %s seconds to process %s requests', end_time - start_time, count)\n", "sub_path": "devops/test/ax/test/devops/e2e/gateway/test_gateway.py", "file_name": "test_gateway.py", "file_ext": "py", "file_size_in_byte": 1586, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "logging.ERROR", "line_number": 9, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 28, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 30, "usage_type": "call"}, {"api_name": "time.time", "line_number": 35, "usage_type": "call"}, {"api_name": "time.time", "line_number": 39, "usage_type": "call"}, {"api_name": "concurrent.futures.ThreadPoolExecutor", "line_number": 42, "usage_type": "call"}, {"api_name": "concurrent.futures.as_completed", "line_number": 46, "usage_type": "call"}, {"api_name": "time.time", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "94789244", "text": "# download_data.py\nimport requests\nfrom datetime import datetime, timedelta\nimport pytz\n\n\nclass TemtopApi:\n readings = [ 'HCHO', 'PM2.5', 'TVOC', 'AQI' ]\n\n def __init__(self, username, password):\n # do login\n self.headers = {\n 'User-Agent': 'okhttp/2.7.5',\n }\n r = requests.get(\n f'http://www.i-elitech.com//apiLoginAction.do?method=login&password={password}&username={username}',\n headers=self.headers,\n )\n resp = r.json()\n if not resp['success']:\n raise RuntimeError('login failed')\n self.userId = resp['user']['id']\n self.token = resp['token']\n self.timezone = pytz.timezone(\"America/Chicago\")\n\n\n def get(self, url: str, params: None):\n headers = {\n 'User-Agent': 'okhttp/2.7.5',\n 'JSESSIONID': self.token,\n }\n cookies = { 'JSESSIONID': self.token }\n r = requests.get(url, headers=headers, cookies=cookies, params=params)\n resp = r.json()\n if not resp['success']:\n print(f'request failed, response={resp}')\n raise RuntimeError('request failed')\n return resp\n\n def getDeviceList(self):\n params={\n 'method': 'getList',\n 'typeList': '0',\n 'userId': self.userId,\n }\n return self.get('http://www.i-elitech.com//apiDeviceAction.do', params=params)['rows']\n \n def getFirstDeviceId(self) -> int:\n return self.getDeviceList()[0]['id']\n\n def getDeviceData(self, deviceId: int, startDateTime: datetime, endDateTime: datetime):\n params = {\n 'method' : 'getList',\n 'page' : '1',\n 'rows' : '4500',\n 'startDate' : startDateTime.replace(tzinfo=None).isoformat(sep=' ', timespec='seconds'),\n 'endDate' : endDateTime.replace(tzinfo=None).isoformat(sep=' ', timespec='seconds'),\n 'deviceId' : deviceId,\n }\n return self.get('http://www.i-elitech.com//apiDeviceDataAction.do', params=params)['rows']\n \n def getM10iDeviceData(self, deviceId: int, startDateTime: datetime, endDateTime: datetime):\n data = self.getDeviceData(deviceId, startDateTime, endDateTime)\n \n def parseDate(createTime) -> datetime:\n d = datetime(\n day = createTime['date'],\n hour = createTime['hours'],\n minute = createTime['minutes'],\n month = createTime['month']+1,\n second = createTime['seconds'],\n year = createTime['year']+1900,\n tzinfo = None,\n )\n return self.timezone.localize(d)\n return [\n {\n # 'originaldatetime': d['createTime'],\n 'datetime' : parseDate(d['createTime']),\n 'HCHO' : float(d['probe1']),\n 'PM2.5' : float(d['probe2']),\n 'TVOC' : float(d['probe3']),\n 'AQI' : float(d['probe4']),\n }\n for d in data\n ]\n\n\nif __name__ == '__main__':\n import os\n\n username = os.environ.get('TEMPTOP_USER')\n password = os.environ.get('TEMPTOP_PASS')\n api = TemtopApi(username, password)\n\n deviceId = api.getFirstDeviceId()\n print(f'device id: {deviceId}')\n\n now = datetime.now()\n midnight = now.replace(hour=0, minute=0, second=0)\n # end 5 minutes in the future, just in case there is some time skew\n now += timedelta(minutes=5)\n deviceData = api.getM10iDeviceData(\n deviceId=deviceId,\n startDateTime=midnight,\n endDateTime=now,\n )\n print('data for today:')\n for d in deviceData:\n print(d)\n\n for reading in [ 'HCHO', 'PM2.5', 'TVOC', 'AQI' ]:\n print(f'min {reading}: {min(deviceData, key=lambda x: x[reading])}')\n\n", "sub_path": "temtop-elitech-cloud-bridge/temtop_api.py", "file_name": "temtop_api.py", "file_ext": "py", "file_size_in_byte": 3875, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "requests.get", "line_number": 15, "usage_type": "call"}, {"api_name": "pytz.timezone", "line_number": 24, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 33, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 51, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 62, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 66, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 65, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 92, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 92, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 93, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 93, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 99, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 99, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 102, "usage_type": "call"}]} +{"seq_id": "607109734", "text": "from flask import Flask\nfrom flask_babel import Babel\n\ntry:\n import urllib.parse\n quote = urllib.parse.quote\nexcept:\n import urllib\n quote = urllib.quote_plus\n\nfrom config import constants\n\nclass MyFlask(Flask):\n def get_send_file_max_age(self, name):\n if name.startswith('js/') or name.startswith('css/'):\n return 0\n return super(MyFlask, self).get_send_file_max_age(name)\n\napp = MyFlask(__name__,\n template_folder=constants.TEMPLATE_ROOT,\n static_folder=constants.STATIC_ROOT)\n\n# `.encode('utf8')` will not be needed for python 3\napp.jinja_env.filters['quote_plus'] = lambda u: quote(u.encode('utf8'))\n", "sub_path": "app/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 650, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "urllib.parse.parse", "line_number": 6, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 6, "usage_type": "name"}, {"api_name": "urllib.quote_plus", "line_number": 9, "usage_type": "attribute"}, {"api_name": "flask.Flask", "line_number": 13, "usage_type": "name"}, {"api_name": "config.constants.TEMPLATE_ROOT", "line_number": 20, "usage_type": "attribute"}, {"api_name": "config.constants", "line_number": 20, "usage_type": "name"}, {"api_name": "config.constants.STATIC_ROOT", "line_number": 21, "usage_type": "attribute"}, {"api_name": "config.constants", "line_number": 21, "usage_type": "name"}]} +{"seq_id": "343696607", "text": "import matplotlib.pyplot as plt\nfrom matplotlib.dates import DateFormatter, WeekdayLocator,\\\n DayLocator, MONDAY\nfrom matplotlib.finance import quotes_historical_yahoo_ohlc, candlestick_ohlc\nimport helper\nimport os\n\n#dataType Index of Doji [Int]\ndef show_result(quotes, ax, data, name):\n\tfor index in data:\n\t\tax.text(quotes[index][0], quotes[index][1], name)\n\n#dataType Tuple of Doji Array ([Int],[Int])\ndef show_test_result(quotes, ax, data, name):\n\tfor index in data[0]:\n\t\tax.text(quotes[index][0], quotes[index][1], name, color='blue')\n\tfor index in data[1]:\n\t\tax.text(quotes[index][0], quotes[index][1], name, color='green')\n\n# for additional_function pass in a function above\ndef draw_one_day_candle_stick(stock_id = \"AAPL\", name = \"\", additional_function = None):\n\n\tquotes = helper.get_today_quote(stock_id)[5]\n\n\t#(?,open,high,low,close,vol)\n\n\tif len(quotes) == 0:\n\t raise SystemExit\n\n\tfig = plt.figure()\n\tax = fig.add_subplot(111)\n\tfig.subplots_adjust(bottom=0.2)\n\n\tif additional_function != None:\n\t\tadditional_function(quotes, ax, data, name)\n\n\tcandlestick_ohlc(ax, quotes, width=0.0001)\n\n\tax.xaxis_date()\n\tax.autoscale_view()\n\tplt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')\n\tplt.title(stock_id)\n\n\tfig.set_size_inches(27, 10)\n\timage_name = stock_id + \".png\"\n\n\tplt.show()\n\n\n# for additional_function pass in a function above\ndef draw_candle_stick_with_today(stock_id, start_date, end_date, quotes, additional_function = None, data = None, name = \"\"):\n\n\tmondays = WeekdayLocator(MONDAY) # major ticks on the mondays\n\talldays = DayLocator() # minor ticks on the days\n\tweekFormatter = DateFormatter('%b %d') # e.g., Jan 12\n\tdayFormatter = DateFormatter('%d') # e.g., 12\n\n\t#(?,open,high,low,close,vol)\n\n\tif len(quotes) == 0:\n\t raise SystemExit\n\n\tfig, ax = plt.subplots()\n\tfig.subplots_adjust(bottom=0.2)\n\tax.xaxis.set_major_locator(mondays)\n\tax.xaxis.set_minor_locator(alldays)\n\tax.xaxis.set_major_formatter(weekFormatter)\n\n\tif additional_function != None:\n\t\tadditional_function(quotes, ax, data, name)\n\tcandlestick_ohlc(ax, quotes, width=0.6)\n\n\tax.xaxis_date()\n\tax.autoscale_view()\n\tplt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')\n\tplt.title(stock_id)\n\n\tif not os.path.exists('image_data'):\n\t\tos.makedirs('image_data')\n\n\tfig.set_size_inches(20, 8)\n\timage_name = stock_id + \".png\"\n\tfig.savefig(os.path.join(\"image_data\",image_name), dpi=300) # save the figure to file\n\tplt.close(fig)", "sub_path": "realtime_checker/drawCandle.py", "file_name": "drawCandle.py", "file_ext": "py", "file_size_in_byte": 2499, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "helper.get_today_quote", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.finance.candlestick_ohlc", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.dates.WeekdayLocator", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.dates.MONDAY", "line_number": 53, "usage_type": "argument"}, {"api_name": "matplotlib.dates.DayLocator", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.dates.DateFormatter", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.dates.DateFormatter", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.finance.candlestick_ohlc", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.close", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}]} +{"seq_id": "185680913", "text": "import imageio\nimport numpy\nimport statistics\n\nimg = imageio.imread(\"carry_grant.jpg\")\n\nimg_shape = img.shape\ny = img_shape[0]\nx = img_shape[1]\n\nimg.sort()\nimg_list = []\n\nfor i in range(y):\n\tfor j in range(x):\n\t\timg_list.append(str(img[i][j]))\n\n\n########### 평균값 계산 ###########\nsum_ = 0\ncount_ = 0\n\nfor i in range(y):\n\tfor j in range(x):\n\t\tsum_ += img[i][j]\n\t\tcount_ += 1\nmean = sum_/count_\nprint('mean pixel value = ',mean)\n\n\n########### 분산값 계산 ###########\nvar_ = 0\ncount_a = 0\n\nfor i in range(y):\n\tfor j in range(x):\n\t\tvar_ += (int(img[i][j]) - mean)**2\n\t\tcount_a += 1\n\nvariance = var_/count_a\nprint('variance = ',variance)\n\n\n########### 최대값 계산 ###########\nmax_ = 0\n\nfor i in range(y):\n\tfor j in range(x):\n\t\tif max_ < int(img[i][j]):\n\t\t\tmax_ = int(img[i][j])\n\nprint('max = ',max_)\n\n\n########### 최소값 계산 ###########\nmin_ = 255\n\nfor i in range(y):\n\tfor j in range(x):\n\t\tif min_ > int(img[i][j]):\n\t\t\tmin_ = int(img[i][j])\n\nprint('min = ',min_)\n\n\n########### 중간값 계산 ###########\nlist_1 = []\nlist_2 = []\nlist_3 = []\n\nfor i in range(len(img_list)):\n\n\tif int(img_list[i]) < 10:\n\t\tlist_1.append(img_list[i])\n\n\telif int(img_list[i]) >= 10 and int(img_list[i]) < 100:\n\t\tlist_2.append(str(img_list[i]))\n\n\telif int(img_list[i]) >= 100 and int(img_list[i]) < 257:\n\t\tlist_3.append(img_list[i])\n\nlist_1.sort()\nlist_2.sort()\nlist_3.sort()\n\nsorted_list = []\nsorted_list.extend(list_1)\nsorted_list.extend(list_2)\nsorted_list.extend(list_3)\n\nn = len(sorted_list)\nmedian = 0\nmid = n//2\nif n%2 == 1:\n\tmedian = sorted_list[mid]\nelse:\n\tlo = mid - 1\n\thi = mid\n\tmedian = (int(sorted_list[lo]) + int(sorted_list[hi])) / 2\n\nprint('median = ',median)\n\n\n\n\n\n\n\n\n", "sub_path": "Python/2019_W6_3-2.py", "file_name": "2019_W6_3-2.py", "file_ext": "py", "file_size_in_byte": 1680, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "imageio.imread", "line_number": 5, "usage_type": "call"}]} +{"seq_id": "299758866", "text": "'''\nAuthor: Kevin Delaney\nDate: 12/27/17\n'''\n\nimport requests\nimport requests.auth\nimport time\nimport re\n\nUSERNAME = ''\nPASSWORD = ''\nCLIENT_ID = ''\nSECRET = ''\ncourses = ['161','162', '165', '225', '261', '271', '290', '325', '340', '344','352', '361', '362', '372', '373',\n '464', '467', '475', '496', '575']\n\n# Getting the token\nclient_auth = requests.auth.HTTPBasicAuth(CLIENT_ID, SECRET)\npost_data = {\"grant_type\": \"password\", \"username\": USERNAME, \"password\": PASSWORD}\nheaders = {\"User-Agent\": \"Basic Python Bot with Requests library by CS290Bot\"}\nresponse = requests.post(\"https://www.reddit.com/api/v1/access_token\", auth=client_auth, data=post_data, headers=headers)\ntoken = response.json()['access_token']\n\n# Testing the token\ntime.sleep(1)\nheaders = {\"Authorization\": \"bearer \" + token, \"User-Agent\": \"Basic Python Bot with Requests library by CS290Bot\"}\nresponse = requests.get(\"https://oauth.reddit.com/api/v1/me\", headers=headers)\n\n# Get 20 hottest posts in /r/OSUOnlineCS\ntime.sleep(1)\npayload = {'limit': 20}\nresponse = requests.get('https://oauth.reddit.com/r/OSUOnlineCS/hot', headers=headers, params=payload)\ntitles = [link['data']['title'] for link in response.json()['data']['children']]\nnames = [link['data']['name'] for link in response.json()['data']['children']]\n\n# Get 20 more (for science)\ntime.sleep(1)\npayload['after'] = response.json()['data']['after']\npayload['count'] = 20\nresponse = requests.get('https://oauth.reddit.com/r/OSUOnlineCS/hot', headers=headers, params=payload)\ntitles = [link['data']['title'] for link in response.json()['data']['children']]\n\n\n# Iterate over each link\nfor name in names:\n # Get all comments\n time.sleep(1)\n payload = {'showedits': 'false', 'showmore': 'false', 'threaded': 'false'}\n response = requests.get('https://oauth.reddit.com/r/OSUOnlineCS/comments/' + name[-6:10], headers=headers, params=payload)\n comments = [comment['data']['body'] for comment in response.json()[1]['data']['children']]\n\n # Parse course numbers\n numbers = re.findall(r'[1-5][0-9][0-9]', ''.join(comments))\n coursenums = [coursenum for coursenum in courses if coursenum in numbers]\n\n # Post comment\n if len(coursenums) > 0:\n time.sleep(1)\n links = map(lambda x: '[CS {0}](http://ecampus.oregonstate.edu/soc/ecatalog/ecoursedetail.htm?'\n 'subject=CS&coursenumber={0}&termcode=all)'.format(x), coursenums)\n text = 'Courses mentioned in this thread: ' + ', '.join(links)\n payload = {'api_type':'json', 'text': text, 'thing_id': name}\n response = requests.post('https://oauth.reddit.com/api/comment', headers=headers, params=payload)\n", "sub_path": "bot.py", "file_name": "bot.py", "file_ext": "py", "file_size_in_byte": 2676, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "requests.auth.HTTPBasicAuth", "line_number": 19, "usage_type": "call"}, {"api_name": "requests.auth", "line_number": 19, "usage_type": "attribute"}, {"api_name": "requests.post", "line_number": 22, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 26, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 28, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 31, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 33, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 38, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 41, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 48, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 50, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 54, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 59, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 64, "usage_type": "call"}]} +{"seq_id": "442460891", "text": "import numpy as np\nimport pandas as pd\nimport streamlit as st\n\n \n\"\"\"app allows user to add or delete test data\"\"\"\n\n\ndef app():\n\tst.write('#')\n\tst.markdown(' ### Edit or replace test data')\n\tst.write('#')\n\t\n\n\t#Load training data\n\tdef load_test():\n\t\ttest= pd.read_csv('./apps/test_ais.csv',usecols=['sentences','labels'])\n\t\treturn test\n \n \n\t#Update with new sentence and label function \n\tdef enter_sentence_category(): \n\t\tnew_data=[] \n\t\tnew={} \n\t\t#need two text fields \n\t\tnew['sentences']=st.text_input(\"Enter sentence for test data set: either something that is bad or innocent\")\n\t\tnew['labels']=st.number_input('Enter label: 1 for sentence of interest and 0 for sentence not of interest', min_value=0, max_value=1)\n\t\tnew_data.append(new) \n\t\treturn new_data\n\n\t#Add individual sentence and label \t\n\tdef new_sentence():\n\t\tadd_sentence= st.checkbox('Add a new sentence and label to the test dataset')\n\t\tif add_sentence:\n\t\t#create new data frame, align columns with input data and write in append mode to csv\n\t\t\ttest_1= pd.DataFrame(columns={'labels','sentences'})\n\t\t\ttest_1=test_1.loc[:,['sentences','labels']]\n\t\t\t#call update function and append results to test csv\n\t\t\ttest_1= test_1.append(enter_sentence_category(),ignore_index=True) \n\t\t\tst.write('#')\n\t\t\tadd_data= st.button(\"Update test data \")\n\t\t\tif add_data:\n\t\t\t\tst.write(\":thumbsup:\")\n\t\t\t\treturn test_1.to_csv('./apps/test_ais.csv', mode='a', header=False,index=False)\n\n\t#Add/upload csv file \n\tdef replace_file():\t \n\t\tadd_file=st.checkbox('Replace current test data csv file with new one ')\n\t\tif add_file:\n\t\t\tst.write('Uploaded file needs just two columns: sentences and labels ')\t\t\t \n\t\t\tdataset = st.file_uploader(\"Upload file here\", type = ['csv'])\n\t\t\tif dataset is not None:\n\t\t\t\ttest_2 = pd.read_csv(dataset,usecols=['sentences','labels'])\n\t\t\t\tst.write('Displying top of the uploaded csv file')\n\t\t\t\tst.dataframe(test_2.head(5))\n\t\t\t\tst.write(\":thumbsup:\")\n\t\t\t\treturn test_2.to_csv('./apps/test_ais.csv', header=True,index=False)\n\t\n\t#Clear out and sentences with label = 1\n\tdef delete_all_ones():\t\t \t \n\t\tdelete_ones=st.checkbox('Delete all test sentences that were previously of interest')\n\t\tif delete_ones:\n\t\t\ttest_3= test[test.labels == 0]\t\t\t \n\t\t\tst.write(\":thumbsup:\")\n\t\t\treturn test_3.to_csv('./apps/test_ais.csv', header=True,index=False)\n\n\n\t#Clear out all sentencs with label = 0\n\tdef delete_all_zeros():\t \n\t\tdelete_zeros=st.checkbox('Delete all test data that was not previously of interest')\n\t\tif delete_zeros:\n\t\t\ttest_4= test[test.labels == 1]\t\t\t \n\t\t\tst.write(\":thumbsup:\")\n\t\t\treturn test_4.to_csv('./apps/test_ais.csv', header=True,index=False)\n\n\t#Delete all data and retur empty datadrame with twoo columns: sentences and label\n\tdef delete_all_data():\t\t \n\t\tdelete_everything=st.checkbox('Delete all test data and create new empty dataset')\n\t\tif delete_everything:\n\t\t\ttest_5= pd.DataFrame(columns=(['sentences','labels']))\t\t\t \n\t\t\tst.write(\":thumbsup:\")\n\t\t\treturn test_5.to_csv('./apps/test_ais.csv', header=True,index=False)\n\n\tdef check_test_data():\n\t\tcheck_test_data=st.checkbox(' Check latest test dataset')\n\t\tif check_test_data:\n\t\t\ttst= pd.read_csv('./apps/test_ais.csv')\n\t\t\tst.write(tst)\n\n\t\t \n\t#Main block of code\t \n\ttest=load_test()\t\n\tnew_sentence()\n\treplace_file()\n\tdelete_all_ones()\n\tdelete_all_zeros()\n\tdelete_all_data()\n\tcheck_test_data()\n\t \n\t ", "sub_path": "apps/ais_update_test_data.py", "file_name": "ais_update_test_data.py", "file_ext": "py", "file_size_in_byte": 3329, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "streamlit.write", "line_number": 10, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 11, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 17, "usage_type": "call"}, {"api_name": "streamlit.text_input", "line_number": 26, "usage_type": "call"}, {"api_name": "streamlit.number_input", "line_number": 27, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 33, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 36, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 40, "usage_type": "call"}, {"api_name": "streamlit.button", "line_number": 41, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 43, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 48, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 50, "usage_type": "call"}, {"api_name": "streamlit.file_uploader", "line_number": 51, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 53, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 54, "usage_type": "call"}, {"api_name": "streamlit.dataframe", "line_number": 55, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 56, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 61, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 64, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 70, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 73, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 78, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 80, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 81, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 85, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 87, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 88, "usage_type": "call"}]} +{"seq_id": "280032337", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Aug 7 15:11:02 2020\n\n@author: Tanvy\n\"\"\"\n\n\nimport spacy\nfrom spacy import displacy\nnlp = spacy.load(\"en_core_web_sm\")\n\ndoc = \"Hello Everyone, my name is Tanvir Duggal\"\n\n# ------------- Tokenization text\nfor token in nlp(doc):\n print(token.text)\n \n# ------------ Complex tokenization with punctuations\ndoc2 = \"You can contact me on 437-772-4737, or email @ duggaltanvir@gmail.com\"\n\nfor token in nlp(doc2):\n print(token.text)\n \n# -------------- Named entity for text\ndoc3 = \"Apple will invest $8 million in India\"\n\nfor entity in nlp(doc3).ents:\n print(entity)\n print(entity.label_)\n print(\"\")\n \n# ------------- Visualizing text\n \ndoc4 = nlp(\"Apple if going to buy ABC for $9 million\")\n\ndisplacy.serve(doc4, style='dep')\n\ndisplacy.serve(doc4, style=\"ent\")", "sub_path": "Udemy_1/Spacy/2_Tokenization.py", "file_name": "2_Tokenization.py", "file_ext": "py", "file_size_in_byte": 822, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "spacy.load", "line_number": 11, "usage_type": "call"}, {"api_name": "spacy.displacy.serve", "line_number": 37, "usage_type": "call"}, {"api_name": "spacy.displacy", "line_number": 37, "usage_type": "name"}, {"api_name": "spacy.displacy.serve", "line_number": 39, "usage_type": "call"}, {"api_name": "spacy.displacy", "line_number": 39, "usage_type": "name"}]} +{"seq_id": "333187361", "text": "__author__ = 'Joe'\n\n\"\"\"\nProject model\n\"\"\"\n\nfrom PyQt5.QtCore import Qt\nfrom PyQt5.QtSql import QSqlTableModel\n\nNFLDS = 8\nID, NICKNAME, START_DATE, END_DATE, SALARY_CAP, TOTAL_SALARY, NAME, DESCRIPTION = range(NFLDS)\n\n\nclass ProjectModel(QSqlTableModel):\n\n def __init__(self, parent, db):\n super(ProjectModel, self).__init__(parent, db)\n self.db = self.database()\n self.setHeaderData(ID, Qt.Horizontal, 'ID')\n self.setHeaderData(NAME, Qt.Horizontal, 'Name')\n self.setHeaderData(NICKNAME, Qt.Horizontal, 'Nickname')\n self.setHeaderData(START_DATE, Qt.Horizontal, 'Start Date')\n self.setHeaderData(END_DATE, Qt.Horizontal, 'End Date')\n self.setHeaderData(SALARY_CAP, Qt.Horizontal, 'Salary Cap')\n self.setHeaderData(TOTAL_SALARY, Qt.Horizontal, 'Total Salary')\n self.setHeaderData(DESCRIPTION, Qt.Horizontal, 'Description')\n self.get_all()\n\n def __str__(self):\n return self.nickname\n\n def get_all(self):\n if not self.db.open():\n raise Exception('Unable to open DB: ' + self.db.lastError().text())\n self.setTable('projects')\n if not self.select():\n raise Exception('Error getting projects: ' + self.lastError().text())\n self.sort(NICKNAME, Qt.AscendingOrder)\n\n @staticmethod\n def get_hidden_columns():\n return [ID, DESCRIPTION]\n\n\n\"\"\"\nProject View\n\"\"\"\n\nfrom PyQt5.QtWidgets import QWidget\nfrom .project_widget import Ui_ProjectsForm\n\n\nclass ProjectView(QWidget):\n\n def __init__(self):\n super(ProjectView, self).__init__()\n self.ui = Ui_ProjectsForm()\n self.ui.setupUi(self)\n self.model = None\n self.ui.tbl_projects.setItemDelegate(ProjectDelegate(self))\n self.ui.tbl_projects.setSortingEnabled(True)\n self.ui.tbl_projects.resizeColumnsToContents()\n\n def setModel(self, model):\n self.model = model\n self.ui.tbl_projects.setModel(model)\n\n def show(self):\n for colnum in self.model.get_hidden_columns():\n self.ui.tbl_projects.hideColumn(colnum)\n super().show()\n\n\n\"\"\"\nProject Delegate\n\"\"\"\n\nfrom datetime import date\nfrom PyQt5.QtWidgets import QStyledItemDelegate, QDateEdit\n\n\nclass ProjectDelegate(QStyledItemDelegate):\n\n def __init__(self, parent=None):\n super(ProjectDelegate, self).__init__(parent)\n\n def paint(self, painter, option, index):\n # if index.column() in [START_DATE, END_DATE]:\n # calendar = QCalendarWidget()\n # painter.save()\n # calendar.\n QStyledItemDelegate.paint(self, painter, option, index)\n\n def sizeHint(self, option, index):\n return QStyledItemDelegate.sizeHint(self, option, index)\n\n def createEditor(self, parent, option, index):\n if index.column() in [START_DATE, END_DATE]:\n calendar = QDateEdit(parent)\n calendar.setDate(date.today())\n calendar.setCalendarPopup(True)\n return calendar\n return QStyledItemDelegate.createEditor(self, parent, option, index)\n\n def commitAndCloseEditor(self):\n pass\n # editor = self.sender(str, object)\n # signal = pyqtSignal()\n # if isinstance(editor, (QTextEdit, QLineEdit)):\n # signal.emit(\"commitData(QWidget*)\", editor)\n # signal.emit(\"closeEditor(QWidget*)\", editor)\n\n def setEditorData(self, editor, index):\n text = index.model().data(index, Qt.DisplayRole)\n # if not index.column() in [START_DATE, END_DATE]:\n # editor.setText(text)\n\n def setModelData(self, editor, model, index):\n if index.column() in [START_DATE, END_DATE]:\n model.setData(index, editor.date())\n else:\n model.setData(index, editor.text())", "sub_path": "department/projects/project_mvd.py", "file_name": "project_mvd.py", "file_ext": "py", "file_size_in_byte": 3770, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "PyQt5.QtSql.QSqlTableModel", "line_number": 14, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.Horizontal", "line_number": 19, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 19, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.Horizontal", "line_number": 20, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 20, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.Horizontal", "line_number": 21, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 21, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.Horizontal", "line_number": 22, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 22, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.Horizontal", "line_number": 23, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 23, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.Horizontal", "line_number": 24, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 24, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.Horizontal", "line_number": 25, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 25, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.Horizontal", "line_number": 26, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 26, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AscendingOrder", "line_number": 38, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 38, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 53, "usage_type": "name"}, {"api_name": "project_widget.Ui_ProjectsForm", "line_number": 57, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QStyledItemDelegate", "line_number": 82, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QStyledItemDelegate.paint", "line_number": 92, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QStyledItemDelegate", "line_number": 92, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QStyledItemDelegate.sizeHint", "line_number": 95, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QStyledItemDelegate", "line_number": 95, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QDateEdit", "line_number": 99, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 100, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 100, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QStyledItemDelegate.createEditor", "line_number": 103, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QStyledItemDelegate", "line_number": 103, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.DisplayRole", "line_number": 114, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 114, "usage_type": "name"}]} +{"seq_id": "65484148", "text": "\nfrom django.urls import path\n\nfrom . import views\n\nurlpatterns = [\n path('sellerhome/',views.seller_home, name='sellerhome'),\n path('customer_to_seller_home/', views.customer_to_seller_home, name='customer_to_seller_home'),\n\n path('addproduct/', views.add_product_view, name='addproduct'),\n\n path('createlaptop/', views.add_laptop, name='createlaptop'),\n path('cfl/', views.create_fake_laptop, name='cfl'),\n path('updatelaptop/', views.update_laptop, name='updatelaptop'),\n path('deletelaptop/', views.delete_laptop, name='deletelaptop'),\n\n path('createmobile/', views.add_mobile, name='createmobile'),\n path('cfm/', views.create_fake_mobile, name='cfm'),\n path('updatemobile/', views.update_mobile, name='updatemobile'),\n path('deletemobile/', views.delete_mobile, name='deletemobile'),\n\n path('creategrocery/', views.add_grocery, name='creategrocery'),\n path('cfg/', views.create_fake_grocery, name='cfg'),\n path('updategrocery/', views.update_grocery, name='updategrocery'),\n path('deletegrocery/', views.delete_grocery, name='deletegrocery'),\n\n path('showallproducts/', views.show_all_products, name='showallproducts'),\n\n]", "sub_path": "EcommerceProject/Seller/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1223, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "67549097", "text": "from flask import Flask,render_template\napp = Flask(__name__)\n# @app.route('/printA')\ndef printA():\n print(\"a\")\n# import requests\n# url = \"http://127.0.0.1:5000/printA\"\n# requests.post(url)\n\n\n@app.route('/change', methods=['GET'])\ndef change():\n if request.method == 'GET':\n print(1)\n message = {'greeting':'Hello from Flask!'}\n return printA()\n # return jsonify(message) # serialize and use JSON headers\n # POST request\n if request.method == 'POST':\n print(\"request.get_json()\") # parse as JSON\n return 'Sucesss', 200\n\n\n@app.route('/')\ndef main():\n return render_template('index.html')\nif __name__ == '__main__':\n app.run()", "sub_path": "searchBar.py", "file_name": "searchBar.py", "file_ext": "py", "file_size_in_byte": 686, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "flask.Flask", "line_number": 2, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "459632753", "text": "\"\"\"eLearningSystem URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/3.0/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom os import name\nfrom django.contrib import admin\nfrom django.urls import path\nfrom Home import views as hbv\nfrom Registration import views as rv\nfrom Login import views as lv\nfrom Profile import views as pv\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('',hbv.Homebase,name=\"homeBase\"),\n path('registration',rv.registration,name=\"registration\"),\n path('login',lv.login,name=\"login\"),\n path('contacts',hbv.contact,name=\"contacts\"),\n path('about',hbv.about,name=\"about\"),\n path('registered',rv.registered,name=\"registered\"),\n path('loging',lv.loging,name=\"loging\"), \n path('tutorials',pv.tutorials,name=\"tutorials\"),\n path('studentTutorials',pv.studentTutorials,name=\"studentTutorials\"),\n path('exams',pv.exams,name=\"exams\"),\n path('uploadtutorial',pv.uploadtutorial,name=\"uploadtutorial\"),\n path('loadTeachersTutorial',pv.loadTeachersTutorial,name=\"loadTeachersTutorial\"),\n path('getTeachersTutorial',pv.getTeachersTutorial,name=\"getTeachersTutorial\"),\n path('uploadExam',pv.uploadExam,name=\"uploadExam\"),\n path('give_exams',pv.give_exams,name=\"give_exams\"),\n path('attemptExam',pv.attemptExam,name=\"attemptExam\"),\n path('submitTest',pv.submitTest,name=\"submitTest\"),\n path('deleteTutorial',pv.deleteTutorial,name=\"deleteTutorial\"),\n path('deleteExam',pv.deleteExam,name=\"deleteExam\"),\n \n]\n", "sub_path": "eLearningSystem/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 2009, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 25, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 25, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "Home.views.Homebase", "line_number": 26, "usage_type": "attribute"}, {"api_name": "Home.views", "line_number": 26, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 27, "usage_type": "call"}, {"api_name": "Registration.views.registration", "line_number": 27, "usage_type": "attribute"}, {"api_name": "Registration.views", "line_number": 27, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 28, "usage_type": "call"}, {"api_name": "Login.views.login", "line_number": 28, "usage_type": "attribute"}, {"api_name": "Login.views", "line_number": 28, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 29, "usage_type": "call"}, {"api_name": "Home.views.contact", "line_number": 29, "usage_type": "attribute"}, {"api_name": "Home.views", "line_number": 29, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 30, "usage_type": "call"}, {"api_name": "Home.views.about", "line_number": 30, "usage_type": "attribute"}, {"api_name": "Home.views", "line_number": 30, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 31, "usage_type": "call"}, {"api_name": "Registration.views.registered", "line_number": 31, "usage_type": "attribute"}, {"api_name": "Registration.views", "line_number": 31, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 32, "usage_type": "call"}, {"api_name": "Login.views.loging", "line_number": 32, "usage_type": "attribute"}, {"api_name": "Login.views", "line_number": 32, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 33, "usage_type": "call"}, {"api_name": "Profile.views.tutorials", "line_number": 33, "usage_type": "attribute"}, {"api_name": "Profile.views", "line_number": 33, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 34, "usage_type": "call"}, {"api_name": "Profile.views.studentTutorials", "line_number": 34, "usage_type": "attribute"}, {"api_name": "Profile.views", "line_number": 34, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 35, "usage_type": "call"}, {"api_name": "Profile.views.exams", "line_number": 35, "usage_type": "attribute"}, {"api_name": "Profile.views", "line_number": 35, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 36, "usage_type": "call"}, {"api_name": "Profile.views.uploadtutorial", "line_number": 36, "usage_type": "attribute"}, {"api_name": "Profile.views", "line_number": 36, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 37, "usage_type": "call"}, {"api_name": "Profile.views.loadTeachersTutorial", "line_number": 37, "usage_type": "attribute"}, {"api_name": "Profile.views", "line_number": 37, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 38, "usage_type": "call"}, {"api_name": "Profile.views.getTeachersTutorial", "line_number": 38, "usage_type": "attribute"}, {"api_name": "Profile.views", "line_number": 38, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 39, "usage_type": "call"}, {"api_name": "Profile.views.uploadExam", "line_number": 39, "usage_type": "attribute"}, {"api_name": "Profile.views", "line_number": 39, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 40, "usage_type": "call"}, {"api_name": "Profile.views.give_exams", "line_number": 40, "usage_type": "attribute"}, {"api_name": "Profile.views", "line_number": 40, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 41, "usage_type": "call"}, {"api_name": "Profile.views.attemptExam", "line_number": 41, "usage_type": "attribute"}, {"api_name": "Profile.views", "line_number": 41, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 42, "usage_type": "call"}, {"api_name": "Profile.views.submitTest", "line_number": 42, "usage_type": "attribute"}, {"api_name": "Profile.views", "line_number": 42, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 43, "usage_type": "call"}, {"api_name": "Profile.views.deleteTutorial", "line_number": 43, "usage_type": "attribute"}, {"api_name": "Profile.views", "line_number": 43, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 44, "usage_type": "call"}, {"api_name": "Profile.views.deleteExam", "line_number": 44, "usage_type": "attribute"}, {"api_name": "Profile.views", "line_number": 44, "usage_type": "name"}]} +{"seq_id": "623831757", "text": "import argparse\nimport os\nfrom boltons import fileutils\n\nparser = argparse.ArgumentParser(description='Create and index file for ALLSSTAR data, index.txt is create in the path of the dataset')\nparser.add_argument('--src', help='data dir', required=True)\nparser.add_argument('--ext', help='extension to index', required=True)\nargs = parser.parse_args()\n\nrows = [\"path\\tspeaker_id\\tgender\\tnative_language\\ttask_language\\ttask\\ttake\\tsub_take\\n\"]\nfiles = fileutils.iter_find_files(args.src, f\"*.{args.ext}\")\nfor file in files:\n sub_take = None\n try:\n file_no_ext = file[:-4].split(\"/\")[-1]\n split = file_no_ext.split(\"_\")\n speaker_id = split[1]\n speaker_gender = split[2]\n native_lang = split[3]\n task_lang = split[4]\n task = split[5]\n take = split[6]\n sub_take = split[7]\n rows.append(f\"{file}\\t{speaker_id}\\t{speaker_gender}\\t{native_lang}\\t{task_lang}\\t{task}\\t{take}\\t{sub_take}\\n\")\n except Exception:\n if sub_take == None:\n sub_take = -1\n rows.append(f\"{file}\\t{speaker_id}\\t{speaker_gender}\\t{native_lang}\\t{task_lang}\\t{task}\\t{take}\\t{sub_take}\\n\")\n else:\n print(f\"warning: '{file}' has an invalid naming convention (skipped)\")\n\nindex_file = os.path.join(args.src, f\"{args.ext}_index.txt\")\nopen(index_file, 'w').writelines(rows)\nprint(\"done\")\n", "sub_path": "scripts/create_index_file.py", "file_name": "create_index_file.py", "file_ext": "py", "file_size_in_byte": 1375, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 5, "usage_type": "call"}, {"api_name": "boltons.fileutils.iter_find_files", "line_number": 11, "usage_type": "call"}, {"api_name": "boltons.fileutils", "line_number": 11, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}]} +{"seq_id": "321857817", "text": "import pandas as pd\n\nimport us\nfrom can_tools.scrapers import CMU\nfrom can_tools.scrapers.official.base import ArcGIS\n\nfrom typing import Any\n\n\nclass PennsylvaniaCasesDeaths(ArcGIS):\n \"\"\"\n Fetch county level covid data from Pennsylvania's ARCGIS dashboard\n \"\"\"\n\n ARCGIS_ID = \"Nifc7wlHaBPig3Q3\"\n has_location = False\n location_type = \"county\"\n state_fips = int(us.states.lookup(\"Pennsylvania\").fips)\n source = \"https://experience.arcgis.com/experience/ed2def13f9b045eda9f7d22dbc9b500e\"\n service: str = \"COVID_PA_Counties\"\n\n cols_to_keep = [\n \"dt\",\n \"location_name\",\n \"category\",\n \"measurement\",\n \"unit\",\n \"age\",\n \"race\",\n \"ethnicity\",\n \"sex\",\n \"value\",\n ]\n\n def fetch(self) -> Any:\n return self.get_all_jsons(self.service, 0, 1)\n\n def pre_normalize(self, data) -> pd.DataFrame:\n df = self.arcgis_jsons_to_df(data)\n\n # Make columns names all-lowercase\n df.columns = [x.lower() for x in list(df)]\n df = df.rename(columns={\"county\": \"location_name\"})\n\n crename = {\n \"cases\": CMU(category=\"cases\", measurement=\"cumulative\", unit=\"people\"),\n \"deaths\": CMU(category=\"deaths\", measurement=\"cumulative\", unit=\"people\"),\n # \"probable\": CMU(\n # category=\"cases_probable\",\n # measurement=\"cumulative\",\n # unit=\"people\",\n # ),\n \"negative\": CMU(\n category=\"pcr_tests_negative\",\n measurement=\"cumulative\",\n unit=\"unique_people\",\n ),\n \"confirmed\": CMU(\n category=\"pcr_tests_positive\",\n measurement=\"cumulative\",\n unit=\"unique_people\",\n ),\n }\n out = (\n df.melt(id_vars=[\"location_name\"], value_vars=crename.keys())\n .assign(dt=self._retrieve_dt(\"US/Eastern\"))\n .dropna()\n .replace(dict(location_name=dict(Mckean=\"McKean\")))\n )\n out.loc[:, \"value\"] = pd.to_numeric(out[\"value\"])\n\n # Extract category information and add other variable context\n out = self.extract_CMU(out, crename)\n\n return out.loc[:, self.cols_to_keep].query(\"location_name != 'Pennsylvania'\")\n\n def normalize(self, data) -> pd.DataFrame:\n # Normalize data, which is dependent on the current class\n out = self.pre_normalize(data)\n\n out[\"vintage\"] = self._retrieve_vintage()\n return out\n\n\nclass PennsylvaniaHospitals(PennsylvaniaCasesDeaths):\n\n service: str = \"covid_hosp\"\n\n def pre_normalize(self, data) -> pd.DataFrame:\n df = self.arcgis_jsons_to_df(data)\n\n # Make columns names all-lowercase\n df.columns = [x.lower() for x in list(df)]\n df = df.rename(columns={\"county\": \"location_name\"})\n\n crename = {\n \"med_total\": CMU(\n category=\"hospital_beds_capacity\", measurement=\"current\", unit=\"beds\"\n ),\n \"covid_patients\": CMU(\n category=\"hospital_beds_in_use_covid\",\n measurement=\"current\",\n unit=\"beds\",\n ),\n \"icu_total\": CMU(\n category=\"icu_beds_capacity\", measurement=\"current\", unit=\"beds\"\n ),\n \"icu_avail\": CMU(\n category=\"icu_beds_available\", measurement=\"current\", unit=\"beds\"\n ),\n }\n\n df[\"dt\"] = df[\"date\"].map(self._esri_ts_to_dt)\n\n out = df.melt(\n id_vars=[\"location_name\", \"dt\"], value_vars=crename.keys()\n ).dropna()\n\n non_county_regions = [\n \"Pennsylvania\",\n \"East Central HCC\",\n \"HCC of Southwest PA\",\n \"Keystone HCC\",\n \"North Central HCC\",\n \"Northeast\",\n \"Northcentral\",\n \"Northeast HCC\",\n \"Northern Tier HCC\",\n \"Northwest\",\n \"Southcentral\",\n \"Southeast HCC\",\n \"Southeast\",\n \"Southwest\",\n ]\n\n out = out[~out[\"location_name\"].isin(non_county_regions)]\n\n out.loc[:, \"value\"] = pd.to_numeric(out[\"value\"])\n\n out = self.extract_CMU(out, crename)\n\n return out.loc[:, self.cols_to_keep]\n", "sub_path": "can_tools/scrapers/official/PA/pa_state.py", "file_name": "pa_state.py", "file_ext": "py", "file_size_in_byte": 4293, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "can_tools.scrapers.official.base.ArcGIS", "line_number": 10, "usage_type": "name"}, {"api_name": "us.states.lookup", "line_number": 18, "usage_type": "call"}, {"api_name": "us.states", "line_number": 18, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 35, "usage_type": "name"}, {"api_name": "can_tools.scrapers.CMU", "line_number": 46, "usage_type": "call"}, {"api_name": "can_tools.scrapers.CMU", "line_number": 47, "usage_type": "call"}, {"api_name": "can_tools.scrapers.CMU", "line_number": 53, "usage_type": "call"}, {"api_name": "can_tools.scrapers.CMU", "line_number": 58, "usage_type": "call"}, {"api_name": "pandas.to_numeric", "line_number": 70, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 38, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 77, "usage_type": "attribute"}, {"api_name": "can_tools.scrapers.CMU", "line_number": 97, "usage_type": "call"}, {"api_name": "can_tools.scrapers.CMU", "line_number": 100, "usage_type": "call"}, {"api_name": "can_tools.scrapers.CMU", "line_number": 105, "usage_type": "call"}, {"api_name": "can_tools.scrapers.CMU", "line_number": 108, "usage_type": "call"}, {"api_name": "pandas.to_numeric", "line_number": 138, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 89, "usage_type": "attribute"}]} +{"seq_id": "339324157", "text": "from django.conf.urls import url\nfrom .views.order import user_orders, order_detail, \\\n order_info, part_detail, user_expertize, \\\n order_expert_detail, expert_part_detail, parts_change_names\n\nfrom .views.kvquestion import kv_questions, kv_question_add, kv_question_del, kv_question_edit, expert_kv_questions, expert_kv_question\nfrom .views.expert_meta import expert_accept, expert_ignore\n\n\nurlpatterns = (\n\n url(r'^my', user_orders),\n url(r'^order/(?P\\d+)', order_detail),\n url(r'^parts/(?P\\d+)', part_detail),\n url(r'^order_info/(?P\\d+)', order_info),\n url(r'^parts_names/(?P\\d+)', parts_change_names),\n\n url(r'^kv_questions/(?P\\d+)', kv_questions),\n url(r'^experts/kv_questions/(?P\\d+)', expert_kv_questions),\n url(r'^kv_questions/add/(?P\\d+)', kv_question_add),\n url(r'^kv_questions/del/(?P\\d+)', kv_question_del),\n url(r'^kv_questions/edit/(?P\\d+)', kv_question_edit),\n url(r'^kv_questions/expert/(?P\\d+)', expert_kv_question),\n\n url(r'^experts', user_expertize),\n url(r'^expert_order/(?P\\d+)$', order_expert_detail),\n url(r'^expert_parts/(?P\\d+)$', expert_part_detail),\n url(r'^expert_accept/', expert_accept),\n url(r'^expert_ignore/', expert_ignore)\n)\n", "sub_path": "orders/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1337, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "views.order.user_orders", "line_number": 12, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "views.order.order_detail", "line_number": 13, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "views.order.part_detail", "line_number": 14, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "views.order.order_info", "line_number": 15, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "views.order.parts_change_names", "line_number": 16, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 18, "usage_type": "call"}, {"api_name": "views.kvquestion.kv_questions", "line_number": 18, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 19, "usage_type": "call"}, {"api_name": "views.kvquestion.expert_kv_questions", "line_number": 19, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 20, "usage_type": "call"}, {"api_name": "views.kvquestion.kv_question_add", "line_number": 20, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 21, "usage_type": "call"}, {"api_name": "views.kvquestion.kv_question_del", "line_number": 21, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 22, "usage_type": "call"}, {"api_name": "views.kvquestion.kv_question_edit", "line_number": 22, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 23, "usage_type": "call"}, {"api_name": "views.kvquestion.expert_kv_question", "line_number": 23, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 25, "usage_type": "call"}, {"api_name": "views.order.user_expertize", "line_number": 25, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 26, "usage_type": "call"}, {"api_name": "views.order.order_expert_detail", "line_number": 26, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 27, "usage_type": "call"}, {"api_name": "views.order.expert_part_detail", "line_number": 27, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 28, "usage_type": "call"}, {"api_name": "views.expert_meta.expert_accept", "line_number": 28, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 29, "usage_type": "call"}, {"api_name": "views.expert_meta.expert_ignore", "line_number": 29, "usage_type": "argument"}]} +{"seq_id": "127964744", "text": "from calendar import day_abbr\nimport datetime\n# seconds\n# minutes\n# hours\n# days\n# weeks\n# months=not supported\n# years=not supported\n\n# now\n# today\n# tomorrow\n# yesterday\n# DDMMYYYY\ndef givep(t):\n dat=datetime.date.today()\n\n if t=='today':\n dat= datetime.date.today()\n elif t=='tomorrow':\n dat= datetime.date.today()+datetime.timedelta(days=1)\n elif t=='yesterday':\n dat= datetime.date.today()-datetime.timedelta(days=1)\n elif t=='now':\n return datetime.datetime.now()\n else:\n dat=datetime.date(int(t[4:8]), int(t[2:4]), int(t[0:2]))\n\n return datetime.datetime.combine(dat, datetime.time(0,0,0))\n\ndef givedelta(t):\n ind=0\n num=''\n for i in range(len(t)):\n if t[i]<'0' or t[i]>'9':\n ind=i\n break\n num+=t[i]\n num=int(num)\n yu=t[ind:]\n if yu==\"second\" or yu==\"seconds\":\n return datetime.timedelta(seconds=num)\n if yu==\"minute\" or yu==\"minutes\":\n return datetime.timedelta(minutes=num)\n if yu==\"hour\" or yu==\"hours\":\n return datetime.timedelta(hours=num)\n if yu==\"day\" or yu==\"days\":\n return datetime.timedelta(days=num)\n if yu==\"week\" or yu==\"weeks\":\n return datetime.timedelta(weeks=num)\n \n\ndef givenextind(t):\n ind=-1\n for i in range(len(t)):\n if t[i]=='+' or t[i]=='-':\n ind=i\n break\n if ind==-1:\n ind=len(t)\n return ind\n\ndef giveout(t):\n ind=givenextind(t)\n ptime=givep(t[0:ind])\n t=t[ind:]\n while len(t)>0:\n next_ind=givenextind(t[1:])+1\n if t[0]=='+':\n ptime+=givedelta(t[1:next_ind])\n elif t[0]=='-':\n ptime-=givedelta(t[1:next_ind])\n t=t[next_ind:]\n return ptime\n\n \n\n# print(datetime.date.today(), type(datetime.date.today().day))\n\n# t = datetime.time(1, 2, 3)\n# print ('t :', t)\n\n# d = datetime.date.today()\n# print ('d :', d)\n\n# dt = datetime.datetime.combine(d, t)\n# print (datetime.timedelta(minutes=4, seconds=5))\n\n# g=\"today+1days+20hours\"\nprint(giveout('24062022+1week'))\n# print(givenextind(g))", "sub_path": "p3.py", "file_name": "p3.py", "file_ext": "py", "file_size_in_byte": 2084, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "datetime.date.today", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 17, "usage_type": "attribute"}, {"api_name": "datetime.date.today", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 20, "usage_type": "attribute"}, {"api_name": "datetime.date.today", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 22, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 24, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 26, "usage_type": "attribute"}, {"api_name": "datetime.date", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.datetime.combine", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 30, "usage_type": "attribute"}, {"api_name": "datetime.time", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 45, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 51, "usage_type": "call"}]} +{"seq_id": "279819469", "text": "__author__ = 'omic'\n__version__ = '0.8.1'\n__build__ = '1'\n\nimport sys\nimport os\nimport argparse\nimport logging\nsys.path.append(os.path.dirname(os.getcwd()))\nfrom adapter import MongoAdapter, HttpAdapter\nfrom toolkit import mtimer\n\ndef main():\n pars = argparse.ArgumentParser(prog='mongostat-client/main.py', usage='%(prog)s [options]')\n pars.add_argument('-v', '--version', action='version', help='print mongostat-client version', version='mongostat-client {0}({1})'.format(__version__, __build__))\n pars.add_argument('-l', '--log_file', action='store', help='define log file\\'s name', default='mongostat-client.log')\n pars.add_argument('-c', '--config', action='store', help='define config', default='settings')\n args = pars.parse_args()\n\n LOG_FORMAT = logging.Formatter('%(asctime)s\\t%(levelname)s\\t%(lineno)d\\t%(message)s')\n LOG_HANDLER = {'FILE':logging.FileHandler(args.log_file),'CON':logging.StreamHandler(sys.stdout)}\n LOG_LEVEL = {'DEBUG':logging.DEBUG,'INFO':logging.INFO,'WARNING':logging.WARNING,'ERROR':logging.ERROR,'CRITICAL':logging.CRITICAL,\n 'NOTSET':logging.NOTSET}\n log = logging.getLogger('monstat-client-logger')\n\n try:\n sett = __import__(args.config)\n except ImportError:\n log.setLevel(LOG_LEVEL['ERROR'])\n log.addHandler(LOG_HANDLER['FILE'])\n log.handlers[0].setFormatter(LOG_FORMAT)\n log.error('Config file \"{0}\" not load'.format(args.config))\n\n mongo_server = sett.mongo_settings.get('server', '127.0.0.1')\n mongo_port = sett.mongo_settings.get('port', 27017)\n http_server = sett.http_settings.get('server', '127.0.0.1')\n http_port = sett.http_settings.get('port', '5000')\n http_method = sett.http_settings.get('method', 'PUT_STAT')\n cycle = sett.common_settings.get('cycle', 60)\n log_level = LOG_LEVEL[sett.log_settings.get('level', 'ERROR').upper()]\n log_handler = LOG_HANDLER[sett.log_settings.get('outlet', 'FILE').upper()]\n log.setLevel(log_level)\n log.addHandler(log_handler)\n log.handlers[0].setFormatter(LOG_FORMAT)\n\n mongo_adapter = MongoAdapter(mongo_server, mongo_port, log)\n http_adapter = HttpAdapter(http_server, http_port, http_method)\n mongo_adapter.subscribe(http_adapter)\n tm = mtimer.Timer(mongo_adapter.pull, cycle)\n tm.start()\n\n\nif __name__ == '__main__':\n sys.exit(main())", "sub_path": "monstat_client/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2355, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sys.path.append", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 9, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 14, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 21, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 21, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 21, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 22, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 22, "usage_type": "attribute"}, {"api_name": "logging.WARNING", "line_number": 22, "usage_type": "attribute"}, {"api_name": "logging.ERROR", "line_number": 22, "usage_type": "attribute"}, {"api_name": "logging.CRITICAL", "line_number": 22, "usage_type": "attribute"}, {"api_name": "logging.NOTSET", "line_number": 23, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 24, "usage_type": "call"}, {"api_name": "adapter.MongoAdapter", "line_number": 46, "usage_type": "call"}, {"api_name": "adapter.HttpAdapter", "line_number": 47, "usage_type": "call"}, {"api_name": "toolkit.mtimer.Timer", "line_number": 49, "usage_type": "call"}, {"api_name": "toolkit.mtimer", "line_number": 49, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "204559105", "text": "\"\"\"\ncontroller.py\n\nContains the Controller class for solving sudoku puzzles. This is where the solving takes place. If not used in an MVC\ncontext, the Controller class can be thought of as a \"SudokuSolver\" class.\n\nauthor: Wiley Matthews\n\"\"\"\nfrom typing import List\nimport time\nimport threading\n\nfrom model import Model\n\n\nclass Controller:\n\n def __init__(self, model, delay=0):\n # Solving elements\n self.stop = False\n\n # Display elements\n self.delay = delay\n self.model = model\n\n def start_solving(self) -> None:\n \"\"\"\n This method starts the worker thread that will spawn from the main-view thread and do the solving.\n :return: None\n \"\"\"\n self._worker = threading.Thread(target=self.worker_task)\n self._worker.start()\n\n def worker_task(self, delay=1) -> None:\n \"\"\"\n The task to be carried out by the worker thread. Waits for a specified delay (default 1 second) before starting\n to solve in order to give the UI time to finish initializing.\n :param delay: time delay between method call and solving start (in seconds).\n :return: None\n \"\"\"\n time.sleep(delay) # To give UI time to initialize.\n self.solveSudoku(self.model)\n\n def solveSudoku(self, model: Model) -> None:\n \"\"\"\n Recursively use backtracking to solve a Sudoku puzzle in-place.\n :param model: model containing puzzle.\n :return: None\n \"\"\"\n if not (self.is_solution(model.board) or self.stop):\n for i in range(len(model.board)):\n for j in range(len(model.board[i])):\n if model.board[i][j] == '.':\n for k in range(1, 10):\n model.make_move(str(k), i, j)\n time.sleep(self.delay)\n if self.is_valid(model.board):\n self.solveSudoku(model)\n if self.stop:\n break\n model.make_move('.', i, j)\n time.sleep(self.delay)\n return\n if self.stop:\n break\n if self.stop:\n break\n else:\n self.stop = True\n\n def is_valid(self, board: List[List[str]]) -> bool:\n \"\"\"\n Determines if the state of the supplied sudoku puzzle is valid.\n :param board: nested lists representing the puzzle state\n :return: True if valid state, false if invalid.\n \"\"\"\n for i in range(len(board)):\n row = []\n col = []\n for j in range(len(board)):\n if board[i][j] not in col and board[j][i] not in row:\n if board[j][i] != '.':\n row.append(board[j][i])\n if board[i][j] != '.':\n col.append(board[i][j])\n else:\n return False\n for i in range(3):\n for j in range(3):\n square = []\n for k in range(3):\n for l in range(3):\n if board[k + i*3][l + j*3] not in square:\n if board[k + i*3][l + j*3] != '.':\n square.append(board[k + i*3][l + j*3])\n else:\n return False\n return True\n\n def is_solution(self, board: List[List[str]]) -> bool:\n \"\"\"\n Determines if the state of the supplied sudoku puzzle is a solution.\n :param board: nested lists representing the puzzle state\n :return: True if solution, false if not a solution.\n \"\"\"\n for i in range(len(board)):\n row = []\n col = []\n for j in range(len(board)):\n if board[i][j] == \".\" or board[j][i] == \".\":\n return False\n if board[i][j] not in col and board[j][i] not in row:\n row.append(board[j][i])\n col.append(board[i][j])\n else:\n return False\n for i in range(3):\n for j in range(3):\n square = []\n for k in range(3):\n for l in range(3):\n if board[k + i*3][l + j*3] == \".\":\n return False\n if board[k + i*3][l + j*3] not in square:\n square.append(board[k + i*3][l + j*3])\n else:\n return False\n return True\n\n\ndef main():\n pass\n\n\nif __name__ == '__main__':\n main()\n\n", "sub_path": "sudoku/controller.py", "file_name": "controller.py", "file_ext": "py", "file_size_in_byte": 4755, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "threading.Thread", "line_number": 31, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 41, "usage_type": "call"}, {"api_name": "model.Model", "line_number": 44, "usage_type": "name"}, {"api_name": "model.board", "line_number": 50, "usage_type": "attribute"}, {"api_name": "model.board", "line_number": 51, "usage_type": "attribute"}, {"api_name": "model.board", "line_number": 52, "usage_type": "attribute"}, {"api_name": "model.board", "line_number": 53, "usage_type": "attribute"}, {"api_name": "model.make_move", "line_number": 55, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 56, "usage_type": "call"}, {"api_name": "model.board", "line_number": 57, "usage_type": "attribute"}, {"api_name": "model.make_move", "line_number": 61, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 62, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 71, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 100, "usage_type": "name"}]} +{"seq_id": "450901104", "text": "import requests\nfrom fastapi import FastAPI\nfrom fastapi.middleware.cors import CORSMiddleware\nimport sqlite3\nimport json\nimport urllib.request\nimport datetime\nfrom datetime import date\n\napp = FastAPI()\norigins = [\"*\"]\n\napp.add_middleware(\n CORSMiddleware,\n allow_origins=origins,\n allow_credentials=True,\n allow_methods=[\"*\"],\n allow_headers=[\"*\"],\n)\n\n\n@app.get('/api')\ndef read_root():\n return {'detail': 'Welcome to this app'}\n\n@app.post('/api/Registrar_P/{cedula}/{vacuna}/{provincia}/{fecha_v}')\ndef Registrar_P(cedula: str, vacuna: str, provincia: str, fecha_v: str):\n conexion=sqlite3.connect('app.db')\n registro=conexion.cursor()\n respuesta = urllib.request.urlopen('https://api.adamix.net/apec/cedula/'+cedula+'')\n data = json.loads(respuesta.read())\n query = registro.execute (\"SELECT * FROM VACUNADO Where CEDULA = '\"+cedula+\"'\")\n datos = query.fetchall()\n for dato in datos:\n if dato[1] == cedula:\n sql =(\"UPDATE VACUNADO SET FECHA_S = '\"+fecha_v+\"' WHERE CEDULA = '\"+cedula+\"'\")\n registro.execute(sql)\n registro.execute(\"UPDATE VACUNA SET CANTIDAD = CANTIDAD-1 WHERE MARCA = '\"+vacuna+\"'\")\n conexion.commit()\n return {\"mensaje\":\"Segunda dosis agregada\",\"nombre\": dato[2],\"apellido\":dato[3], \"fecha_p\":dato[7] }\n if datos == []: \n try: \n if data['Cedula']==cedula:\n info= (data['Cedula'],data['Nombres'],data['Apellido1'],datetime.datetime.strptime(data['FechaNacimiento'], '%Y-%m-%d %H:%M:%S.%f').date(),vacuna,provincia,fecha_v)\n sql=''' INSERT INTO VACUNADO(CEDULA,NOMBRE,APELLIDO,FECHA_N,VACUNA,PROVINCIA,FECHA_P) VALUES (?,?,?,?,?,?,?) '''\n registro.execute(sql,info)\n registro.execute(\"UPDATE VACUNA SET CANTIDAD = CANTIDAD-1 WHERE MARCA = '\"+vacuna+\"'\")\n conexion.commit()\n return {\"mensaje\": \"Registro Exitoso\"}\n except:\n return{'Cedula invalida'}\n@app.post('/api/Registrar_v/{marca}/{cantidad}')\ndef Registrar_v(marca: str, cantidad: int):\n conexion=sqlite3.connect('app.db')\n registro=conexion.cursor()\n info = (marca,cantidad)\n query = ''' INSERT INTO VACUNA(MARCA,CANTIDAD) VALUES (?,?) '''\n registro.execute(query,info)\n conexion.commit()\n return {'Registro Completo'}\n\n\n\n \n\n\n\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2368, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "fastapi.FastAPI", "line_number": 10, "usage_type": "call"}, {"api_name": "fastapi.middleware.cors.CORSMiddleware", "line_number": 14, "usage_type": "argument"}, {"api_name": "sqlite3.connect", "line_number": 28, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 30, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 30, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 30, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 44, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 44, "usage_type": "attribute"}, {"api_name": "sqlite3.connect", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "266763235", "text": "import json\nimport unittest\nimport time\nimport os\n\nfrom gitstatic import http, settings\n\nclass GitstaticTest(unittest.TestCase):\n\n # appends the third msg argument to assertion failure messages instead of\n # replacing it.\n longMessage = True\n\n def setUp(self):\n self.app = http.app.test_client()\n\n def test_get_root(self):\n res = self.app.get('/')\n assert res.status_code == 204\n\n def _build(self, git_url, git_treeish='master'):\n data = {'git_url': git_url, 'git_treeish': git_treeish}\n return self.app.post('/build', data=data,\n headers={'accept': 'application/json'})\n\n def test_post_build(self):\n git_url = 'git@github.com:hdgarrood/foo.git'\n res = self._build(git_url)\n\n self.assertEqual(res.status_code, 202)\n res_json = json.loads(res.data)\n self.assertEqual(res_json['message'], 'building %s' % git_url)\n\n def test_missing_git_url(self):\n res = self.app.post('/build')\n\n self.assertEqual(res.status_code, 422)\n res_json = json.loads(res.data)\n self.assertEqual(res_json['message'], 'missing parameter: git_url')\n\n def _test_invalid_git_url(self, git_url, reason):\n res = self._build(git_url)\n\n self.assertEqual(res.status_code, 403, git_url)\n res_json = json.loads(res.data)\n self.assertIn('invalid git url', res_json['message'], git_url)\n self.assertIn(reason, res_json['reason'], git_url)\n\n def test_invalid_host(self):\n urls = (\n 'git@ithub.com:hdgarrood/foo.git',\n 'git@evil-attacker.com:hdgarrood/foo.git',\n 'https://githubz.com/hdgarrood/foo.git'\n )\n for url in urls:\n self._test_invalid_git_url(url, 'bad host')\n\n def test_invalid_owner(self):\n urls = (\n 'git@github.com:evil/repo.git',\n 'git@github.com:garrood/repo.git',\n 'https://github.com/attacker/foo.git'\n )\n for url in urls:\n self._test_invalid_git_url(url, 'bad owner')\n\n def test_smoke(self):\n url = 'git@github.com:hdgarrood/gitstatic-test.git'\n res = self._build(url)\n\n self.assertEqual(res.status_code, 202)\n time.sleep(5)\n index_path = os.path.join(settings.WEB_ROOT,\n 'test.gitstatic.local', 'index.html')\n self.assertIn('hello world', open(index_path).read())\n\nif __name__ == '__main__':\n unittest.main()\n", "sub_path": "tests/test_app.py", "file_name": "test_app.py", "file_ext": "py", "file_size_in_byte": 2503, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "unittest.TestCase", "line_number": 8, "usage_type": "attribute"}, {"api_name": "gitstatic.http.app.test_client", "line_number": 15, "usage_type": "call"}, {"api_name": "gitstatic.http.app", "line_number": 15, "usage_type": "attribute"}, {"api_name": "gitstatic.http", "line_number": 15, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 31, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 38, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 45, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path", "line_number": 73, "usage_type": "attribute"}, {"api_name": "gitstatic.settings.WEB_ROOT", "line_number": 73, "usage_type": "attribute"}, {"api_name": "gitstatic.settings", "line_number": 73, "usage_type": "name"}, {"api_name": "unittest.main", "line_number": 78, "usage_type": "call"}]} +{"seq_id": "630157984", "text": "# -*- coding: utf-8 -*-\nfrom django.shortcuts import render\nfrom django.http import HttpResponse\nfrom panel.models import *\nfrom django.utils.encoding import smart_unicode\n\n# Create your views here.\n\n\ndef index(request):\n return render(request, 'index.html')\n\n\ndef jobrunner(request):\n\n #-------------------------------------------------------------\n\n website = open(\"Address.dat\", 'w')\n for sample in Website.objects.all():\n website.write(str(sample.id)+','+sample.Address)\n website.write(\"\\n\")\n website.close()\n\n #-------------------------------------------------------------\n click = open(\"click.dat\", 'w')\n for sample in Click.objects.all():\n click.write(str(sample.website.id)+','+sample.Flage)\n click.write(\"\\n\")\n click.close()\n #-------------------------------------------------------------\n searchEngine = open(\"SearchEngine.dat\", 'w')\n for sample in SearchEngine.objects.all():\n searchEngine.write(sample.keyword.encode(\"utf-8\")+'@@@'+sample.Flage1.encode(\"utf-8\")+'@@@'+str(sample.NumberPage))\n searchEngine.write(\"\\n\")\n click.close()\n\n #-------------------------------------------------------------\n WebsiteQ = open(\"WebsiteQ.dat\", 'w')\n for sample in WebSiteQuick.objects.all():\n WebsiteQ.write(sample.Address)\n WebsiteQ.write(\"\\n\")\n WebsiteQ.close()\n\n #-------------------------------------------------------------\n SettingQ = open(\"SettingQ.dat\", 'w')\n for sample in SettingQuick.objects.all():\n SettingQ.write(str(sample.NumberIteration))\n SettingQ.write(\"\\n\")\n SettingQ.close()\n\n return HttpResponse(\"job run Success\")\n", "sub_path": "v1.1/panel/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1671, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.shortcuts.render", "line_number": 11, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 51, "usage_type": "call"}]} +{"seq_id": "621268218", "text": "from django.urls import path\n\nfrom . import views\n\nurlpatterns = [\n # path('logindetails', views.logindetails, name='logindetails'),\n path('', views.login_view, name='login'),\n path('index', views.index, name='index'),\n path('registration', views.registration, name='registration'),\n path('errorImg',views.errorImg,name='ErrorImage'),\n path('detect_image',views.detect_image,name='detect_image'),\n path('create_dataset',views.create_dataset,name='create_dataset'),\n path('detect',views.detect,name='detect'),\n path('eigen_train',views.eigen_train,name='eigenTrain'),\n path('trainer', views.trainer, name='trainer'),\n path('webcam/details/',views.details,name='details'),\n path('adminlogin', views.adminlogin, name='adminlogin'),\n path('adminpage', views.adminpage,name='adminpage'),\n path('viewdetail',views.viewdetail,name='viewdetail'),\n path('webcam/deleterow/',views.deleterow,name='deleterow'),\n path('edit/',views.posts_edit,name='edit'),\n path('add_record',views.add_record,name='add_record'),\n path('updaterow',views.updaterow,name='updaterow'),\n path('webcam/editimage/',views.editimage,name='editimage'),\n]", "sub_path": "final_pr/webcam/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1205, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "268675", "text": "from django.shortcuts import render\nfrom django.views.generic import TemplateView\nfrom django.core import serializers\nfrom django.http import HttpResponse\n\nfrom scores.models import score\nfrom .models import recovery\nfrom courses.models import course\nfrom registerCourses.models import registerCourse\nfrom registers.models import register\n\ndef newRecovery(scr, rgc):\n\trcvr = recovery()\n\trcvr.test\t\t\t= scr.test\n\trcvr.workshop\t\t= scr.workshop\n\trcvr.punctuality\t= scr.punctuality\n\trcvr.dateIn\t\t\t= scr.dateIn\n\trcvr.dateMade\t\t= scr.dateMade\n\trcvr.comment\t\t= scr.comment\n\trcvr.values\t\t\t= scr.values\n\trcvr.total\t\t\t= scr.total\n\trcvr.registerCourse = rgc\n\trcvr.save()\n\nclass recoveryList(TemplateView):\n\tdef get(self, request, *args, **kwargs):\n\t\trgc = registerCourse.objects.get(pk=request.GET[\"registerCourse\"])\n\t\tcrs = recovery.objects.filter(registerCourse=rgc)\n\t\tdata = serializers.serialize(\"json\", crs)\n\t\treturn HttpResponse(data, mimetype=\"application/json\")\n\nclass recoveryCount(TemplateView):\n\tdef get(self, request, *args, **kwargs):\n\t\trgt = register.objects.get(pk=request.GET[\"register\"])\n\t\tcrs = course.objects.filter(program=rgt.program)\n\t\trc \t= registerCourse.objects.filter(student=rgt.student, course=crs).order_by('course')\n\t\t#scr = score.objects.filter(registerCourse=rc)\n\t\trcvr = recovery.objects.filter(registerCourse=rc)\n\t\tdata = serializers.serialize(\"json\", rcvr)\n\t\treturn HttpResponse(data, mimetype=\"application/json\")", "sub_path": "recoveries/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1436, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "models.recovery", "line_number": 13, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView", "line_number": 25, "usage_type": "name"}, {"api_name": "registerCourses.models.registerCourse.objects.get", "line_number": 27, "usage_type": "call"}, {"api_name": "registerCourses.models.registerCourse.objects", "line_number": 27, "usage_type": "attribute"}, {"api_name": "registerCourses.models.registerCourse", "line_number": 27, "usage_type": "name"}, {"api_name": "models.recovery.objects.filter", "line_number": 28, "usage_type": "call"}, {"api_name": "models.recovery.objects", "line_number": 28, "usage_type": "attribute"}, {"api_name": "models.recovery", "line_number": 28, "usage_type": "name"}, {"api_name": "django.core.serializers.serialize", "line_number": 29, "usage_type": "call"}, {"api_name": "django.core.serializers", "line_number": 29, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 30, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView", "line_number": 32, "usage_type": "name"}, {"api_name": "registers.models.register.objects.get", "line_number": 34, "usage_type": "call"}, {"api_name": "registers.models.register.objects", "line_number": 34, "usage_type": "attribute"}, {"api_name": "registers.models.register", "line_number": 34, "usage_type": "name"}, {"api_name": "courses.models.course.objects.filter", "line_number": 35, "usage_type": "call"}, {"api_name": "courses.models.course.objects", "line_number": 35, "usage_type": "attribute"}, {"api_name": "courses.models.course", "line_number": 35, "usage_type": "name"}, {"api_name": "registerCourses.models.registerCourse.objects.filter", "line_number": 36, "usage_type": "call"}, {"api_name": "registerCourses.models.registerCourse.objects", "line_number": 36, "usage_type": "attribute"}, {"api_name": "registerCourses.models.registerCourse", "line_number": 36, "usage_type": "name"}, {"api_name": "models.recovery.objects.filter", "line_number": 38, "usage_type": "call"}, {"api_name": "models.recovery.objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "models.recovery", "line_number": 38, "usage_type": "name"}, {"api_name": "django.core.serializers.serialize", "line_number": 39, "usage_type": "call"}, {"api_name": "django.core.serializers", "line_number": 39, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "554744810", "text": "from bs4 import BeautifulSoup\nfrom flask import Flask, render_template, request, redirect, url_for, session\nfrom operator import itemgetter\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.desired_capabilities import DesiredCapabilities\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom urllib.parse import parse_qs, urlparse\nimport requests\n\n# Create the Flask application.\napp = Flask(__name__)\napp.debug = True\napp.secret_key = \"super secret key\"\n\n\n@app.route('/', methods=['GET', 'POST'])\ndef index():\n invalidURL = request.args.get('invalidURL') or False\n return render_template('index.html', invalidURL=invalidURL)\n\n\n@app.route('/tools', methods=['GET', 'POST'])\ndef tools():\n if request.method == 'POST':\n url = request.form['url']\n query = parse_qs(urlparse(url).query, keep_blank_values=True)\n try:\n leagueId = str(query['leagueId'][0])\n seasonId = str(query['seasonId'][0])\n return redirect(url_for('tools', leagueId=leagueId, seasonId=seasonId))\n except Exception as ex:\n print('Exception in tools:' + ex)\n return redirect(url_for('index', invalidURL=True))\n else:\n try:\n leagueId = request.args.get('leagueId')\n seasonId = request.args.get('seasonId')\n return render_template('tools.html', leagueId=leagueId, seasonId=seasonId)\n except Exception as ex:\n print('Exception in tools2:' + ex)\n return redirect(url_for('index', invalidURL=True))\n\n\n@app.route('/season_rankings')\ndef season_rankings():\n try:\n leagueId = request.args.get('leagueId')\n seasonId = request.args.get('seasonId')\n url = 'http://fantasy.espn.com/basketball/league/standings?leagueId={}&seasonId={}'.format(leagueId, seasonId)\n teams, categories, weeks = setup(url)\n season_rankings, season_matchups, season_analysis = computeStats(teams, categories)\n return render_template('season_rankings.html', season_rankings=season_rankings, leagueId=leagueId,\n seasonId=seasonId)\n except Exception as ex:\n print('Exception in season rankings:' + ex)\n return redirect(url_for('index', invalidURL=True))\n\n\n@app.route('/season_matchups')\ndef season_matchups():\n try:\n leagueId = request.args.get('leagueId')\n seasonId = request.args.get('seasonId')\n url = 'http://fantasy.espn.com/basketball/league/standings?leagueId={}&seasonId={}'.format(leagueId, seasonId)\n teams, categories, weeks = setup(url)\n season_rankings, season_matchups, season_analysis = computeStats(teams, categories)\n return render_template('season_matchups.html', season_matchups=season_matchups, leagueId=leagueId,\n seasonId=seasonId)\n except Exception as ex:\n print('Exception in season matchups:' + ex)\n return redirect(url_for('index', invalidURL=True))\n\n\n@app.route('/season_analysis')\ndef season_analysis():\n try:\n leagueId = request.args.get('leagueId')\n seasonId = request.args.get('seasonId')\n url = 'http://fantasy.espn.com/basketball/league/standings?leagueId={}&seasonId={}'.format(leagueId, seasonId)\n teams, categories, weeks = setup(url)\n season_rankings, season_matchups, season_analysis = computeStats(teams, categories)\n return render_template('season_analysis.html', season_matchups=season_matchups, season_analysis=season_analysis,\n leagueId=leagueId, seasonId=seasonId)\n except Exception as ex:\n print('Exception in season analysis:' + ex)\n return redirect(url_for('index', invalidURL=True))\n\n\n@app.route('/weekly_rankings', methods=['GET', 'POST'])\ndef weekly_rankings():\n try:\n leagueId = request.args.get('leagueId')\n seasonId = request.args.get('seasonId')\n week = request.form.get('week_selection')\n if week is None:\n week = getCurrentWeek(leagueId, seasonId)\n session['currentWeek'] = week\n url = ('http://fantasy.espn.com/basketball/league/scoreboard?leagueId={}&seasonId={}&matchupPeriodId={}')\\\n .format(leagueId, seasonId, week)\n teams, categories, weeks = setup(url)\n weekly_rankings, weekly_matchups, weekly_analysis = computeStats(teams, categories)\n return render_template('weekly_rankings.html', weekly_rankings=weekly_rankings, leagueId=leagueId,\n seasonId=seasonId, currentWeek=week, weeks=weeks)\n except Exception as ex:\n print('Exception in weekly rankings:' + ex)\n return redirect(url_for('index', invalidURL=True))\n\n@app.route('/weekly_matchups', methods=['GET', 'POST'])\ndef weekly_matchups():\n try:\n leagueId = request.args.get('leagueId')\n seasonId = request.args.get('seasonId')\n week = request.form.get('week_selection')\n if week is None:\n week = session.get('currentWeek', None)\n if week is None:\n week = getCurrentWeek(leagueId, seasonId)\n session['currentWeek'] = week\n url = ('http://fantasy.espn.com/basketball/league/scoreboard?leagueId={}&seasonId={}&matchupPeriodId={}') \\\n .format(leagueId, seasonId, week)\n teams, categories, weeks = setup(url)\n weekly_rankings, weekly_matchups, weekly_analysis = computeStats(teams, categories)\n return render_template('weekly_matchups.html', weekly_matchups=weekly_matchups, leagueId=leagueId,\n seasonId=seasonId, currentWeek=week, weeks=weeks)\n except Exception as ex:\n print('Exception in weekly matchups:' + ex)\n return redirect(url_for('index', invalidURL=True))\n\n\n@app.route('/weekly_analysis', methods=['GET', 'POST'])\ndef weekly_analysis():\n try:\n leagueId = request.args.get('leagueId')\n seasonId = request.args.get('seasonId')\n week = request.form.get('week_selection')\n if week is None:\n week = session.get('currentWeek', None)\n if week is None:\n week = getCurrentWeek(leagueId, seasonId)\n session['currentWeek'] = week\n url = ('http://fantasy.espn.com/basketball/league/scoreboard?leagueId={}&seasonId={}&matchupPeriodId={}') \\\n .format(leagueId, seasonId, week)\n teams, categories, weeks = setup(url)\n weekly_rankings, weekly_matchups, weekly_analysis = computeStats(teams, categories)\n return render_template('weekly_analysis.html', weekly_matchups=weekly_matchups, weekly_analysis=weekly_analysis,\n leagueId=leagueId, seasonId=seasonId, currentWeek=week, weeks=weeks)\n except Exception as ex:\n print('Exception in weekly analysis:' + ex)\n return redirect(url_for('index', invalidURL=True))\n\ndef getCurrentWeek(leagueId, seasonId):\n url = (\"http://fantasy.espn.com/apis/v3/games/fba/seasons/{}/segments/0/leagues/{}?view=mMatchupScore&view\"\n \"mScoreboard&view=mSettings&view=mTeam&view=modular&view=mNav\").format(seasonId, leagueId)\n r = requests.get(url)\n data = r.json()\n return data['status']['currentMatchupPeriod']\n\n# Scrapes the \"Season Stats\" table from the ESPN Fantasy Standings page.\ndef setup(url):\n try:\n options = webdriver.ChromeOptions()\n options.add_argument('headless')\n capa = DesiredCapabilities.CHROME\n capa[\"pageLoadStrategy\"] = \"none\"\n driver = webdriver.Chrome(chrome_options=options, desired_capabilities=capa)\n driver.get(url)\n\n # Season standings have a different URL than weekly scoreboard\n seasonData = url.startswith('http://fantasy.espn.com/basketball/league/standings')\n if seasonData:\n WebDriverWait(driver, 10).until(EC.presence_of_element_located((By.CLASS_NAME, 'Table2__sub-header')))\n else:\n WebDriverWait(driver, 10).until(EC.presence_of_element_located((By.CLASS_NAME, 'Table2__header-row')))\n\n weeks = []\n plain_text = driver.page_source\n driver.close()\n soup = BeautifulSoup(plain_text, 'lxml')\n teams = []\n # Scrape table depending on whether it's season or weekly data.\n if seasonData:\n table = soup.find_all('thead', class_='Table2__sub-header Table2__thead')[1]\n tableSubHead = table.find('tr', class_='Table2__header-row Table2__tr Table2__even')\n listCats = tableSubHead.find_all('th')\n categories = []\n for cat in listCats:\n categories.append(cat.string)\n tableBody = soup.find_all('table', class_='Table2__table-scroller Table2__right-aligned Table2__table')[0]\n rows = tableBody.findAll('tr', {'class': 'Table2__tr Table2__tr--md Table2__even'})\n else:\n weeksDropdown = soup.find(\"select\", class_='dropdown__select')\n weeksValues = weeksDropdown.find_all(\"option\")\n weeks = [w.text for w in weeksValues]\n values = [w.get(\"value\") for w in weeksValues]\n tableSubHead = soup.find_all('tr', class_='Table2__header-row Table2__tr Table2__even')\n tableSubHead = tableSubHead[0]\n listCats = tableSubHead.find_all('th')\n categories = []\n for cat in listCats:\n if cat.string is not None:\n categories.append(cat.string)\n rows = soup.findAll('tr', {'class': 'Table2__tr Table2__tr--sm Table2__even'})\n\n # Creates a 2-D matrix which resembles the Season Stats table.\n for row in range(len(rows)):\n team_row = []\n # Season Data values always have 3 extra columns, weekly data always has 2 extra columns when scraping.\n if seasonData:\n columns = rows[row].findAll('td')[:(3 + len(categories))]\n else:\n columns = rows[row].findAll('td')[1:(2 + len(categories))]\n for column in columns:\n team_row.append(column.getText())\n # Add each team to a teams matrix.\n teams.append(team_row)\n if seasonData:\n tableBody = soup.find_all('section', class_='Table2__responsiveTable Table2__table-outer-wrap Table2--hasFixed-left Table2--hasFixed-right')[0]\n teamNamesList = tableBody.find_all('span', class_='teamName truncate')\n else:\n teamNamesList = soup.find_all('div', {'class': 'ScoreCell__TeamName ScoreCell__TeamName--shortDisplayName truncate db'})\n\n teamNames = []\n\n for teamName in teamNamesList:\n teamNames.append(teamName.string)\n\n namedTeams = []\n count = 0\n for team in teams:\n team.insert(0, teamNames[count])\n namedTeams.append(team)\n count += 1\n except Exception as ex:\n driver.close()\n print(\"Exception in setup\" + ex)\n return namedTeams, categories, weeks\n\n\n# Computes the standings and matchups.\ndef computeStats(teams, categories):\n try:\n # Initialize the dictionary which will hold information about each team along with their \"standings score\".\n teamDict = {}\n for team in teams:\n teamDict[team[0]] = 0\n\n matchupsList = []\n analysisList = []\n for team1 in teams:\n for team2 in teams:\n score, wonList, lossList, tiesList = calculateScore(team1[1:], team2[1:], categories)\n if team1 != team2:\n # The value for the dictionary is the power rankings score. A win increases the score and a loss\n # decreases the \"PR\" score.\n if score[0] > score[1]:\n teamDict[team1[0]] += 1\n elif score[0] < score[1]:\n teamDict[team1[0]] -= 1\n # map(str, score) is for formatting the score tuple into a string.\n matchupsList.append(\n team1[0] + ' vs. ' + team2[0] + ' || SCORE (W-L-T): ' + '-'.join(map(str, score)))\n analysisList.append(\n team1[0] + ' vs. ' + team2[0] + ' -- ' + team1[0] + ' won ' + ', '.join(wonList) + '. '\n + team1[0] + ' lost ' + ', '.join(lossList) + '. ' + team1[0] + ' tied ' + ', '.join(\n tiesList) + '.')\n matchupsList.append('*' * 100)\n analysisList.append('*' * 100)\n\n # Check if two keys in the dictionary have the same value (used to process\n # ties in standings score).\n result = {}\n for val in teamDict:\n if teamDict[val] in result:\n result[teamDict[val]].append(val)\n else:\n result[teamDict[val]] = [val]\n\n # Sort the dictionary by greatest standings score.\n sortedDict = sorted(result.items(), key=itemgetter(0), reverse=True)\n\n # Contains the standings.\n rankingsList = []\n counter = 1\n # Keys are the standings score, values are the team names.\n for k, v in sortedDict:\n rankingsList.append(str(counter) + '. ' + ', '.join(v))\n counter += 1\n except Exception as ex:\n print(\"Exception in compute \" + ex)\n\n return rankingsList, matchupsList, analysisList\n\n\n# Calculates the score for individual matchups.\ndef calculateScore(teamA, teamB, categories):\n try:\n wins = 0\n losses = 0\n ties = 0\n wonList = []\n lossList = []\n tiesList = []\n\n turnoverCol = -1\n for category in categories:\n if category == 'TO':\n turnoverCol = categories.index(category)\n break\n\n for i, (a, b) in enumerate(zip(teamA, teamB)):\n # Ignore empty values.\n if a != '' and b != '':\n a = float(a)\n b = float(b)\n # When comparing turnovers, having a smaller value is a \"win\".\n if i == turnoverCol:\n if a < b:\n wins += 1\n wonList.append(categories[i] + ' (' + str(b - a) + ')')\n elif a == b:\n ties += 1\n tiesList.append(categories[i] + ' (' + str(b - a) + ')')\n else:\n losses += 1\n lossList.append(categories[i] + ' (' + str(b - a) + ')')\n else:\n if a > b:\n wins += 1\n wonList.append(categories[i] + ' (' + str(round((a - b), 4)) + ')')\n elif a == b:\n ties += 1\n tiesList.append(categories[i] + ' (' + str(round((a - b), 4)) + ')')\n else:\n losses += 1\n lossList.append(categories[i] + ' (' + str(round((a - b), 4)) + ')')\n\n valuesTuple = ((wins, losses, ties), wonList, lossList, tiesList)\n except Exception as ex:\n print(\"Exception in calculateScore \" + ex)\n return valuesTuple\n\n# Run the Flask app.\nif __name__ == '__main__':\n app.run()\n\n# Comment out the if statement above and uncomment the line below to debug Python code.\n# teams, categories = setup('http://fantasy.espn.com/basketball/league/standings?leagueId=224165&seasonId=2019')\n# rankingsList, matchupsList, analysisList = computeStats(teams, categories)", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 15583, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "flask.Flask", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 27, "usage_type": "name"}, {"api_name": "urllib.parse.parse_qs", "line_number": 28, "usage_type": "call"}, {"api_name": "urllib.parse.urlparse", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 32, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 32, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 49, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 49, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 49, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 50, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 50, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 64, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 64, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 65, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 65, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 79, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 79, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 79, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 80, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 80, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 84, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 88, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 88, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 94, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 94, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 94, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 95, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 95, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 95, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 96, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 96, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 96, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 99, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 104, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 108, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 108, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 113, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 113, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 113, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 114, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 114, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 114, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 115, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 115, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 115, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 117, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 117, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 120, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 125, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 129, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 129, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 135, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 135, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 135, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 136, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 136, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 136, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 137, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 137, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 137, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 139, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 139, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 142, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 147, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 151, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 151, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 156, "usage_type": "call"}, {"api_name": "selenium.webdriver.ChromeOptions", "line_number": 163, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 163, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.desired_capabilities.DesiredCapabilities.CHROME", "line_number": 165, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.desired_capabilities.DesiredCapabilities", "line_number": 165, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 167, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 167, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.wait.WebDriverWait", "line_number": 173, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 173, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 173, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CLASS_NAME", "line_number": 173, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 173, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.wait.WebDriverWait", "line_number": 175, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 175, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 175, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CLASS_NAME", "line_number": 175, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 175, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 180, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 281, "usage_type": "call"}]} +{"seq_id": "533778870", "text": "import requests\nimport json\ndef get_translate_date(word = None):\n url = 'http://fanyi.youdao.com/translate_o?smartresult=dict&smartresult=rule'\n Form_data = {'i': '我爱中国',\n 'from': 'AUTO',\n 'to': 'AUTO',\n 'smartresult': 'dict',\n 'client': 'fanyideskweb',\n 'salt': '15642968895820',\n 'sign': '724f79ff1b002a43098d7452e0329c2d',\n 'ts': '1564296889582',\n 'bv': '9d1e6a4f9d4241fb7947f623cc9e4efa',\n 'doctype': 'json',\n 'version': '2.1',\n 'keyfrom': 'fanyi.web',\n 'action': 'FY_BY_REALTlME'}\n response = requests.post(url, data = payload)\n content = json.load(response.text)\n print(content['translateResult'][0][0]['tgt'])\nif __name__ == '__main__':\n get_translate_date('我爱数据')\n", "sub_path": "Post-practice.py", "file_name": "Post-practice.py", "file_ext": "py", "file_size_in_byte": 898, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "requests.post", "line_number": 18, "usage_type": "call"}, {"api_name": "json.load", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "247601295", "text": "#-------------------------------------#\n#輸出視訊鏡頭影像#\nimport cv2\n# 選擇第二隻攝影機（0 代表第一隻、1 代表第二隻）。\ncap = cv2.VideoCapture(1)\nwhile(True):\n # 從攝影機擷取一張影像\n ret, frame = cap.read()\n # 顯示圖片\n cv2.imshow('frame', frame)\n # 若按下 q 鍵則離開迴圈\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n# 釋放攝影機\ncap.release()\n# 關閉所有 OpenCV 視窗\ncv2.destroyAllWindows()\n#用cap.isOpened() 檢查攝影機是否有啟動\n#用cap.open() 啟動它。\n#-------------------------------------#\n\n#-------------------------------------#\n#縮放視窗大小#\n# 讓視窗可以自由縮放大小\ncv2.namedWindow('My Image', cv2.WINDOW_NORMAL)\ncv2.imshow('My Image', img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n#-------------------------------------#\n", "sub_path": "輸出視訊鏡頭影像.py", "file_name": "輸出視訊鏡頭影像.py", "file_ext": "py", "file_size_in_byte": 837, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "cv2.VideoCapture", "line_number": 5, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.WINDOW_NORMAL", "line_number": 25, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "300041174", "text": "from pyfaidx import Fasta\n\nthis_query_id = \"\"\nallSet = set()\nbadSet = set()\nfor line in open(\"/Users/yangh13/python/packages/CRoS/CRoS/geo/fastq/trimmed/trinity_out_dgriG1/dgriG1_trinity_assembled_transcriptome.short.out\",\"r\"):\n\tthis_line = line.rstrip()\n\tif this_line.startswith(\"Query= \"):\n\t\tthis_query_id = this_line.replace(\"Query= \",\"\")\n\t\tallSet.add(this_query_id)\n\t\t#print(this_query_id+\"\\n\")\n\tif this_line.startswith(\"Sequences producing significant alignments\"):\n\t\t#print(this_line+\"\\n\\n\")\n\t\tbadSet.add(this_query_id)\n\n\t\t\n\nfasta = Fasta(\"/Users/yangh13/python/packages/CRoS/CRoS/geo/fastq/trimmed/trinity_out_dgriG1/dgriG1_trinity_assembled_transcriptome.short.fasta\", as_raw = True)\n\n\ngoodSet = allSet - badSet\n\nfor Trinity_trans_id in badSet:\n\tprint(\"Bad\\t\" + str(len(str(fasta[Trinity_trans_id]))))\n\nfor Trinity_trans_id in goodSet:\n\tprint(\"Good\\t\" + str(len(str(fasta[Trinity_trans_id]))))\n\t\n", "sub_path": "CRoS/remove.trinity.transcripts.with.vec.py", "file_name": "remove.trinity.transcripts.with.vec.py", "file_ext": "py", "file_size_in_byte": 904, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "pyfaidx.Fasta", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "593375898", "text": "import ROOT\nfrom hnSparse import *\nfrom roottompl import *\n\nimport matplotlib.pyplot as plt\n\nkwargs = {\"fmt\" : 'o'}\nkwarg = {'cmap' : cm.jet, 'rstride' : 1, 'cstride' : 1}\n\n\nf = ROOT.TFile(\"\")\nf = ROOT.TFile(\"\")\nf = ROOT.TFile(\"\")\nf = ROOT.TFile(\"\")\n\n#f = ROOT.TFile(\"Photon_cent_0_vtx_0___tPt_3.0_6.0___cPt_1.0_3.0___iso_2_2.root\")\nf = ROOT.TFile(\"Photon_cent_1_vtx_1___tPt_3.0_10.0___cPt_1.0_3.0___iso_1_2.root\");\n\nl = f.GetListOfKeys()\nl.Print()\n\nme = l.FindObject(\"bg\").ReadObj()\n#me.Rebin2D(2, 2)\nme.Scale(1./me.GetBinContent(me.GetMaximumBin()))\n\n\nfig = figure()\nX, Y, Z = draw_3d(me, fig=fig, alpha = 0.7, **kwarg)\ngca().set_zlabel(r\"$ \\frac{dN^{\\gamma-h}}{d\\Delta \\eta d\\Delta\\phi}$\")\ngca().text(-2, 0.0, 1.0, r\"Mixed event photon track correlations. \\\\ Normalized by setting the maximum bin = 1\")\ngca().margins(0.0, 0.0, 0.0)\ngca().view_init(20, -50)\n\n# cset = gca().contour(X, Y, Z, zdir = 'y', offset = 1.8)\n# gca().set_ylim(-1.6, 1.8)\n\n# cset = gca().contour(X, Y, Z, zdir = 'x', offset = - 2)\n# gca().set_xlim(-2, 5)\n\nplt.tight_layout()\nplt.savefig(\"me.pdf\")#, bbox_inches='tight')\n\n\nsig = l.FindObject(\"signal\").ReadObj()\n#sig.Rebin2D(2, 2)\nfig = figure()\ndraw_3d(sig, fig=fig, alpha = 0.9, **kwarg)\n#gca().set_zscale(\"log\")\nplt.savefig(\"sig.jpg\", bbox_inches='tight')\n\n\noned = me.ProjectionY()\nDraw1D(oned, **kwargs)\ngca().set_ylim(bottom=0.0)\nplt.savefig(\"1d.jpg\", bbox_inches='tight')\n\n\n\n\n\n\n\n\n\n# etaax = me.GetYaxis()\n# for etabin in range(1, etaax.GetNbins()):\n# etaax.SetRange(etabin, etabin)\n# hist = me.ProjectionX()\n# hist.Scale(1./hist.Integral())\n# Draw1D(hist)\n# gca().set_ylim(bottom = 0.0, top=0.07) #, top = 1.1*max(self.entries), auto = False)\n \n# plt.show()\n \n", "sub_path": "draw_phi_me.py", "file_name": "draw_phi_me.py", "file_ext": "py", "file_size_in_byte": 1723, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "ROOT.TFile", "line_number": 11, "usage_type": "call"}, {"api_name": "ROOT.TFile", "line_number": 12, "usage_type": "call"}, {"api_name": "ROOT.TFile", "line_number": 13, "usage_type": "call"}, {"api_name": "ROOT.TFile", "line_number": 14, "usage_type": "call"}, {"api_name": "ROOT.TFile", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}]} +{"seq_id": "602367783", "text": "#!/usr/local/bin ipython\n# encoding=gbk\n\n\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jan 23 16:00:13 2014\n\n@author: ruilei\n\"\"\"\n\ntry:\n import cPickle as pickle\nexcept:\n import pickle\nimport lz4\nimport date_converter\nimport numpy as np\nimport pandas as pd\n\n\n# from dateutil.parser import parse\n\n\ndef pksave(filename, data):\n \"\"\"\n save to pickle in lz format\n \"\"\"\n data_pickle = pickle.dumps(data, 1)\n data_lz = lz4.compress(data_pickle)\n f = open(filename, 'wb')\n f.write(data_lz)\n f.close()\n\n\ndef pkload(filename):\n \"\"\"\n load lz file\n \"\"\"\n f = open(filename, 'rb')\n data_lz = f.read()\n data_pickle = lz4.loads(data_lz)\n data = pickle.loads(data_pickle)\n f.close()\n return data\n\n\ndef tzsave(filename, data):\n \"\"\"\n save bin data file in tz format\n \"\"\"\n data_tz = lz4.compress(data)\n f = open(filename, 'wb')\n f.write(data_tz)\n f.close()\n\n\ndef binload(filename):\n f = open(filename, 'rb')\n data = f.read()\n f.close()\n return data\n\n\ndef bin2z(filename_bin, filename_tz):\n \"\"\"\n bin data file to tz file\n \"\"\"\n data = binload(filename_bin)\n tzsave(filename_tz, data)\n\n\ndef tzload(filename):\n \"\"\"\n load bz format data\n \"\"\"\n f = open(filename, 'rb')\n data_tz = f.read()\n data = lz4.loads(data_tz)\n f.close()\n return data\n\n\ndef ticks_df_tzload(filename):\n ticks = tzload(filename)\n dt = np.dtype([('nTime', np.int32), # 时间\n ('nIndex', np.int32), # 不加权指数\n ('nPrice', np.int32), # 成交价\n ('iVolume', np.int64), # 成交量\n ('iTurnover', np.int64), # 成交金额\n ('nMatchItems', np.int32), # 成交笔数\n ('nInterest', np.int32), # 持仓量\n ('chTradeFlag', (np.str_, 1)), # 成交标志\n ('chBSFlag', (np.str_, 1)), # BS标志\n ('iAccVolume', np.int64), # 当日累计成交量\n ('iAccTurover', np.int64), # 当日成交额(元)\n ('nAskPrice1', np.int32), ('nAskPrice2', np.int32), ('nAskPrice3', np.int32),\n ('nAskPrice4', np.int32), ('nAskPrice5', np.int32),\n ('nAskPrice6', np.int32), ('nAskPrice7', np.int32), ('nAskPrice8', np.int32),\n ('nAskPrice9', np.int32), ('nAskPrice10', np.int32),\n ('nAskVolume1', np.int32), ('nAskVolume2', np.int32), ('nAskVolume3', np.int32),\n ('nAskVolume4', np.int32), ('nAskVolume5', np.int32),\n ('nAskVolume6', np.int32), ('nAskVolume7', np.int32), ('nAskVolume8', np.int32),\n ('nAskVolume9', np.int32), ('nAskVolume10', np.int32),\n ('nBidPrice1', np.int32), ('nBidPrice2', np.int32), ('nBidPrice3', np.int32),\n ('nBidPrice4', np.int32), ('nBidPrice5', np.int32),\n ('nBidPrice6', np.int32), ('nBidPrice7', np.int32), ('nBidPrice8', np.int32),\n ('nBidPrice9', np.int32), ('nBidPrice10', np.int32),\n ('nBidVolume1', np.int32), ('nBidVolume2', np.int32), ('nBidVolume3', np.int32),\n ('nBidVolume4', np.int32), ('nBidVolume5', np.int32),\n ('nBidVolume6', np.int32), ('nBidVolume7', np.int32), ('nBidVolume8', np.int32),\n ('nBidVolume9', np.int32), ('nBidVolume10', np.int32)])\n\n data = pd.DataFrame(np.frombuffer(ticks[16::], dtype=dt))\n data.ix[:, [2] + range(11, 21) + range(31, 41)] /= 10000.\n data = data[(data.nTime >= 91400) & (data.nTime <= 160000)]\n\n nTime = data.nTime.values\n H = nTime / 10000\n M = nTime / 100 - H * 100\n S = nTime % 100\n ns = (H * 3600 + M * 60 + S)\n\n theDate = date_converter.string_to_timestamp(filename[-11:-3], '%Y%m%d') + 8 * 3600\n data.index = pd.to_datetime(theDate + ns, unit='s')\n # tic()\n # f = lambda x:parse(theDate + \" \" + str(x)[:-4] + \":\" + str(x)[-4:-2] + ':' + str(x)[-2:])\n # data.index = map(f, data.nTime)\n # toc()\n return data\n\n\ndef trans_df_tzload(filename):\n trans = tzload(filename)\n dt = np.dtype([('nTradeRef', np.int32),\n ('nTradeTime', np.int32),\n ('chTradeFlag', np.byte),\n ('chBSFlag', np.byte),\n ('nTradePrice', np.int32),\n ('nTradeVolume', np.int32)])\n\n data = pd.DataFrame(np.frombuffer(trans[16::], dtype=dt))\n data.ix[:, 4] /= 10000.\n return data\n\n\ndef ticks_df_save(filename_tz, filename_df):\n \"\"\"\n ticks data to dataframe\n \"\"\"\n pksave(filename_df, ticks_df_tzload(filename_tz))\n", "sub_path": "data_io/center_io.py", "file_name": "center_io.py", "file_ext": "py", "file_size_in_byte": 4674, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pickle.dumps", "line_number": 29, "usage_type": "call"}, {"api_name": "lz4.compress", "line_number": 30, "usage_type": "call"}, {"api_name": "lz4.loads", "line_number": 42, "usage_type": "call"}, {"api_name": "pickle.loads", "line_number": 43, "usage_type": "call"}, {"api_name": "lz4.compress", "line_number": 52, "usage_type": "call"}, {"api_name": "lz4.loads", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.dtype", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 86, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 87, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 88, "usage_type": "attribute"}, {"api_name": "numpy.int64", "line_number": 89, "usage_type": "attribute"}, {"api_name": "numpy.int64", "line_number": 90, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 91, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 92, "usage_type": "attribute"}, {"api_name": "numpy.str_", "line_number": 93, "usage_type": "attribute"}, {"api_name": "numpy.str_", "line_number": 94, "usage_type": "attribute"}, {"api_name": "numpy.int64", "line_number": 95, "usage_type": "attribute"}, {"api_name": "numpy.int64", "line_number": 96, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 97, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 98, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 99, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 100, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 101, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 102, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 103, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 104, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 105, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 106, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 107, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 108, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 109, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 110, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 111, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 112, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.frombuffer", "line_number": 114, "usage_type": "call"}, {"api_name": "date_converter.string_to_timestamp", "line_number": 124, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.dtype", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 135, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 136, "usage_type": "attribute"}, {"api_name": "numpy.byte", "line_number": 137, "usage_type": "attribute"}, {"api_name": "numpy.byte", "line_number": 138, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 139, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 140, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.frombuffer", "line_number": 142, "usage_type": "call"}]} +{"seq_id": "431001051", "text": "import torch\nimport time\nimport random\nimport numpy as np\nfrom args import args\nfrom model import sbm\nimport torch.nn.functional as F\nimport scipy.sparse as sp\nfrom appnp import GCN,MLP\nfrom appnp import APPNPModel,SpGAT,SGCN\nfrom compNB import return_opera\nfrom utils1 import (\n ensure_dir,\n init_out_dir,\n my_log,\n print_args,\n \n \n)\nfrom utils import load_data\n\n\ndef initCW(eps1,eps2,Q,c1,c2):\n C=torch.zeros([Q,Q]).cpu()\n W=torch.zeros([Q,Q]).cpu()\n cin=Q*c1/(1+(Q-1)*eps1)\n win=Q*c2/(1+(Q-1)*eps2)\n cou=cin*eps1\n wou=win*eps2\n for i in range(Q):\n for j in range(Q):\n if i==j:\n C[i][j]=cin\n W[i][j]=win\n else:\n C[i][j]=cou\n W[i][j]=wou\n return C,W\ndef normalize(mx):\n \"\"\"Row-normalize sparse matrix\"\"\"\n rowsum = np.array(mx.sum(1))\n r_inv = np.power(rowsum, -1).flatten()\n r_inv[np.isinf(r_inv)] = 0.\n r_mat_inv = sp.diags(r_inv)\n mx = r_mat_inv.dot(mx)\n return mx\n\n\ndef accuracy(output, labels):\n preds = output.max(1)[1].type_as(labels)\n correct = preds.eq(labels).double()\n correct = correct.sum()\n return correct / len(labels)\n\n\ndef sparse_mx_to_torch_sparse_tensor(sparse_mx):\n \"\"\"Convert a scipy sparse matrix to a torch sparse tensor.\"\"\"\n sparse_mx = sparse_mx.tocoo().astype(np.float32)\n indices = torch.from_numpy(\n np.vstack((sparse_mx.row, sparse_mx.col)).astype(np.int64))\n values = torch.from_numpy(sparse_mx.data)\n shape = torch.Size(sparse_mx.shape)\n return torch.sparse.FloatTensor(indices, values, shape)\n'''\npython main.py --nu 1000 --ne 1000 --c2 3 --seed_model 50000 --init_flag 2 --max_iter_time 500 --c1 3 --eps1 0.3 --eps2 0.48 --seed 26 --conv_crite 0.0001 --out_infix 00\n'''\n\ndef con_to_spars(B,E1,E2):\n return torch.sparse.FloatTensor(B.t(), torch.ones(B.shape[0]), torch.Size([E1.shape[0],E2.shape[0]])).to(args.device)\ndef con_to_spars1(B,n,E2):\n return torch.sparse.FloatTensor(B.t(), torch.ones(B.shape[0]), torch.Size([n,E2.shape[0]])).to(args.device)\n \ndef main():\n start_time= time.time()\n init_out_dir()\n print_args()\n \n \n model= sbm(**vars(args))\n \n E1,E2,E3,labels=model.generate_sbm()\n print(labels)\n adj=sp.coo_matrix((np.ones(E1.shape[0]),(E1[:,0],E1[:,1])),shape=(args.nu,args.nu))\n # print(adj)\n \n E2[:,1]=E2[:,1]-args.nu\n feature_indices=torch.from_numpy(E2).long().t()\n feature_values=torch.ones(E2.shape[0])\n #feature=torch.sparse.FloatTensor(torch.from_numpy(E2).long().t(),torch.ones(E2.shape[0]),torch.Size([args.nu,args.ne])).to_dense().to(args.device).float()\n \n randomindex=[i for i in range(args.nu)]\n \n num_train=int(args.nu*args.rho)\n random.shuffle(randomindex)\n # print(randomindex)\n idx_train=randomindex[0:num_train]\n idx_val=randomindex[num_train:num_train+500]\n idx_test=randomindex[num_train+500:args.nu]\n idx_train=torch.LongTensor(idx_train)\n idx_val=torch.LongTensor(idx_val)\n idx_test=torch.LongTensor(idx_test)\n \n idx_train=idx_train.to(args.device)\n idx_val=idx_val.to(args.device)\n idx_test=idx_test.to(args.device)\n if args.net == 'GCN':\n net = GCN(args,args.Q,args.ne,adj)\n elif args.net == 'appnp':\n net=APPNPModel(args,args.Q,args.ne,adj)\n elif args.net =='gat':\n net=SpGAT(args,args.Q,args.ne,adj)\n elif args.net==\"mlp\":\n net=MLP(args,args.Q,args.ne,adj)\n else:\n net=SGCN(args,args.Q,args.ne,adj)\n net.to(args.device) \n params = list(net.parameters())\n params = list(filter(lambda p: p.requires_grad, params))\n if args.optimizer == 'sgd':\n optimizer = torch.optim.SGD(params, lr=args.lr)\n elif args.optimizer == 'sgdm':\n optimizer = torch.optim.SGD(params, lr=args.lr, momentum=0.9)\n elif args.optimizer == 'rmsprop':\n optimizer = torch.optim.RMSprop(params, lr=args.lr, alpha=0.99)\n elif args.optimizer == 'adam':\n optimizer = torch.optim.Adam(params, lr=args.lr, weight_decay=args.wd)\n elif args.optimizer == 'adam0.5':\n optimizer = torch.optim.Adam(params, lr=args.lr, betas=(0.5, 0.999))\n else:\n raise ValueError('Unknown optimizer: {}'.format(args.optimizer))\n\n # Train model\n t_total = time.time()\n best_accuracy=0\n step_counter=0\n test_accu=0\n for epoch in range(args.epochs):\n accuracy=train(epoch,net,optimizer,feature_indices, feature_values,labels,idx_train,idx_val)\n if accuracy >=best_accuracy:\n best_accuracy=accuracy\n test_accu=test(net,feature_indices, feature_values,labels,idx_test)\n step_counter=0\n else:\n step_counter=step_counter+1\n if step_counter>args.early_stop:\n break \n \n print(\"Optimization Finished!\")\n print(\"Total time elapsed: {:.4f}s\".format(time.time() - t_total))\n\n # Testing\n test(net,feature_indices, feature_values,labels,idx_test)\n with open(args.fname, 'a', newline='\\n') as f:\n f.write('{} {:.3g} {:.3g} {:.3g} {:.3g}\\n'.format(\n args.eps1,\n args.eps2,\n args.c1,\n args.c2,\n test_accu\n )) \n\ndef test(net,feature_indices, feature_values,labels,idx_test):\n net.eval()\n output = net(feature_indices, feature_values)\n loss_test = F.nll_loss(output[idx_test], labels[idx_test])\n acc_test = accuracy(output[idx_test], labels[idx_test])\n print(\"Test set results:\",\n \"loss= {:.4f}\".format(loss_test.item()),\n \"accuracy= {:.4f}\".format(acc_test.item()))\n return acc_test\ndef train(epoch,net,optimizer,feature_indices, feature_values,labels,idx_train,idx_val):\n t = time.time()\n net.train()\n optimizer.zero_grad()\n output = net(feature_indices, feature_values)\n \n loss_train = F.nll_loss(output[idx_train], labels[idx_train])\n acc_train = accuracy(output[idx_train], labels[idx_train])\n loss_train.backward()\n optimizer.step()\n\n if not args.fastmode:\n # Evaluate validation set performance separately,\n # deactivates dropout during validation run.\n net.eval()\n output = net(feature_indices, feature_values)\n\n loss_val = F.nll_loss(output[idx_val], labels[idx_val])\n acc_val = accuracy(output[idx_val], labels[idx_val])\n print('Epoch: {:04d}'.format(epoch+1),\n 'loss_train: {:.4f}'.format(loss_train.item()),\n 'acc_train: {:.4f}'.format(acc_train.item()),\n 'loss_val: {:.4f}'.format(loss_val.item()),\n 'acc_val: {:.4f}'.format(acc_val.item()),\n 'time: {:.4f}s'.format(time.time() - t))\n\n return acc_val.item()\n\n\n\n\nif __name__ == '__main__':\n main()\n \n \n\n", "sub_path": "main_gcn.py", "file_name": "main_gcn.py", "file_ext": "py", "file_size_in_byte": 6771, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "torch.zeros", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.isinf", "line_number": 43, "usage_type": "call"}, {"api_name": "scipy.sparse.diags", "line_number": 44, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 44, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 58, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 60, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.Size", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.sparse.FloatTensor", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.sparse", "line_number": 63, "usage_type": "attribute"}, {"api_name": "torch.sparse.FloatTensor", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.sparse", "line_number": 69, "usage_type": "attribute"}, {"api_name": "torch.ones", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.Size", "line_number": 69, "usage_type": "call"}, {"api_name": "args.args.device", "line_number": 69, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.sparse.FloatTensor", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.sparse", "line_number": 71, "usage_type": "attribute"}, {"api_name": "torch.ones", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.Size", "line_number": 71, "usage_type": "call"}, {"api_name": "args.args.device", "line_number": 71, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 71, "usage_type": "name"}, {"api_name": "time.time", "line_number": 74, "usage_type": "call"}, {"api_name": "utils1.init_out_dir", "line_number": 75, "usage_type": "call"}, {"api_name": "utils1.print_args", "line_number": 76, "usage_type": "call"}, {"api_name": "model.sbm", "line_number": 79, "usage_type": "call"}, {"api_name": "args.args", "line_number": 79, "usage_type": "argument"}, {"api_name": "model.generate_sbm", "line_number": 81, "usage_type": "call"}, {"api_name": "scipy.sparse.coo_matrix", "line_number": 83, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 83, "usage_type": "name"}, {"api_name": "numpy.ones", "line_number": 83, "usage_type": "call"}, {"api_name": "args.args.nu", "line_number": 83, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 83, "usage_type": "name"}, {"api_name": "args.args.nu", "line_number": 86, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 86, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 88, "usage_type": "call"}, {"api_name": "args.args.nu", "line_number": 91, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 91, "usage_type": "name"}, {"api_name": "args.args.nu", "line_number": 93, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 93, "usage_type": "name"}, {"api_name": "args.args.rho", "line_number": 93, "usage_type": "attribute"}, {"api_name": "random.shuffle", "line_number": 94, "usage_type": "call"}, {"api_name": "args.args.nu", "line_number": 98, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 98, "usage_type": "name"}, {"api_name": "torch.LongTensor", "line_number": 99, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 101, "usage_type": "call"}, {"api_name": "args.args.device", "line_number": 103, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 103, "usage_type": "name"}, {"api_name": "args.args.device", "line_number": 104, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 104, "usage_type": "name"}, {"api_name": "args.args.device", "line_number": 105, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 105, "usage_type": "name"}, {"api_name": "args.args.net", "line_number": 106, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 106, "usage_type": "name"}, {"api_name": "appnp.GCN", "line_number": 107, "usage_type": "call"}, {"api_name": "args.args", "line_number": 107, "usage_type": "argument"}, {"api_name": "args.args.Q", "line_number": 107, "usage_type": "attribute"}, {"api_name": "args.args.ne", "line_number": 107, "usage_type": "attribute"}, {"api_name": "args.args.net", "line_number": 108, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 108, "usage_type": "name"}, {"api_name": "appnp.APPNPModel", "line_number": 109, "usage_type": "call"}, {"api_name": "args.args", "line_number": 109, "usage_type": "argument"}, {"api_name": "args.args.Q", "line_number": 109, "usage_type": "attribute"}, {"api_name": "args.args.ne", "line_number": 109, "usage_type": "attribute"}, {"api_name": "args.args.net", "line_number": 110, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 110, "usage_type": "name"}, {"api_name": "appnp.SpGAT", "line_number": 111, "usage_type": "call"}, {"api_name": "args.args", "line_number": 111, "usage_type": "argument"}, {"api_name": "args.args.Q", "line_number": 111, "usage_type": "attribute"}, {"api_name": "args.args.ne", "line_number": 111, "usage_type": "attribute"}, {"api_name": "args.args.net", "line_number": 112, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 112, "usage_type": "name"}, {"api_name": "appnp.MLP", "line_number": 113, "usage_type": "call"}, {"api_name": "args.args", "line_number": 113, "usage_type": "argument"}, {"api_name": "args.args.Q", "line_number": 113, "usage_type": "attribute"}, {"api_name": "args.args.ne", "line_number": 113, "usage_type": "attribute"}, {"api_name": "appnp.SGCN", "line_number": 115, "usage_type": "call"}, {"api_name": "args.args", "line_number": 115, "usage_type": "argument"}, {"api_name": "args.args.Q", "line_number": 115, "usage_type": "attribute"}, {"api_name": "args.args.ne", "line_number": 115, "usage_type": "attribute"}, {"api_name": "args.args.device", "line_number": 116, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 116, "usage_type": "name"}, {"api_name": "args.args.optimizer", "line_number": 119, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 119, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 120, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 120, "usage_type": "attribute"}, {"api_name": "args.args.lr", "line_number": 120, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 120, "usage_type": "name"}, {"api_name": "args.args.optimizer", "line_number": 121, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 121, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 122, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 122, "usage_type": "attribute"}, {"api_name": "args.args.lr", "line_number": 122, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 122, "usage_type": "name"}, {"api_name": "args.args.optimizer", "line_number": 123, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 123, "usage_type": "name"}, {"api_name": "torch.optim.RMSprop", "line_number": 124, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 124, "usage_type": "attribute"}, {"api_name": "args.args.lr", "line_number": 124, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 124, "usage_type": "name"}, {"api_name": "args.args.optimizer", "line_number": 125, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 125, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 126, "usage_type": "attribute"}, {"api_name": "args.args.lr", "line_number": 126, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 126, "usage_type": "name"}, {"api_name": "args.args.wd", "line_number": 126, "usage_type": "attribute"}, {"api_name": "args.args.optimizer", "line_number": 127, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 127, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 128, "usage_type": "attribute"}, {"api_name": "args.args.lr", "line_number": 128, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 128, "usage_type": "name"}, {"api_name": "args.args.optimizer", "line_number": 130, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 130, "usage_type": "name"}, {"api_name": "time.time", "line_number": 133, "usage_type": "call"}, {"api_name": "args.args.epochs", "line_number": 137, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 137, "usage_type": "name"}, {"api_name": "args.args.early_stop", "line_number": 145, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 145, "usage_type": "name"}, {"api_name": "time.time", "line_number": 149, "usage_type": "call"}, {"api_name": "args.args.fname", "line_number": 153, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 153, "usage_type": "name"}, {"api_name": "args.args.eps1", "line_number": 155, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 155, "usage_type": "name"}, {"api_name": "args.args.eps2", "line_number": 156, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 156, "usage_type": "name"}, {"api_name": "args.args.c1", "line_number": 157, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 157, "usage_type": "name"}, {"api_name": "args.args.c2", "line_number": 158, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 158, "usage_type": "name"}, {"api_name": "torch.nn.functional.nll_loss", "line_number": 165, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 165, "usage_type": "name"}, {"api_name": "time.time", "line_number": 172, "usage_type": "call"}, {"api_name": "torch.nn.functional.nll_loss", "line_number": 177, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 177, "usage_type": "name"}, {"api_name": "args.args.fastmode", "line_number": 182, "usage_type": "attribute"}, {"api_name": "args.args", "line_number": 182, "usage_type": "name"}, {"api_name": "torch.nn.functional.nll_loss", "line_number": 188, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 188, "usage_type": "name"}, {"api_name": "time.time", "line_number": 195, "usage_type": "call"}]} +{"seq_id": "46693231", "text": "from django.db import models\nfrom django.utils.translation import ugettext as _\nfrom django_extensions.db.fields import (AutoSlugField, CreationDateTimeField,\n ModificationDateTimeField)\n\n\nclass Resource(models.Model):\n \"\"\"List of resources\"\"\"\n MENTOR = 1\n LEARNING = 2\n GENI = 3\n RESOURCE_CHOICES = (\n (MENTOR, _('Mentor')),\n (LEARNING, _('Learning Labs/ Webcasts')),\n (GENI, _('Geni Resources')),\n )\n PUBLISHED = 1\n HIDDEN = 2\n STATUS_CHOICES = (\n (PUBLISHED, _('Published')),\n (HIDDEN, _('Hidden')),\n )\n title = models.CharField(max_length=255)\n slug = AutoSlugField(populate_from='title')\n resource_type = models.IntegerField(choices=RESOURCE_CHOICES)\n body = models.TextField()\n url = models.URLField(verify_exists=False, max_length=500, blank=True)\n email = models.EmailField(max_length=150, blank=True)\n is_featured = models.BooleanField(default=False)\n status = models.IntegerField(choices=STATUS_CHOICES, default=PUBLISHED)\n created = CreationDateTimeField()\n updated = ModificationDateTimeField()\n\n def __unicode__(self):\n return self.title\n", "sub_path": "apps/resources/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 1204, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.db.models.Model", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 7, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 13, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 14, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 15, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 20, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django_extensions.db.fields.AutoSlugField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models.IntegerField", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.models.URLField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.models.EmailField", "line_number": 28, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 28, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 30, "usage_type": "name"}, {"api_name": "django_extensions.db.fields.CreationDateTimeField", "line_number": 31, "usage_type": "call"}, {"api_name": "django_extensions.db.fields.ModificationDateTimeField", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "579249418", "text": "from django.conf.urls import url\n\nfrom . import views\nfrom django.urls import reverse\napp_name = 'self'\nurlpatterns = [\n url(r'^$', views.selfInfo, name='selfInfo'),\n url(r'^addObserver/$', views.addObserver, name='addObserver'),\n url(r'^register/$', views.register, name='register'),\n url(r'^login/$', views.login, name='login'),\n url(r'^logout/$', views.logout, name='logout'),\n url(r'^toLogin/$', views.toLogin, name='toLogin'),\n url(r'^selfCenter/$', views.selfCenter, name='selfCenter'),\n url(r'^updateToDB/$', views.updateToDB, name='updateToDB'),\n url(r'^registerToDB/$', views.registerToDB, name='registerToDB'),\n url(r'^deleteObserver/(?P[0-9]+)/$', views.deleteObserver, name='deleteObserver'),\n url(r'^getFrequencyList/$', views.getFrequencyList, name='getFrequencyList'),\n url(r'^updateFrequency/$', views.updateFrequency, name='updateFrequency'),\n\n]", "sub_path": "weiboWeb0403/selfInfo/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 902, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "605101746", "text": "import os\nfrom setuptools import setup\n\ndef read(filename):\n with open(os.path.join(os.path.dirname(__file__), filename)) as f:\n return f.read()\n\nsetup(name='genbox',\n description='input data generator for ``boxmox``',\n long_description=read('README.rst') + '\\n\\n' + read('INSTALL.rst') + '\\n\\n' + read('CHANGES.rst'),\n version='1.0.0',\n url='https://boxmodeling.meteo.physik.uni-muenchen.de',\n author='Christoph Knote',\n author_email='christoph.knote@physik.uni-muenchen.de',\n license='GPLv3',\n classifiers=[\n 'Development Status :: 5 - Production/Stable',\n 'Environment :: Console',\n 'Intended Audience :: Developers',\n 'Intended Audience :: Education',\n 'Intended Audience :: End Users/Desktop',\n 'Intended Audience :: Science/Research',\n 'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',\n 'Operating System :: POSIX',\n 'Programming Language :: Python :: 2 :: Only',\n 'Topic :: Education',\n 'Topic :: Scientific/Engineering',\n 'Topic :: Utilities'\n ],\n keywords='',\n python_requires='<3',\n packages=['genbox'],\n install_requires=['numpy', 'tuv','frappedata', 'chemspectranslator', 'boxmox'],\n entry_points={\n 'console_scripts':[\n 'make_BOXMOX_environment = genbox._console:makeEnvironment',\n 'make_BOXMOX_initialConditions = genbox._console:makeInitialConditions',\n 'make_BOXMOX_photolysisRates = genbox._console:makePhotolysisRates'\n ]\n },\n include_package_data=True,\n zip_safe=False)\n", "sub_path": "pypi_install_script/genbox-1.0.0.tar/setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1701, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.path.join", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 5, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 8, "usage_type": "call"}]} +{"seq_id": "444204949", "text": "from django.test.client import RequestFactory\n\nfrom cms.conf import settings\nfrom cms.regions import set_region, get_region, get_region_finder, _region\nfrom cms.regions.finder import URLFinder, DomainFinder\nfrom cms.tests.testcases import CMSTestCase\n\n\nclass RegionTest(CMSTestCase):\n def test_set_region(self):\n set_region(self.region)\n region = _region.value\n self.assertEqual(self.region, region)\n\n def test_get_region(self):\n _region.value = self.region\n region = get_region()\n self.assertEqual(self.region, region)\n\n def test_get_region_finder(self):\n settings.REGION_FINDER = 'cms.regions.finder.URLFinder'\n finder = get_region_finder()\n self.assertEqual('URLFinder', finder.__class__.__name__)\n\n\nclass URLFinderTest(CMSTestCase):\n def test_url_finder(self):\n self.factory = RequestFactory()\n request = self.factory.get('/%s/path/to/view' % self.region.abbr)\n finder = URLFinder()\n finder.definition_region(request)\n self.assertEqual(self.region, get_region())\n\n\nclass DomainFinderTest(CMSTestCase):\n def test_domain_finder(self):\n self.factory = RequestFactory()\n request = self.factory.get('/')\n request.get_host = lambda: '%s.test.lh' % self.region.abbr\n finder = DomainFinder()\n finder.definition_region(request)\n self.assertEqual(self.region, get_region())\n", "sub_path": "cms/tests/regions/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 1423, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "cms.tests.testcases.CMSTestCase", "line_number": 9, "usage_type": "name"}, {"api_name": "cms.regions.set_region", "line_number": 11, "usage_type": "call"}, {"api_name": "cms.regions._region.value", "line_number": 12, "usage_type": "attribute"}, {"api_name": "cms.regions._region", "line_number": 12, "usage_type": "name"}, {"api_name": "cms.regions._region.value", "line_number": 16, "usage_type": "attribute"}, {"api_name": "cms.regions._region", "line_number": 16, "usage_type": "name"}, {"api_name": "cms.regions.get_region", "line_number": 17, "usage_type": "call"}, {"api_name": "cms.conf.settings.REGION_FINDER", "line_number": 21, "usage_type": "attribute"}, {"api_name": "cms.conf.settings", "line_number": 21, "usage_type": "name"}, {"api_name": "cms.regions.get_region_finder", "line_number": 22, "usage_type": "call"}, {"api_name": "cms.tests.testcases.CMSTestCase", "line_number": 26, "usage_type": "name"}, {"api_name": "django.test.client.RequestFactory", "line_number": 28, "usage_type": "call"}, {"api_name": "cms.regions.finder.URLFinder", "line_number": 30, "usage_type": "call"}, {"api_name": "cms.regions.get_region", "line_number": 32, "usage_type": "call"}, {"api_name": "cms.tests.testcases.CMSTestCase", "line_number": 35, "usage_type": "name"}, {"api_name": "django.test.client.RequestFactory", "line_number": 37, "usage_type": "call"}, {"api_name": "cms.regions.finder.DomainFinder", "line_number": 40, "usage_type": "call"}, {"api_name": "cms.regions.get_region", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "571455408", "text": "import math, pygame\nfrom drawable import Drawable\nfrom resources import resolution\n\n\nclass Man(Drawable):\n def __init__(self, image, player):\n Drawable.__init__(self, image, player)\n self.__speed = .2\n\n def update(self, input_dict, dt=0):\n # get angle\n mx, my = pygame.mouse.get_pos()\n angle = -math.degrees(math.atan2(my+25-resolution[1]/2, mx+25-resolution[0]/2))\n self._surface = pygame.transform.rotate(self._original_surface, angle - 90)\n\n x, y = self.pos\n if input_dict[\"up\"]:\n y -= self.__speed * dt\n\n if input_dict[\"down\"]:\n y += self.__speed * dt\n\n if input_dict[\"left\"]:\n x -= self.__speed * dt\n\n if input_dict[\"right\"]:\n x += self.__speed * dt\n\n self.pos = (x, y)\n", "sub_path": "core/man.py", "file_name": "man.py", "file_ext": "py", "file_size_in_byte": 809, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "drawable.Drawable", "line_number": 6, "usage_type": "name"}, {"api_name": "drawable.Drawable.__init__", "line_number": 8, "usage_type": "call"}, {"api_name": "drawable.Drawable", "line_number": 8, "usage_type": "name"}, {"api_name": "pygame.mouse.get_pos", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 13, "usage_type": "attribute"}, {"api_name": "math.degrees", "line_number": 14, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 14, "usage_type": "call"}, {"api_name": "resources.resolution", "line_number": 14, "usage_type": "name"}, {"api_name": "pygame.transform.rotate", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 15, "usage_type": "attribute"}]} +{"seq_id": "117026705", "text": "# -*- coding: utf-8 -*-\nfrom PyQt5 import QtWidgets as QtGui\nfrom PyQt5 import QtCore\nfrom PyQt5.QtGui import *\nimport os,datetime\nimport sys\nimport sip\nimport _thread\nimport subprocess\nfrom multiprocessing import Process\nfrom shutdown import LoginDialog\nimport numpy as np\n#from pic import src\nimport pythoncom\nfrom win32com.shell import shell\nimport locale\nimport win32timezone\nimport random\nimport win32api\nroot = os.path.dirname(__file__)\n\n\nprint (root)\nos.chdir(root)\nspeed = 360\n\nclass circle_label(QtGui.QLabel):\n def __init__(self,parent=None):\n QtGui.QLabel.__init__(self, parent)\n self._parent = parent\n\n self.time_num=0\n \n \n self.setWindowTitle(\"Tray!\")\n self.setAcceptDrops(True)\n self.setWindowFlags(QtCore.Qt.WindowStaysOnTopHint)\n self.setAttribute(QtCore.Qt.WA_TranslucentBackground)\n self.setWindowFlags(QtCore.Qt.FramelessWindowHint | QtCore.Qt.Tool)\n self.setAttribute(QtCore.Qt.WA_TranslucentBackground);\n self.setWindowOpacity(1)\n self._time=QtCore.QTimer()\n self._time.start(10) \n self.pic=QPixmap('pic/circle.png')\n self.setPixmap(self.pic)\n self.setMask(self.pic.mask())\n self.setAlignment(QtCore.Qt.AlignCenter)\n self._time.timeout.connect(self.change_position)\n\n def mousePressEvent(self,event):\n if event.button()==QtCore.Qt.LeftButton:\n self.dragPosition=event.globalPos()-self.frameGeometry().topLeft()\n event.accept()\n if event.button()==QtCore.Qt.RightButton:\n pass\n def mouseMoveEvent(self,event):\n if event.buttons()& QtCore.Qt.LeftButton:\n self.move(event.globalPos()-self.dragPosition)\n event.accept()\n def change_position(self):\n r = self._parent.height()/1.2\n a = (self._parent.pos().x()+ self._parent.width()/2)\n b = (self._parent.pos().y() +self._parent.height()/2.5)\n\n pos_data= ((speed-self.time_num)/speed)*2*np.pi\n self.time_num+=1\n\n if self.time_num>=speed:\n self.time_num=0\n \n x = a + r * np.cos(pos_data)\n y = b + r * np.sin(pos_data)\n self.move(x,y)\n\nclass custdom_label(QtGui.QLabel):\n def __init__(self,text=\"\",parent=None):\n QtGui.QLabel.__init__(self, parent)\n\n self.setWindowTitle(\"Tray!\")\n self.setAcceptDrops(True)\n self.setWindowFlags(QtCore.Qt.WindowStaysOnTopHint)\n self.setAttribute(QtCore.Qt.WA_TranslucentBackground)\n self.setWindowFlags(QtCore.Qt.FramelessWindowHint | QtCore.Qt.Tool)\n self.setAttribute(QtCore.Qt.WA_TranslucentBackground);\n self.setWindowOpacity(1)\n \n \n self.setAlignment(QtCore.Qt.AlignCenter)\n font = QFont()\n color = \"color:white\"\n self.setStyleSheet(color)\n #font.setFamily(\"Arial\") \n font.setPointSize(28) \n self.setFont(font)\n self.setText(text)\n \n self.resize(1920,300)\n self.show()\n def mousePressEvent(self,event):\n if event.button()==QtCore.Qt.LeftButton:\n self.dragPosition=event.globalPos()-self.frameGeometry().topLeft()\n event.accept()\n if event.button()==QtCore.Qt.RightButton:\n pass\n def mouseMoveEvent(self,event):\n if event.buttons()& QtCore.Qt.LeftButton:\n self.move(event.globalPos()-self.dragPosition)\n event.accept()\n\n \nclass Tray_(QtGui.QLabel):\n def __init__(self, parent=None):\n QtGui.QLabel.__init__(self, parent)\n self._pic_vaule = 1\n self.zoomscale=1\n self.contorl=1\n self.contorl_=1\n self._change_pic=1\n self.change_switch = 0\n self.move_value=0\n \n self.setWindowTitle(\"Tray!\")\n self.setAcceptDrops(True)\n self.setWindowFlags(QtCore.Qt.WindowStaysOnTopHint)\n self.setAttribute(QtCore.Qt.WA_TranslucentBackground)\n self.setWindowFlags(QtCore.Qt.FramelessWindowHint | QtCore.Qt.Tool)\n self.setAttribute(QtCore.Qt.WA_TranslucentBackground);\n self.setWindowOpacity(1)\n self._time=QtCore.QTimer()\n \n self._image=QtGui.QLabel()\n self._pic =QPixmap('pic/pic1.png')\n self._image.setPixmap(self._pic)\n self._image.setMask(self._pic.mask())\n #self._image.setMask(self._pic.mask())\n \n self.layout = QtGui.QVBoxLayout()\n self.layout.addWidget(self._image)\n self.setLayout(self.layout) \n \n\n self._time.timeout.connect(self.shut_down)\n self._image.setScaledContents(True) \n self._image.setAlignment(QtCore.Qt.AlignCenter)\n self.dragPosition=None\n self.createContextMenu() \n \n self._time.start(600000)\n\n \n self.resize(100,100)\n self.show()\n \n def createContextMenu(self): \n self.setContextMenuPolicy(QtCore.Qt.CustomContextMenu) \n self.customContextMenuRequested.connect(self.showContextMenu) \n self.contextMenu = QtGui.QMenu(self) \n self.actionA = self.contextMenu.addAction(u'执行exe')\n self.actionW = self.contextMenu.addAction(u'web')\n self.actionN = self.contextMenu.addAction(u'vpn')\n self.jupyterlab= self.contextMenu.addAction(u'jupyterlab') \n self.BlackBird= self.contextMenu.addAction(u'BlackBird') \n self.actionB = self.contextMenu.addAction(u'关机') \n self.actionS = self.contextMenu.addAction(u'Timer') \n self.actionC = self.contextMenu.addAction(u'关闭') \n self.actionH = self.contextMenu.addAction(u'环绕') \n self.actionRoot = self.contextMenu.addAction(u'root') \n self.actionA.triggered.connect(self.action_add) \n self.actionB.triggered.connect(self.action_del) \n self.actionC.triggered.connect(self.action_close)\n self.actionN.triggered.connect(self.action_vpn)\n self.actionW.triggered.connect(self.action_web)\n self.actionH.triggered.connect(self.circle_round)\n self.jupyterlab.triggered.connect(self.jupyter)\n self.BlackBird.triggered.connect(self.blackBird)\n self.actionS.triggered.connect(self.close_timer)\n self.actionRoot.triggered.connect(self.action_root)\n def circle_round(self):\n if self.contorl_==1:\n self.contorl_=0\n self.app = circle_label(self)\n self.app.show()\n else:\n self.contorl_=1\n if self.app!=None:\n self.app.close()\n \n def action_root(self):\n root = \"D:\\\\software\\\\Python3.7\\\\Lib\\\\site-packages\"\n #os.startfile(root)\n if not os.path.exists(root):\n return\n else:\n win32api.ShellExecute(None, \"open\", \"explorer.exe\", \"/select, \"+root+\"\\\\tensorflow\",None, 1);\n def close_timer(self):\n \n if self.change_switch == 1:\n self._time.start(60000)\n self.change_switch = 0\n else:\n self._time.start(200)\n self.change_switch = 1\n def shut_down(self):\n current_time = str(datetime.datetime.strftime(datetime.datetime.now(),'%H:%M:%S'))\n compare_time = \"23:10:00\"\n next_time = \"23:11:00\"\n \n \n if self.change_switch == 1:\n r = 400 \n a=800\n b=300\n \n current_minutes = int(datetime.datetime.strftime(datetime.datetime.now(),'%M'))\n pos_data= (current_minutes/60)*2*np.pi\n x = a + r * np.cos(pos_data)\n y = b + r * np.sin(pos_data)\n self.move(x,y)\n if current_time>compare_time and current_time < next_time and self.change_switch == 0: \n cmd=\"shutdown -s -t 30\"\n self.change_switch == 0\n subprocess.Popen(cmd, shell=True)\n\n def change_pic(self):\n if self._pic_vaule <5:\n self._pic_vaule+=1\n file_pic = 'pic/pic'+str(self._pic_vaule)+'.png'\n else:\n self._pic_vaule = 1\n file_pic = 'pic/pic'+str(self._pic_vaule)+'.png'\n \n \n #self.layout.removeWidget(self._image)\n \n self._image.deleteLater()\n sip.delete(self.layout)\n \n self._image=QtGui.QLabel()\n self._pic = QPixmap(file_pic)\n self._image.setPixmap(self._pic)\n self._image.setMask(self._pic.mask())\n self._image.setMask(self._pic.mask())\n \n self.layout = QtGui.QVBoxLayout()\n self.layout.addWidget(self._image)\n self.setLayout(self.layout) \n \n def showContextMenu(self, pos): \n self.contextMenu.move(self.pos() + pos) \n self.contextMenu.show() \n def jupyter(self):\n cmd = u\"jupyter notebook\"\n subprocess.Popen(cmd, shell=True)\n def blackBird(self):\n root = \"D:/local_software/software/BlackBird-Player/playlist/qita\"\n if not os.path.exists(root):\n QtGui.QMessageBox.information(self,u\"提示\", u\"您的电脑未配置环境！\")\n return\n else:\n os.chdir(root)\n cmd = \"python2 update.py\"\n subprocess.Popen(cmd, shell=True)\n def action_vpn(self):\n root = \"D:/file/other/google/ChromeGo\"\n if not os.path.exists(root):\n QtGui.QMessageBox.information(self,u\"提示\", u\"您的电脑未配置环境！\")\n return\n cmd = u\"call \\\"D:/file/other/google/ChromeGo/7.SSR翻墙.cmd\\\"\"\n subprocess.Popen(cmd, shell=True)\n def action_web(self):\n root = \"D:/other/website\"\n if not os.path.exists(root):\n QtGui.QMessageBox.information(self,u\"提示\", u\"您的电脑未配置环境！\")\n return\n else:\n os.chdir(root)\n cmd = u\"python2 D:/other/website/run.py\"\n subprocess.Popen(cmd, shell=True)\n def action_add(self):\n root=\"D:/software/player\"\n if not os.path.exists(root):\n QtGui.QMessageBox.information(self,u\"提示\", u\"您的电脑未配置环境！\")\n return\n else:\n os.chdir(root)\n cmd = \"start h-player.exe\"\n subprocess.Popen(cmd, shell=True)\n def action_del(self):\n if self.contorl == True:\n dialog = LoginDialog(self)\n dialog.show()\n self.contorl=0\n else:\n cmd=\"shutdown -a\"\n self.contorl=1\n subprocess.Popen(cmd, shell=True)\n def action_close(self):\n if self.contorl_==0:\n self.app.close()\n self.close()\n def play_audio(self,file,txt):\n from playsound import playsound\n text_label = custdom_label(txt,self)\n playsound(file)\n text_label.close()\n def mouseDoubleClickEvent(self, event):\n if event.buttons () == QtCore.Qt.LeftButton: \n #self.change_pic()\n x = ( event.pos().x()) \n y = ( event.pos().y()) \n print (x,y)\n if ((x>=205 and x<=260) and (y>=38 and y<=80)): \n self.change_pic()\n else:\n root = os.listdir(\"wav_jp/\")\n if root != []:\n file = \"wav_jp/\"+random.choice(root)\n else:\n root = os.listdir(\"wav_en/\")\n file = \"wav_en/\"+random.choice(root)\n with open(\"txt/data.txt\",\"r\",encoding = \"shift-jis\") as f:\n data = f.readlines()\n \n txt = random.choice(data)\n _thread.start_new_thread( self.play_audio, (file,txt, ) )\n \n \n \n \n def mousePressEvent(self,event):\n if event.button()==QtCore.Qt.LeftButton:\n self.dragPosition=event.globalPos()-self.frameGeometry().topLeft()\n event.accept()\n if event.button()==QtCore.Qt.RightButton:\n pass\n def mouseMoveEvent(self,event):\n if event.buttons()& QtCore.Qt.LeftButton:\n self.move(event.globalPos()-self.dragPosition)\n event.accept() \n def func(self,cmd):\n subprocess.Popen(cmd, shell=True)\n def login(self,path):\n\n if not os.path.exists(\"D:/software/python2.7\"):\n gettime= (\"\\\"\"+ path +\"\\\"\")\n cmd=r\"python \"+(gettime)\n subprocess.Popen(cmd, shell=True)\n else:\n gettime= (\"\\\"\"+ path +\"\\\"\")\n try:\n cmd=r\"python2 \"+(gettime)\n subprocess.Popen(cmd, shell=True)\n except Exception as e:\n print (e)\n def dragEnterEvent( self, event ):\n data = event.mimeData()\n urls = data.urls()\n if ( urls and urls[0].scheme() == 'file' ):\n event.acceptProposedAction()\n def dragMoveEvent( self, event ):\n data = event.mimeData()\n urls = data.urls()\n if ( urls and urls[0].scheme() == 'file' ):\n event.acceptProposedAction()\n def dropEvent( self, event ):\n data = event.mimeData()\n urls = data.urls()\n if ( urls and urls[0].scheme() == 'file' ):\n filepath = str(urls[0].path())[1:]\n if \".\" not in filepath:\n QtGui.QMessageBox.information(self,u\"提示\", u\"不是文件！\")\n elif (filepath.split(\".\")[-1]==\"py\" or filepath.split(\".\")[-1]==\"pyw\"):\n self.login(filepath )\n elif filepath.split(\".\")[-1]==\"lnk\":\n file_path=self.getShortcutRealPath(filepath)\n print (file_path)\n if (file_path.split(\".\")[-1]==\"py\" or file_path.split(\".\")[-1]==\"pyw\"):\n self.login(file_path )\n else:\n QtGui.QMessageBox.information(self,u\"提示\", u\"文件不符合要求！\")\n else:\n QtGui.QMessageBox.information(self,u\"提示\", u\"文件不符合要求！\") \n def getShortcutRealPath(self,filePath):\n \n pythoncom.CoInitialize()\n shortcut = pythoncom.CoCreateInstance(\n shell.CLSID_ShellLink, None,\n pythoncom.CLSCTX_INPROC_SERVER, shell.IID_IShellLink)\n shortcut.QueryInterface(pythoncom.IID_IPersistFile).Load(filePath)\n fileRealPath = shortcut.GetPath(shell.SLGP_UNCPRIORITY)[0]\n fileRealPath = fileRealPath\n print (fileRealPath)\n return fileRealPath\nif __name__ == '__main__':\n import sys\n app = QtGui.QApplication(sys.argv)\n dialog = Tray_()\n dialog.show()\n sys.exit(app.exec_())\n \n\n", "sub_path": "tray1.py", "file_name": "tray1.py", "file_ext": "py", "file_size_in_byte": 14649, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "os.path.dirname", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 24, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 27, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 27, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel.__init__", "line_number": 29, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 29, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 29, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 37, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 37, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 38, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 38, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 39, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 39, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 40, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 40, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QTimer", "line_number": 42, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 42, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 47, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 47, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 51, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 51, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 54, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 54, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 57, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 57, "usage_type": "name"}, {"api_name": "numpy.pi", "line_number": 65, "usage_type": "attribute"}, {"api_name": "numpy.cos", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 72, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 75, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 75, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel.__init__", "line_number": 77, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 77, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 77, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 81, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 81, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 82, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 82, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 83, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 83, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 84, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 84, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 88, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 88, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 100, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 100, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 103, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 103, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 106, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 106, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 111, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 111, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel.__init__", "line_number": 113, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 113, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 113, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 124, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 124, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 125, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 125, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 126, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 126, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 127, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 127, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QTimer", "line_number": 129, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 129, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 131, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 131, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 137, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 137, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 144, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 144, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 155, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 155, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMenu", "line_number": 157, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 157, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 191, "usage_type": "call"}, {"api_name": "os.path", "line_number": 191, "usage_type": "attribute"}, {"api_name": "win32api.ShellExecute", "line_number": 194, "usage_type": "call"}, {"api_name": "datetime.datetime.strftime", "line_number": 204, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 204, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 204, "usage_type": "call"}, {"api_name": "datetime.datetime.strftime", "line_number": 214, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 214, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 214, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 215, "usage_type": "attribute"}, {"api_name": "numpy.cos", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 217, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 222, "usage_type": "call"}, {"api_name": "sip.delete", "line_number": 236, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 238, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 238, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 244, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 244, "usage_type": "name"}, {"api_name": "subprocess.Popen", "line_number": 253, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 256, "usage_type": "call"}, {"api_name": "os.path", "line_number": 256, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.information", "line_number": 257, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 257, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 257, "usage_type": "name"}, {"api_name": "os.chdir", "line_number": 260, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 262, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 265, "usage_type": "call"}, {"api_name": "os.path", "line_number": 265, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.information", "line_number": 266, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 266, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 266, "usage_type": "name"}, {"api_name": "subprocess.Popen", "line_number": 269, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 272, "usage_type": "call"}, {"api_name": "os.path", "line_number": 272, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.information", "line_number": 273, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 273, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 273, "usage_type": "name"}, {"api_name": "os.chdir", "line_number": 276, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 278, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 281, "usage_type": "call"}, {"api_name": "os.path", "line_number": 281, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.information", "line_number": 282, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 282, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 282, "usage_type": "name"}, {"api_name": "os.chdir", "line_number": 285, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 287, "usage_type": "call"}, {"api_name": "shutdown.LoginDialog", "line_number": 290, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 296, "usage_type": "call"}, {"api_name": "playsound.playsound", "line_number": 304, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 307, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 307, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 315, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 317, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 319, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 320, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 324, "usage_type": "call"}, {"api_name": "_thread.start_new_thread", "line_number": 325, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 331, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 331, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 334, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 334, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 337, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 337, "usage_type": "name"}, {"api_name": "subprocess.Popen", "line_number": 341, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 344, "usage_type": "call"}, {"api_name": "os.path", "line_number": 344, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 347, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 352, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.information", "line_number": 371, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 371, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 371, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.information", "line_number": 380, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 380, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 380, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.information", "line_number": 382, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 382, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 382, "usage_type": "name"}, {"api_name": "pythoncom.CoInitialize", "line_number": 385, "usage_type": "call"}, {"api_name": "pythoncom.CoCreateInstance", "line_number": 386, "usage_type": "call"}, {"api_name": "win32com.shell.shell.CLSID_ShellLink", "line_number": 387, "usage_type": "attribute"}, {"api_name": "win32com.shell.shell", "line_number": 387, "usage_type": "name"}, {"api_name": "pythoncom.CLSCTX_INPROC_SERVER", "line_number": 388, "usage_type": "attribute"}, {"api_name": "win32com.shell.shell.IID_IShellLink", "line_number": 388, "usage_type": "attribute"}, {"api_name": "win32com.shell.shell", "line_number": 388, "usage_type": "name"}, {"api_name": "pythoncom.IID_IPersistFile", "line_number": 389, "usage_type": "attribute"}, {"api_name": "win32com.shell.shell.SLGP_UNCPRIORITY", "line_number": 390, "usage_type": "attribute"}, {"api_name": "win32com.shell.shell", "line_number": 390, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 396, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 396, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 396, "usage_type": "attribute"}, {"api_name": "{'playsound': 'playsound.playsound'}", "line_number": 397, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 399, "usage_type": "call"}]} +{"seq_id": "459342644", "text": "# Use https://www.si.umich.edu/programs/bachelor-science-\n# information/bsi-admissions as a template.\n# STEPS \n# Create a similar HTML file but \n# 1) Replace every occurrence of the word “student” with “AMAZING\n# student.” \n# 2) Replace the main picture with a picture of yourself.\n# 3) Replace any local images with the image I provided in media. (You\n# must keep the image in a separate folder than your html code.\n\n# Deliverables\n# Make sure the new page is uploaded to your GitHub account.\nimport sys\nimport requests\nfrom bs4 import BeautifulSoup\nimport re\n\n\n\n\nbase_url = 'http://collemc.people.si.umich.edu/data/bshw3StarterFile.html'\nr = requests.get(base_url)\nsoup = BeautifulSoup(r.text, 'lxml')\n\nfor tag in soup.find_all(class_ = \"html not-front logged-in two-sidebars page-node page-node- page-node-11581 node-type-general-page section-programs\"):\n\tfor thing in tag(id = \"body-inside\"):\n\t\tfor close in thing(class_ = \"body-inside2\"):\n\t\t\tfor x in close(class_ = \"field field-name-body field-type-text-with-summary field-label-hidden\"):\n\t\t\t\tfor ima in x(class_ = \"field-item even\"):\n\t\t\t\t\tfor img in ima.find_all(\"img\"):\n\t\t\t\t\t\timg[\"src\"] = \"https://media.licdn.com/mpr/mpr/shrinknp_200_200/p/4/005/08e/305/09e69c2.jpg\"\n\nfor img in soup.find_all(\"img\"):\n\tif \"https:\" not in img['src']:\n\t\timg[\"src\"] = 'media/logo.png'\n\npret = soup.prettify()\npret = pret.replace(\"student\", \"AMAZING student\")\n\n\n\n\nhtm = open(\"proj3.html\", 'w')\nhtm.write(pret)\nhtm.close()\n\n\n", "sub_path": "HW3-StudentCopy/bshw3.py", "file_name": "bshw3.py", "file_ext": "py", "file_size_in_byte": 1472, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "requests.get", "line_number": 22, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "521695171", "text": "import pandas as pd\nfrom progressbar import ProgressBar\n\npbar = ProgressBar().start()\n\npath = 'Dataset/movies.csv'\n\ndf = pd.read_csv(path)\n\ni = 0\nadult = []\n\nfor a in df.adult:\n if a == 'FALSE':\n adult.append(str(0))\n else:\n adult.append(str(1))\n pbar.update()\n\nprint(adult)\ndf.adult = adult\ndf.to_csv('movies_metadata - Copy.csv')\npbar.finish()\n", "sub_path": "db_refiner_adult.py", "file_name": "db_refiner_adult.py", "file_ext": "py", "file_size_in_byte": 369, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "progressbar.ProgressBar", "line_number": 4, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 8, "usage_type": "call"}]} +{"seq_id": "603928012", "text": "import pygame, characters, translate\nimport numpy as np\n\npygame.init()\n\nBLACK = (0, 0, 0)\nWHITE = (255, 255, 255)\nBLUE = (0, 0, 255)\nGREEN = (0, 255, 0)\nRED = (255, 0, 0)\n\nsize = (400, 500)\nscreen = pygame.display.set_mode(size)\nscreen.fill(WHITE)\npygame.display.set_caption(\"Snail Run 3D\")\n\nbob = characters.Snail([0,0,0])\ndef draw_bob(woah):\n for cube,color in bob.return_sides():\n for side in cube:\n\n screen_position = np.array([1000,1000,woah + 0.01])\n screen_direction = np.array([-1,-1,-woah/1000 + 0.01])\n viewer_distance = 1000\n \n start = translate.translate(side[0], screen_position, screen_direction, viewer_distance)\n end = translate.translate(side[1], screen_position, screen_direction, viewer_distance)\n pygame.draw.line(screen, BLACK, (start[0]+200,-start[1]+250), (end[0]+200, -end[1]+250), 1)\n\ndef draw_bob_magic(woah):\n\n screen_position = np.array([1000,1000,woah + 0.01])\n screen_direction = np.array([-1,-1,-woah/1000 + 0.01])\n viewer_distance = 1000\n \n for cube, color in bob.return_faces(screen_position):\n for face in cube:\n\n points = []\n\n for point in face:\n point = translate.translate(point, screen_position, screen_direction, viewer_distance)\n point[0] += 200\n point[1] = -point[1] + 250\n points.append(point)\n \n pygame.draw.polygon(screen, color, points, 0)\n\ndef draw_axis(height):\n screen_position = np.array([1000,1000,height +0.01])\n screen_direction = np.array([-1,-1,-height/1000 + 0.01])\n viewer_distance = 1000\n \n origin = translate.translate(np.array([0,0,0]), screen_position, screen_direction, viewer_distance)\n\n x_axis = translate.translate(np.array([500,0,0]), screen_position, screen_direction, viewer_distance)\n y_axis = translate.translate(np.array([0,500,0]), screen_position, screen_direction, viewer_distance)\n z_axis = translate.translate(np.array([0,0,500]), screen_position, screen_direction, viewer_distance)\n\n #print(\"x, y, z\", x_axis, y_axis, z_axis)\n\n pygame.draw.line(screen, BLACK, (origin[0]+200,-origin[1]+250), (x_axis[0]+200, -x_axis[1]+250), 1)\n pygame.draw.line(screen, BLUE, (origin[0]+200,-origin[1]+250), (y_axis[0]+200, -y_axis[1]+250), 1)\n pygame.draw.line(screen, GREEN, (origin[0]+200,-origin[1]+250), (z_axis[0]+200, -z_axis[1]+250), 1)\n\nheight = 1000\nmoving_up = False\nmoving_down = False\n\nclock = pygame.time.Clock()\n\nrunning = True\nwhile running:\n\n clock.tick(50)\n for event in pygame.event.get():\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_UP:\n moving_up = True\n if event.key == pygame.K_DOWN:\n moving_down = True\n if event.type == pygame.KEYUP:\n if event.key == pygame.K_UP:\n moving_up = False\n if event.key == pygame.K_DOWN:\n moving_down = False\n if event.type == pygame.QUIT:\n running = False\n\n screen.fill((230,255,255))\n if moving_up:\n height += 50\n elif moving_down:\n height -= 50\n \n draw_axis(height)\n draw_bob_magic(height)\n draw_bob(height)\n pygame.display.flip()\n\npygame.quit()\n", "sub_path": "test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 3306, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pygame.init", "line_number": 4, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 15, "usage_type": "attribute"}, {"api_name": "characters.Snail", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 23, "usage_type": "call"}, {"api_name": "translate.translate", "line_number": 26, "usage_type": "call"}, {"api_name": "translate.translate", "line_number": 27, "usage_type": "call"}, {"api_name": "pygame.draw.line", "line_number": 28, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 28, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 33, "usage_type": "call"}, {"api_name": "translate.translate", "line_number": 42, "usage_type": "call"}, {"api_name": "pygame.draw.polygon", "line_number": 47, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 51, "usage_type": "call"}, {"api_name": "translate.translate", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 54, "usage_type": "call"}, {"api_name": "translate.translate", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 56, "usage_type": "call"}, {"api_name": "translate.translate", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 57, "usage_type": "call"}, {"api_name": "translate.translate", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 58, "usage_type": "call"}, {"api_name": "pygame.draw.line", "line_number": 62, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 62, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 63, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 63, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 64, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 64, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 70, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 70, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 76, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 76, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 77, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 78, "usage_type": "attribute"}, {"api_name": "pygame.K_DOWN", "line_number": 80, "usage_type": "attribute"}, {"api_name": "pygame.KEYUP", "line_number": 82, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 83, "usage_type": "attribute"}, {"api_name": "pygame.K_DOWN", "line_number": 85, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 87, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 99, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 99, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 101, "usage_type": "call"}]} +{"seq_id": "166765141", "text": "import argparse\r\nimport pandas as pd\r\nimport sys\r\n\r\n\r\n# this function parse arguments from command line\r\ndef parser():\r\n parser = argparse.ArgumentParser(description=\r\n 'It is a script for phylogenetic alignments'\r\n ' merging. Here it reads fasta files from IN'\r\n ' paths, merge it by name, and write to the'\r\n ' OUT file. Additionally you can create nexus'\r\n ' file with the length parameters of your'\r\n ' sequences.\\n\\n',\r\n epilog='Example of usage: '\r\n '')\r\n parser.add_argument('-in', nargs='*',\r\n help='paths to all alignment files in fasta format, which you want merge')\r\n parser.add_argument('-out', help='path to output fasta file with merged sequences')\r\n parser.add_argument('-nexus', help='path to output nexus file with length parameters of your sequences')\r\n args = parser.parse_args()\r\n return args\r\n\r\n\r\n# this function read and processed fasta-files from IN\r\ndef file_read(path):\r\n dna_list = {}\r\n with open(path, 'r') as file:\r\n for line in file:\r\n line = line.rstrip('\\n')\r\n if '>' in line:\r\n if line not in dna_list:\r\n dna_list[line] = str()\r\n temp_taxon = line\r\n else:\r\n print(path, 'contain duplicated name', line)\r\n raise NameError('DublicatedGenes')\r\n else:\r\n dna_list[temp_taxon] = dna_list[temp_taxon] + line\r\n dna_table = pd.DataFrame.from_dict(dna_list, orient='index')\r\n dna_table.reset_index(inplace=True)\r\n dna_table.columns = ['taxon', path.split('\\\\')[-1]]\r\n length_variables = dna_table[path.split('\\\\')[-1]].apply(len).unique()\r\n if len(length_variables) > 1:\r\n print(path, 'contain sequences with different length. May be you forgot align it?')\r\n raise NameError('NotAlignment')\r\n return dna_table\r\n\r\n\r\nargs = parser()\r\npath_input = vars(args)['in']\r\n\r\n# read files and merge all alignments to one dataframe\r\nlength = {}\r\nfor path in path_input:\r\n one_gene_table = file_read(path)\r\n length[path.split('\\\\')[-1]] = len(one_gene_table[path.split('\\\\')[-1]][1])\r\n if 'all_genes_table' not in globals():\r\n all_genes_table = one_gene_table\r\n else:\r\n all_genes_table = all_genes_table.merge(one_gene_table, left_on='taxon', right_on='taxon', how='outer')\r\n\r\n# fill NA by '-'\r\nall_genes_table['all_genes'] = str()\r\nfor gene in length.keys():\r\n all_genes_table[gene].fillna(int(length[gene]) * '-', inplace=True)\r\n all_genes_table['all_genes'] = all_genes_table['all_genes'] + all_genes_table[gene]\r\n\r\n# write OUT and NEXUS files\r\nfinal_table = all_genes_table[['taxon', 'all_genes']]\r\nif vars(args)['out']:\r\n final_table.to_csv(vars(args)['out'], sep='\\n', index=False, header=False)\r\nelse:\r\n print(final_table)\r\n\r\nif vars(args)['nexus']:\r\n start = 0\r\n original_stdout = sys.stdout\r\n with open(vars(args)['nexus'], 'w') as f:\r\n sys.stdout = f\r\n print('#nexus\\nbegin sets;')\r\n for gene in length.keys():\r\n print('\\tCHARSET ', gene, ' = ', start + 1, '-', length[gene] + start, ';', sep='')\r\n start += length[gene]\r\n print('end;\\n')\r\n sys.stdout = original_stdout\r\n", "sub_path": "merge_fasta.py", "file_name": "merge_fasta.py", "file_ext": "py", "file_size_in_byte": 3725, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 8, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 42, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 42, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 80, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 82, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 88, "usage_type": "attribute"}]} +{"seq_id": "583709195", "text": "# -*- coding: utf-8 -*-\n\n#\n# Copyright (c) 2014-2021 Virtual Cable S.L.U.\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without modification,\n# are permitted provided that the following conditions are met:\n#\n# * Redistributions of source code must retain the above copyright notice,\n# this list of conditions and the following disclaimer.\n# * Redistributions in binary form must reproduce the above copyright notice,\n# this list of conditions and the following disclaimer in the documentation\n# and/or other materials provided with the distribution.\n# * Neither the name of Virtual Cable S.L. nor the names of its contributors\n# may be 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\"\n# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\n# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n# OR TORT (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\"\"\"\n@author: Adolfo Gómez, dkmaster at dkmon dot com\n\"\"\"\nimport datetime\nimport logging\nimport typing\n\n\nfrom uds.REST import Handler\nfrom uds.REST import RequestError\nfrom uds import models\nfrom uds.core.managers import cryptoManager\nfrom uds.core.util.model import processUuid\nfrom uds.core.util import tools\n\nlogger = logging.getLogger(__name__)\n\n# Valid parameters accepted by ticket creation method\nVALID_PARAMS = (\n 'authId',\n 'auth_id',\n 'authTag',\n 'auth_tag',\n 'authSmallName',\n 'auth',\n 'auth_name',\n 'username',\n 'realname',\n 'password',\n 'groups',\n 'servicePool',\n 'service_pool',\n 'transport', # Admited to be backwards compatible, but not used. Will be removed on a future release.\n 'force',\n 'userIp',\n 'user_ip',\n)\n\n\n# Enclosed methods under /tickets path\nclass Tickets(Handler):\n \"\"\"\n Processes tickets access requests.\n Tickets are element used to \"register\" & \"allow access\" to users.\n\n The rest API accepts the following parameters:\n authId: uuid of the authenticator for the user | Mutually excluyents\n authSmallName: tag of the authenticator (alias for \"authTag\") | But must include one of theese\n authTag: tag of the authenticator |\n auth: Name of authenticator |\n userIp: Direccion IP del cliente. Si no se pasa, no se puede filtar\n username:\n password:\n groups:\n servicePool:\n transport: Ignored. Transport must be auto-detected on ticket auth\n force: If \"1\" or \"true\" will ensure that:\n - Groups exists on authenticator\n - servicePool has these groups in it's allowed list\n \"\"\"\n\n needs_admin = True # By default, staff is lower level needed\n\n @staticmethod\n def result(\n result: str = '', error: typing.Optional[str] = None\n ) -> typing.Dict[str, typing.Any]:\n \"\"\"\n Returns a result for a Ticket request\n \"\"\"\n res = {'result': result, 'date': datetime.datetime.now()}\n if error is not None:\n res['error'] = error\n return res\n\n def get(self):\n \"\"\"\n Processes get requests, currently none\n \"\"\"\n logger.debug('Ticket args for GET: %s', self._args)\n\n raise RequestError('Invalid request')\n\n def _checkInput(self) -> None:\n # Parameters can only be theese\n for p in self._params:\n if p not in VALID_PARAMS:\n logger.debug('Parameter %s not in valid ticket parameters list', p)\n raise RequestError('Invalid parameters')\n\n if len(self._args) != 1 or self._args[0] not in ('create',):\n raise RequestError('Invalid method')\n\n try:\n for i in ('authId', 'auth_id', 'authTag', 'auth_tag', 'auth', 'auth_name', 'authSmallName'):\n if i in self._params:\n raise StopIteration\n\n if 'username' in self._params and 'groups' in self._params:\n raise StopIteration()\n \n raise RequestError('Invalid parameters (no auth or username/groups)')\n except StopIteration:\n pass # All ok\n \n # Must be invoked as '/rest/ticket/create, with \"username\", (\"authId\" or \"auth_id\") or (\"auth_tag\" or \"authSmallName\" or \"authTag\"), \"groups\" (array) and optionally \"time\" (in seconds) as paramteres\n def put(\n self,\n ) -> typing.Dict[str, typing.Any]:\n \"\"\"\n Processes put requests, currently only under \"create\"\n \"\"\"\n logger.debug(self._args)\n\n # Check that call is correct (pamateters, args, ...)\n self._checkInput()\n\n force: bool = self.getParam('force') in ('1', 'true', 'True', True)\n\n try:\n servicePoolId: typing.Optional[str] = None\n\n # First param is recommended, last ones are compatible with old versions\n authId = self.getParam('auth_id', 'authId')\n authName = self.getParam('auth_name', 'auth')\n authTag = self.getParam('auth_tag', 'authTag', 'authSmallName')\n\n # Will raise an exception if no auth found\n if authId:\n auth = models.Authenticator.objects.get(\n uuid=processUuid(authId.lower())\n )\n elif authName:\n auth = models.Authenticator.objects.get(name=authName)\n else:\n auth = models.Authenticator.objects.get(small_name=authTag)\n\n username: str = self.getParam('username')\n password: str = self.getParam('password')\n # Some machines needs password, depending on configuration\n\n groupIds: typing.List[str] = []\n for groupName in tools.as_list(self.getParam('groups')):\n try:\n groupIds.append(auth.groups.get(name=groupName).uuid or '')\n except Exception:\n logger.info(\n 'Group %s from ticket does not exists on auth %s, forced creation: %s',\n groupName,\n auth,\n force,\n )\n if force: # Force creation by call\n groupIds.append(\n auth.groups.create(\n name=groupName,\n comments='Autocreated form ticket by using force paratemeter',\n ).uuid\n or ''\n )\n\n if not groupIds: # No valid group in groups names\n raise RequestError(\n 'Authenticator does not contain ANY of the requested groups and force is not used'\n )\n\n try:\n time = int(self.getParam('time') or 60)\n time = 60 if time < 1 else time\n except Exception:\n time = 60\n realname: str = self.getParam('realname', 'username') or ''\n\n poolUuid = self.getParam('servicePool')\n if poolUuid:\n # Check if is pool or metapool\n poolUuid = processUuid(poolUuid)\n pool: typing.Union[models.ServicePool, models.MetaPool]\n\n try:\n pool = typing.cast(\n models.MetaPool, models.MetaPool.objects.get(uuid=poolUuid)\n ) # If not an metapool uuid, will process it as a servicePool\n if force:\n # First, add groups to metapool\n for addGrp in set(groupIds) - set(\n pool.assignedGroups.values_list('uuid', flat=True)\n ):\n pool.assignedGroups.add(auth.groups.get(uuid=addGrp))\n # And now, to ALL metapool members\n for metaMember in pool.members.all():\n # Now add groups to pools\n for addGrp in set(groupIds) - set(\n metaMember.pool.assignedGroups.values_list(\n 'uuid', flat=True\n )\n ):\n metaMember.pool.assignedGroups.add(\n auth.groups.get(uuid=addGrp)\n )\n\n # For metapool, transport is ignored..\n\n servicePoolId = 'M' + pool.uuid\n\n except models.MetaPool.DoesNotExist:\n pool = typing.cast(\n models.ServicePool,\n models.ServicePool.objects.get(uuid=poolUuid),\n )\n\n # If forced that servicePool must honor groups\n if force:\n for addGrp in set(groupIds) - set(\n pool.assignedGroups.values_list('uuid', flat=True)\n ):\n pool.assignedGroups.add(auth.groups.get(uuid=addGrp))\n\n servicePoolId = 'F' + pool.uuid # type: ignore\n\n except models.Authenticator.DoesNotExist:\n return Tickets.result(error='Authenticator does not exists')\n except models.ServicePool.DoesNotExist: # type: ignore # this is fine, is not the same as models.Authenticator.DoesNotExist\n return Tickets.result(error='Service pool (or metapool) does not exists')\n except models.Transport.DoesNotExist: # type: ignore # this is fine, is not the same as models.Authenticator.DoesNotExist\n return Tickets.result(error='Transport does not exists')\n except Exception as e:\n return Tickets.result(error=str(e))\n\n data = {\n 'username': username,\n 'password': cryptoManager().encrypt(password),\n 'realname': realname,\n 'groups': groupIds,\n 'auth': auth.uuid,\n 'servicePool': servicePoolId,\n }\n\n ticket = models.TicketStore.create(data)\n\n return Tickets.result(ticket)\n", "sub_path": "server/src/uds/REST/methods/tickets.py", "file_name": "tickets.py", "file_ext": "py", "file_size_in_byte": 10807, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 45, "usage_type": "call"}, {"api_name": "uds.REST.Handler", "line_number": 70, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 95, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 100, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 100, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 96, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 96, "usage_type": "attribute"}, {"api_name": "uds.REST.RequestError", "line_number": 111, "usage_type": "call"}, {"api_name": "uds.REST.RequestError", "line_number": 118, "usage_type": "call"}, {"api_name": "uds.REST.RequestError", "line_number": 121, "usage_type": "call"}, {"api_name": "uds.REST.RequestError", "line_number": 131, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 150, "usage_type": "attribute"}, {"api_name": "uds.models.Authenticator.objects.get", "line_number": 159, "usage_type": "call"}, {"api_name": "uds.models.Authenticator", "line_number": 159, "usage_type": "attribute"}, {"api_name": "uds.models", "line_number": 159, "usage_type": "name"}, {"api_name": "uds.core.util.model.processUuid", "line_number": 160, "usage_type": "call"}, {"api_name": "uds.models.Authenticator.objects.get", "line_number": 163, "usage_type": "call"}, {"api_name": "uds.models.Authenticator", "line_number": 163, "usage_type": "attribute"}, {"api_name": "uds.models", "line_number": 163, "usage_type": "name"}, {"api_name": "uds.models.Authenticator.objects.get", "line_number": 165, "usage_type": "call"}, {"api_name": "uds.models.Authenticator", "line_number": 165, "usage_type": "attribute"}, {"api_name": "uds.models", "line_number": 165, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 171, "usage_type": "attribute"}, {"api_name": "uds.core.util.tools.as_list", "line_number": 172, "usage_type": "call"}, {"api_name": "uds.core.util.tools", "line_number": 172, "usage_type": "name"}, {"api_name": "uds.REST.RequestError", "line_number": 192, "usage_type": "call"}, {"api_name": "uds.core.util.model.processUuid", "line_number": 206, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 207, "usage_type": "attribute"}, {"api_name": "uds.models.ServicePool", "line_number": 207, "usage_type": "attribute"}, {"api_name": "uds.models", "line_number": 207, "usage_type": "name"}, {"api_name": "uds.models.MetaPool", "line_number": 207, "usage_type": "attribute"}, {"api_name": "typing.cast", "line_number": 210, "usage_type": "call"}, {"api_name": "uds.models.MetaPool", "line_number": 211, "usage_type": "attribute"}, {"api_name": "uds.models", "line_number": 211, "usage_type": "name"}, {"api_name": "uds.models.MetaPool.objects.get", "line_number": 211, "usage_type": "call"}, {"api_name": "uds.models.MetaPool", "line_number": 235, "usage_type": "attribute"}, {"api_name": "uds.models", "line_number": 235, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 236, "usage_type": "call"}, {"api_name": "uds.models.ServicePool", "line_number": 237, "usage_type": "attribute"}, {"api_name": "uds.models", "line_number": 237, "usage_type": "name"}, {"api_name": "uds.models.ServicePool.objects.get", "line_number": 238, "usage_type": "call"}, {"api_name": "uds.models.ServicePool", "line_number": 238, "usage_type": "attribute"}, {"api_name": "uds.models", "line_number": 238, "usage_type": "name"}, {"api_name": "uds.models.Authenticator", "line_number": 250, "usage_type": "attribute"}, {"api_name": "uds.models", "line_number": 250, "usage_type": "name"}, {"api_name": "uds.models.ServicePool", "line_number": 252, "usage_type": "attribute"}, {"api_name": "uds.models", "line_number": 252, "usage_type": "name"}, {"api_name": "uds.models.Transport", "line_number": 254, "usage_type": "attribute"}, {"api_name": "uds.models", "line_number": 254, "usage_type": "name"}, {"api_name": "uds.core.managers.cryptoManager", "line_number": 261, "usage_type": "call"}, {"api_name": "uds.models.TicketStore.create", "line_number": 268, "usage_type": "call"}, {"api_name": "uds.models.TicketStore", "line_number": 268, "usage_type": "attribute"}, {"api_name": "uds.models", "line_number": 268, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 138, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 138, "usage_type": "attribute"}]} +{"seq_id": "33319824", "text": "\nimport logging\nfrom helper.Database import Database\nfrom Rule import Rule\n\nclass RuleManager(object):\n \n def __init__(self):\n super(RuleManager, self).__init__()\n\n self.log = logging.getLogger('drw.' + self.__module__ + '.' + self.__class__.__name__)\n\n self.db = Database()\n\n def getRules(self):\n rids = self.db.read(\"SELECT rid FROM rule\")\n\n rules = []\n\n for rule in rids:\n rules.append(self.getRule(rule['rid']))\n\n return rules\n\n def getRule(self, rid):\n dbRule = self.db.read(\"SELECT rid, name, enabled FROM rule r WHERE r.rid = :rid\", { \"rid\": int(rid) })\n\n if dbRule == None:\n return False\n\n dbRule = dbRule.fetchone()\n\n if not dbRule:\n return False\n\n rule = Rule(dbRule['rid'], dbRule['name'], dbRule['enabled'])\n\n dbResConditions = self.db.read(\"SELECT c.cid, c.rule_rid, c.device_did, c.device_value_vid, c.operator, c.value, d.name FROM condition c LEFT JOIN device d ON d.did = c.device_did WHERE rule_rid = :rid\", {\"rid\": rule.rid})\n\n for (cid, rule_rid, device_did, device_value_vid, operator, value, device_name) in dbResConditions.fetchall():\n rule.addCondition({\n \"cid\": cid,\n \"rule_rid\": rule_rid,\n \"device_did\": device_did,\n \"device_name\": device_name,\n \"device_value_vid\": device_value_vid,\n \"operator\": operator,\n \"value\": value\n })\n\n dbResActions = self.db.read(\"SELECT a.aid, a.rule_rid, a.device_did, a.device_action_aid, a.value, d.name FROM action a LEFT JOIN device d ON d.did = a.device_did WHERE a.rule_rid = :rid\", {\"rid\": rule.rid})\n\n for (aid, rule_rid, device_did, device_action_aid, value, device_name) in dbResActions.fetchall():\n rule.addAction({\n \"aid\": aid,\n \"rule_rid\": rule_rid,\n \"device_did\": device_did,\n \"device_name\": device_name,\n \"device_action_aid\": device_action_aid,\n \"value\": value\n })\n\n return rule\n\n def saveRule(self, rule):\n if rule.rid is None:\n res = self.db.write(\"INSERT INTO rule (name, enabled) VALUES (:name, :enabled)\", rule.getDict())\n\n rule.rid = res.lastrowid\n\n for condition in rule.conditions:\n condition['rid'] = rule.rid\n res = self.db.write(\"INSERT INTO condition (rule_rid, device_did, device_value_vid, operator, value) VALUES (:rid, :device_did, :device_value_vid, :operator, :value)\", condition)\n \n for action in rule.actions:\n action['rid'] = rule.rid\n res = self.db.write(\"INSERT INTO action (rule_rid, device_did, device_action_aid, value) VALUES (:rid, :device_did, :device_action_aid, :value)\", action)\n\n else:\n self.db.write(\"UPDATE rule SET name = :name, enabled = :enabled WHERE rid = :rid\", rule.getDict())\n\n self.db.write(\"DELETE FROM condition WHERE rule_rid = :rid\", rule.getDict())\n for condition in rule.conditions:\n condition['rid'] = rule.rid\n res = self.db.write(\"INSERT INTO condition (rule_rid, device_did, device_value_vid, operator, value) VALUES (:rid, :device_did, :device_value_vid, :operator, :value)\", condition)\n \n self.db.write(\"DELETE FROM action WHERE rule_rid = :rid\", rule.getDict())\n for action in rule.actions:\n action['rid'] = rule.rid\n res = self.db.write(\"INSERT INTO action (rule_rid, device_did, device_action_aid, value) VALUES (:rid, :device_did, :device_action_aid, :value)\", action)\n \n\n def deleteRule(self, rule):\n if rule.rid:\n self.db.write(\"DELETE FROM action WHERE rule_rid = :rid\", rule.getDict())\n self.db.write(\"DELETE FROM condition WHERE rule_rid = :rid\", rule.getDict())\n self.db.write(\"DELETE FROM rule_trigger WHERE rule_rid = :rid\", rule.getDict())\n self.db.write(\"DELETE FROM rule WHERE rid = :rid\", rule.getDict())\n\n def enable(self, rid):\n self.db.write(\"UPDATE rule SET enabled = 1 WHERE rid = :rid\", { \"rid\": int(rid) })\n \n def disable(self, rid):\n self.db.write(\"UPDATE rule SET enabled = 0 WHERE rid = :rid\", { \"rid\": int(rid) })\n", "sub_path": "src/core/RuleManager.py", "file_name": "RuleManager.py", "file_ext": "py", "file_size_in_byte": 4414, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "helper.Database.Database", "line_number": 13, "usage_type": "call"}, {"api_name": "Rule.Rule", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "92791078", "text": "import os\nimport pandas as pd\nfrom datetime import datetime\nfrom paramiko import SSHClient, AutoAddPolicy\nfrom Plugins.Profilers.LogFileProfiler import LogFileProfiler\nfrom ProgressManager.Output.OutputProcedure import OutputProcedure\n\n\nclass MoveBaseProfiler(LogFileProfiler):\n\n def __init__(self, ip_addr, username, hostname) -> None:\n super().__init__(ip_addr, username, hostname)\n\n def process_log_files(self, output_folder, move_base_on_pc=True):\n ssh_client = None\n sftp_client = None\n move_base_log_file = None\n navigation_results_log_file = None\n\n # SSH to the remote machine\n try:\n ssh_client = SSHClient()\n ssh_client.load_host_keys(f\"/home/{os.environ['USERNAME']}/.ssh/known_hosts\")\n ssh_client.set_missing_host_key_policy(AutoAddPolicy())\n ssh_client.connect(self.ip_addr, username=self.username)\n sftp_client = ssh_client.open_sftp()\n \n # If move_base node is executed on this PC, fetch the log file locally\n if move_base_on_pc:\n move_base_log_file = self.open_local_log_file(\"move_base\")\n # Otherwise fetch the file over SFTP \n else:\n move_base_log_file = self.open_remote_log_file(ssh_client, sftp_client, \"move_base\")\n\n # Process log file\n move_base_df = self.process_move_base_log_file(move_base_log_file)\n\n # Fetch remotely and process obj_recognition_results log file\n navigation_results_log_file = self.open_remote_log_file(ssh_client, sftp_client, \"sherlock_controller\")\n navigation_results_df = self.process_navigation_results(navigation_results_log_file)\n\n # Calculate the delay of receiving the detection result at the side of obj_recognition_results node in ms\n results_df = self.combine_data_frames(move_base_df, navigation_results_df)\n\n results_df.to_csv(os.path.join(output_folder, \"move_base_results.csv\"), index=False, header=True)\n OutputProcedure.console_log_OK(\"MoveBase profiler done\")\n\n except BaseException as e:\n OutputProcedure.console_log_FAIL(\"FindObject2d profiler failed!\")\n print(e)\n \n finally:\n # Close all resources that are successfully open\n if navigation_results_log_file:\n navigation_results_log_file.close()\n\n if move_base_log_file:\n move_base_log_file.close()\n\n if sftp_client:\n sftp_client.close()\n\n if ssh_client:\n ssh_client.close()\n\n\n def process_move_base_log_file(self, log_file):\n # Data to extract from the file\n data = {\n 'goal_processed_at': [], \n 'goal_reached_at': []\n }\n\n # Catch only the first 'Got new plan' message after new goal is sent\n firs_goal_processing = True\n\n for line in log_file:\n # First time new goal is processed\n if 'Got new plan' in line and firs_goal_processing:\n # Get log time\n line = line[line.index(']') + 1 :]\n time_as_string = line[line.index('[') + 1 : line.index(']')]\n log_time = datetime.fromtimestamp(float(time_as_string))\n\n data['goal_processed_at'].append(log_time)\n firs_goal_processing = False\n # Destination is reached\n elif 'Goal reached' in line:\n # Get log time\n line = line[line.index(']') + 1 :]\n time_as_string = line[line.index('[') + 1 : line.index(']')]\n log_time = datetime.fromtimestamp(float(time_as_string))\n\n data['goal_reached_at'].append(log_time)\n firs_goal_processing = True\n\n return pd.DataFrame(data)\n\n\n def process_navigation_results(self, log_file):\n data = {\n 'goal_sent_at': [],\n 'result_received_at': []\n }\n\n for line in log_file:\n\n if 'Sending goal location' in line:\n # Get log time\n line = line[line.index(']') + 1 :]\n time_as_string = line[line.index('] ') + 2 : line.index('Sending goal location')]\n log_time = datetime.strptime(time_as_string, '%Y-%m-%d %H:%M:%S,%f: ')\n\n data['goal_sent_at'].append(log_time)\n elif 'The robot has reached the destination' in line:\n # Get log time\n line = line[line.index(']') + 1 :]\n time_as_string = line[line.index('] ') + 2 : line.index('The robot has reached the destination')]\n log_time = datetime.strptime(time_as_string, '%Y-%m-%d %H:%M:%S,%f: ')\n\n data['result_received_at'].append(log_time)\n\n return pd.DataFrame(data)\n\n def combine_data_frames(self, move_base_df, navigation_results_df):\n data = {\n 'goal_sent_at': [],\n 'goal_sending_delay_ms': [],\n 'goal_processing_s': [],\n 'result_delay_ms': []\n }\n\n # Time when goal location is sent\n data['goal_sent_at'] = navigation_results_df['goal_sent_at']\n\n # Delay to start navigating to the goal location\n data['goal_sending_delay_ms'] = move_base_df['goal_processed_at'] - navigation_results_df['goal_sent_at']\n data['goal_sending_delay_ms'] = data['goal_sending_delay_ms'].apply(lambda x: x.total_seconds() * 1000)\n\n # Navigation duration\n data['goal_processing_s'] = move_base_df['goal_reached_at'] - move_base_df['goal_processed_at']\n data['goal_processing_s'] = data['goal_processing_s'].apply(lambda x: x.total_seconds())\n\n # Receiving destination reached result delay\n data['result_delay_ms'] = navigation_results_df['result_received_at'] - move_base_df['goal_reached_at']\n data['result_delay_ms'] = data['result_delay_ms'].apply(lambda x: x.total_seconds() * 1000)\n\n return pd.DataFrame(data)\n\n\n def get_average_results(self, input_folder):\n input_file = os.path.join(input_folder, \"move_base_results.csv\")\n results_df = pd.read_csv(input_file)\n return results_df['goal_sending_delay_ms'].mean(), results_df['goal_processing_s'].mean(), results_df['result_delay_ms'].mean()\n\n", "sub_path": "robot-runner/Plugins/Profilers/MoveBaseProfiler.py", "file_name": "MoveBaseProfiler.py", "file_ext": "py", "file_size_in_byte": 6353, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "Plugins.Profilers.LogFileProfiler.LogFileProfiler", "line_number": 9, "usage_type": "name"}, {"api_name": "paramiko.SSHClient", "line_number": 22, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 23, "usage_type": "attribute"}, {"api_name": "paramiko.AutoAddPolicy", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "ProgressManager.Output.OutputProcedure.OutputProcedure.console_log_OK", "line_number": 46, "usage_type": "call"}, {"api_name": "ProgressManager.Output.OutputProcedure.OutputProcedure", "line_number": 46, "usage_type": "name"}, {"api_name": "ProgressManager.Output.OutputProcedure.OutputProcedure.console_log_FAIL", "line_number": 49, "usage_type": "call"}, {"api_name": "ProgressManager.Output.OutputProcedure.OutputProcedure", "line_number": 49, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 83, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 83, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 92, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 92, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 97, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 112, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 112, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 119, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 119, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 123, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 148, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 152, "usage_type": "call"}, {"api_name": "os.path", "line_number": 152, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 153, "usage_type": "call"}]} +{"seq_id": "13716444", "text": "from __future__ import print_function\nimport requests\nimport json\nimport base64\nfrom string import Template\nimport argparse\nimport csv\nimport untangle\nimport subprocess\nfrom xml.sax import SAXParseException\n\n__author__ = 'lab'\n\nPDB_UNP_ALIGN_URL = 'http://www.rcsb.org/pdb/rest/das/pdb_uniprot_mapping/alignment?query='\nUNP_FASTA_URL = 'http://www.uniprot.org/uniprot/'\nECOD_FASTA_URL = 'http://prodata.swmed.edu/ecod/complete/sequence?id='\nUNP_FASTA_FILE = \"/tmp/unp.fasta\"\nECOD_FASTA_FILE = \"/tmp/ecod.fasta\"\nSWALIGN_CMD = \"java -jar NWAlign.jar \"\nNEO4J_CREATE_TRAN_URL = \"http://localhost:7474/db/data/transaction/commit\"\nNEO4J_USER_PASS = 'neo4j:Sheshi6'\nECOD_PDB_RES_MAP_URL = 'http://prodata.swmed.edu/ecod/data/%s/%s.residues.xml'\nECOD_PDB_RES_MAP_FILE = '/home/lab/Downloads/ecod_domain_res/data/ecod/domain_data/%s/%s/%s.residues.xml'\n\ndef main():\n args = parse_args()\n source = args.ecod_source\n # csv_destination = args.csv_destination\n # destination = args.destination\n csv_reader = csv.DictReader(source, delimiter=\",\")\n domain_unp_mapping = {}\n for row in csv_reader:\n try:\n domain_uid = \"%09d\" % int(row['Uid'])\n domain_id = row['Domain_id']\n pdb_id = domain_id[1:5]\n domain_to_pdb_res_map = create_mapping(domain_uid)\n createneo4jmapping(domain_uid, pdb_id, domain_to_pdb_res_map, NEO4J_CREATE_TRAN_URL)\n except SAXParseException as e:\n print('ECOD entry '+domain_uid+\" raised a SAX exception while matching against UNP entry\")\n print(e)\n except Exception as e:\n print('ECOD entry '+domain_uid+\" raised an exception while matching against UNP entry\")\n print(e)\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Import ECOD to pdb and uniprot mapping.\")\n # exclusive_group = parser.add_mutually_exclusive_group(required=True)\n # batch_group = exclusive_group.add_argument_group(\"Batch processing\", \"description\")\n parser.add_argument(\"-ecod_src\", \"--ecod_source\", help=\"Source file with ECOD IDs\", type=file, required=True)\n # parser.add_argument(\"-pdb_unp_map\", \"--pdb_unp_mapping\", help=\"Source file with PDB to UNP residue mapping\", type=file, required=True)\n # parser.add_argument(\"-csvdst\", \"--csv_destination\", help=\"CSV mapping file destination\", type=argparse.FileType('w'),\n # required=True)\n # parser.add_argument(\"-dst\", \"--destination\", help=\"Mapping file destination\", type=argparse.FileType('w'),\n # required=True)\n args = parser.parse_args()\n return args\n\ndef create_mapping(domain_uid):\n res_mapping = {}\n xml_map = untangle.parse(ECOD_PDB_RES_MAP_URL % (domain_uid ,domain_uid))\n\n # TODO:\n # Mapping can be to the same chain but skip a few residues.\n # In this case a new match needs to be created!\n # See for example: http://prodata.swmed.edu/ecod/complete/domain/e3pymA2, http://prodata.swmed.edu/ecod/data/000147122/000147122.residues.xml\n # The mapping is split at resdiue 149 to 323 :-(\n for residue in xml_map.domain_residue_doc.residue_list.residue:\n chain_id = residue['chain_id']\n seq_id = int(residue['seq_id'])\n if residue['chain_id'] in res_mapping:\n if seq_id < res_mapping[chain_id]['seq_start']:\n res_mapping[chain_id]['seq_start'] = seq_id\n res_mapping[chain_id]['pdbresnum_start'] = residue['pdb_resnum']\n elif seq_id > res_mapping[chain_id]['seq_end']:\n res_mapping[chain_id]['seq_end'] = seq_id\n res_mapping[chain_id]['pdbresnum_end'] = residue['pdb_resnum']\n else:\n res_mapping[chain_id] = {}\n res_mapping[chain_id]['seq_start'] = seq_id\n res_mapping[chain_id]['pdbresnum_start'] = residue['pdb_resnum']\n res_mapping[chain_id]['seq_end'] = seq_id\n res_mapping[chain_id]['pdbresnum_end'] = residue['pdb_resnum']\n\n return res_mapping\n\ndef createneo4jmapping(domain_uid, pdb_id, res_mapping, trans_location):\n unp_statment_template = 'MATCH (d:Domain {uid: {domain_uid} })' \\\n ', (c:PDBChain {id: {pdbchain_id} })' \\\n ' CREATE UNIQUE (d)-[:MATCHES {props} ]->(c)'\n statments_list = []\n\n for chain_id in res_mapping:\n parameters_dict = {'domain_uid': int(domain_uid),'pdbchain_id': pdb_id+\".\"+chain_id}\n props = {\n 'seq_start': res_mapping[chain_id]['seq_start'],\n 'pdbresnum_start': res_mapping[chain_id]['pdbresnum_start'],\n 'seq_end': res_mapping[chain_id]['seq_end'],\n 'pdbresnum_end': res_mapping[chain_id]['pdbresnum_end']\n }\n parameters_dict['props'] = props\n statement_dict = {'statement': unp_statment_template,'parameters':parameters_dict}\n statments_list.append(statement_dict)\n\n statements_dict = {'statements': statments_list}\n\n r = requests.post(trans_location, headers=generateheaders(),data=json.dumps(statements_dict))\n r_obj = json.loads(r.text)\n if r_obj['errors']:\n print(r_obj['errors'])\n\ndef generateheaders():\n return {'Authorization': base64.b64encode(NEO4J_USER_PASS),\n 'Accept': 'application/json; charset=UTF-8',\n 'Content-Type': 'application/json',}\n\nif __name__ == \"__main__\":\n main()", "sub_path": "mapDomain2PDB.py", "file_name": "mapDomain2PDB.py", "file_ext": "py", "file_size_in_byte": 5393, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "csv.DictReader", "line_number": 30, "usage_type": "call"}, {"api_name": "xml.sax.SAXParseException", "line_number": 39, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 47, "usage_type": "call"}, {"api_name": "untangle.parse", "line_number": 61, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 107, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 107, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 108, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 113, "usage_type": "call"}]} +{"seq_id": "206422462", "text": "\nimport discord\nimport jikanpy\nfrom discord.ext import tasks, commands\nfrom jikanpy import Jikan\n\n\nclass UserCog(commands.Cog):\n \"\"\"\n UserCog: handles all the user-related logic.\n \"\"\"\n\n def __init__(self, bot):\n \"\"\"\n Constructor: initialize the cog.\n\n :param bot: The Discord bot.\n \"\"\"\n self.bot = bot\n self.discordUser = None\n self.malUser = None\n self.channel = None\n self.jikan = Jikan()\n\n @commands.command(brief='Ping the bot')\n async def ping(self, ctx):\n \"\"\"\n Ping the bot.\n\n :param ctx: The context.\n \"\"\"\n await ctx.send(f'Pong: {round(self.bot.latency*1000)}ms')\n\n def start(self):\n \"\"\"\n Start the UserCog:\n - retrieves the user from the database, if possible\n - start the updateMalProfileLoop\n \"\"\"\n user = self.bot.get_cog('DatabaseCog').getUser()\n if user:\n try:\n self.malUser = self._getMALProfile(user['mal'])\n except jikanpy.exceptions.APIException:\n pass\n self.discordUser = self._getMember(user['discord'])\n self.channel = self._getChannel(user['channel'])\n self.bot.command_prefix = user['prefix']\n\n self.updateMalProfileLoop.start()\n\n def _getMALProfile(self, username):\n \"\"\"\n Get the MyAnimeList user object, given the username.\n\n :param username: The username of the MAL account.\n :return: The MAL user.\n \"\"\"\n return self.jikan.user(username=username)\n\n def _updateMALProfile(self, profile):\n \"\"\"\n Update the internal MAL user, i.e. updating the watching/reading list.\n\n :param profile: The username of the MAL account.\n \"\"\"\n try:\n newAnimeList = []\n watching = self.jikan.user(username=profile, request='animelist', argument='watching')['anime']\n ptw = self.jikan.user(username=profile, request='animelist', argument='ptw')['anime']\n for anime in watching + ptw:\n anime['title_english'] = self.jikan.anime(anime['mal_id'])['title_english']\n newAnimeList.append(anime)\n\n newMangaList = []\n reading = self.jikan.user(username=profile, request='mangalist', argument='reading')['manga']\n ptr = self.jikan.user(username=profile, request='mangalist', argument='ptr')['manga']\n for manga in reading + ptr:\n manga['title_english'] = self.jikan.manga(manga['mal_id'])['title_english']\n newMangaList.append(manga)\n\n # If for some reason, we cannot retrieve the new lists (e.g. API error), keep the old ones\n if newAnimeList:\n self.bot.get_cog('AnimeCog').list = newAnimeList\n if newMangaList:\n self.bot.get_cog('MangaCog').list = newMangaList\n\n except Exception as e:\n # There's nothing we can do :'(\n print(str(e))\n\n def _getMember(self, user):\n \"\"\"\n Get the Discord member object, give its name and tag.\n\n :param user: The user (name + tag).\n :return: The member object, if none can be found, return None.\n \"\"\"\n for member in self.bot.get_all_members():\n if str(member) == user:\n return member\n return None\n\n def _getChannel(self, channelName):\n \"\"\"\n Get the Discord channel object, give the name of the channel.\n\n :param channelName: The name of the channel.\n :return: The channel object, if none can be found, return None.\n \"\"\"\n for channel in self.bot.get_all_channels():\n if str(channel) == channelName:\n return channel\n return None\n\n @commands.command(brief='Set your MAL profile')\n async def setProfile(self, ctx, profile: str):\n \"\"\"\n Set the internal MAL account, as well as the discord account and bot channel.\n\n :param ctx: The context.\n :param profile: Name of the MAL account.\n \"\"\"\n try:\n self.malUser = self._getMALProfile(profile)\n except jikanpy.exceptions.APIException:\n await ctx.send(f'Unable to find user {profile}, make sure the profile is public.')\n return\n \n await ctx.send(\n 'Successfully set profile, you\\'ll now receive notifications for new anime episodes and manga chapters!')\n\n self.discordUser = ctx.author\n if self.channel is None:\n self.channel = ctx.channel\n\n # Store data in database\n self.bot.get_cog('DatabaseCog').addUser(profile, str(self.discordUser), str(self.channel))\n\n self._updateMALProfile(profile)\n\n @commands.command(brief='Remove your MAL profile from the bot')\n async def removeProfile(self, ctx):\n self.bot.get_cog('DatabaseCog').truncateUsers()\n self.discordUser = None\n self.malUser = None\n self.channel = None\n self.bot.get_cog('AnimeCog').list = []\n self.bot.get_cog('MangaCog').list = []\n await ctx.send('Successfully removed you from the bot!')\n\n @commands.command(brief='Get a brief overview of your MAL profile')\n async def getProfile(self, ctx):\n \"\"\"\n Get the MAL profile in form of an embed\n\n :param ctx: The context.\n \"\"\"\n if self.malUser:\n embed = discord.Embed(title=self.malUser['username'], color=discord.Color.green())\n embed.add_field(name=\"Watching/Plan-to-Watch\", value=str(len(self.bot.get_cog('AnimeCog').list)))\n embed.add_field(name=\"Reading/Plan-to-Read\", value=str(len(self.bot.get_cog('MangaCog').list)))\n embed.add_field(name=\"Link\", value=self.malUser['url'])\n embed.set_thumbnail(url=self.malUser['image_url'])\n await ctx.send(embed=embed)\n else:\n await ctx.send(\"Profile is not set, please use `!setProfile ` first.\")\n\n @commands.command(brief='Set the bot channel (where it will ping you)')\n async def setChannel(self, ctx, channel: discord.TextChannel):\n \"\"\"\n Set the bot channel.\n\n :param ctx: The context.\n :param channel: Name of the bot channel.\n \"\"\"\n self.channel = channel\n self.bot.get_cog('DatabaseCog').addUser(self.malUser['username'], str(self.discordUser), str(channel))\n await ctx.send(f'Successfully set bot channel to {channel.mention}.')\n\n @commands.command(brief='Set the prefix of the bot')\n async def setPrefix(self, ctx, prefix: str):\n \"\"\"\n Set the prefix of the bot\n\n :param ctx: The context.\n :param prefix: The new prefix for the bot.\n \"\"\"\n self.bot.command_prefix = prefix\n self.bot.get_cog('DatabaseCog').addUser(self.malUser['username'], str(self.discordUser), str(self.channel), prefix)\n await ctx.send(f'Successfully set the prefix to `{prefix}`.')\n\n @setChannel.error\n async def setChannelError(self, ctx, error):\n \"\"\"\n Error Handler for setChannel.\n\n :param ctx: The context.\n :param error: The error raised.\n \"\"\"\n await ctx.send(error.args[0])\n\n @tasks.loop(hours=1)\n async def updateMalProfileLoop(self):\n \"\"\"\n Loop that periodically updates the MAL account, i.e. update watching/reading list.\n \"\"\"\n if self.malUser:\n await self._updateMALProfile(self.malUser['username'])\n", "sub_path": "naotomori/cogs/usercog.py", "file_name": "usercog.py", "file_ext": "py", "file_size_in_byte": 7511, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "discord.ext.commands.Cog", "line_number": 8, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 8, "usage_type": "name"}, {"api_name": "jikanpy.Jikan", "line_number": 23, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 25, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 25, "usage_type": "name"}, {"api_name": "jikanpy.exceptions", "line_number": 44, "usage_type": "attribute"}, {"api_name": "jikanpy.exceptions", "line_number": 126, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.command", "line_number": 116, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 116, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 142, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 142, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 160, "usage_type": "call"}, {"api_name": "discord.Color.green", "line_number": 160, "usage_type": "call"}, {"api_name": "discord.Color", "line_number": 160, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.command", "line_number": 152, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 152, "usage_type": "name"}, {"api_name": "discord.TextChannel", "line_number": 170, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.command", "line_number": 169, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 169, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 181, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 181, "usage_type": "name"}, {"api_name": "discord.ext.tasks.loop", "line_number": 203, "usage_type": "call"}, {"api_name": "discord.ext.tasks", "line_number": 203, "usage_type": "name"}]} +{"seq_id": "452099007", "text": "from foryou.common.base import *\nimport requests\n\n\n# 公共接口-省市区配置\ndef bidding_provincial_allocation(cookies_bidding):\n url = mk_url('superbrain', 'api/src/getAllRegionTree')\n data = {\n 'level': 3\n }\n r = requests.post(url, data, cookies=cookies_bidding)\n print()\n return r.json()\n\n\n# 获取全部省份\ndef bidding_all_province(cookies_bidding):\n url = mk_url('superbrain', 'api/src/getProvinces')\n r = requests.post(url, cookies=cookies_bidding)\n return r.json()\n\n\n# 获取全部城市\ndef bidding_all_city(cookies_bidding):\n url = mk_url('superbrain', 'api/src/getProvinces')\n r = requests.post(url, cookies=cookies_bidding)\n return r.json()\n\n\n# 获得图灵和区域用户的用户列表\ndef bidding_turing_area_userlist(cookies_bidding):\n url = mk_url('superbrain', 'api/user/userSearch')\n data = {\n 'text': 1,\n 'userType': 1\n }\n r = requests.post(url, data, cookies=cookies_bidding)\n return r.json()\n\n\n# 缓存所有省市区信息到本地\ndef bidding_cache_provincial_allocation(cookies_bidding):\n url = mk_url('superbrain', 'api/src/putAllRegionTreeMap')\n r = requests.post(url, cookies=cookies_bidding)\n return r.json()\n\n\n# 获取系统车型车长配置列表\ndef bidding_car_conductor_list(cookies_bidding):\n url = mk_url('superbrain', 'api/src/getSelectionsList')\n r = requests.post(url, cookies=cookies_bidding)\n return r.json()\n\n\n# 查询子公司信息(模糊匹配)\ndef bidding_sub_company_information(cookies_bidding):\n url = mk_url('superbrain', 'api/src/subCompanyList')\n data = {\n 'companyName': '测试'\n }\n r = requests.post(url, data, cookies=cookies_bidding)\n return r.json()\n\n\n# 获取决策组人员列表\ndef bidding_decision_user_list(cookies_bidding):\n url = mk_url('superbrain', 'api/user/getDecisionUserList')\n data = {\n 'pageIndex': 1,\n 'pageSize': 30\n\n }\n r = requests.post(url, data, cookies=cookies_bidding)\n return r.json()\n\n\n# 获取当前用户关联的图灵用户和区域用户\ndef bidding_turing_area_list(cookies_bidding):\n url = mk_url('superbrain', 'api/user/getRelationList')\n data = {\n 'userId': 1178\n }\n r = requests.post(url, data, cookies=cookies_bidding)\n return r.json()\n\n\n# 获取当前用户可关联的图灵用户和区域用户\ndef bidding_current_turing_area_list(cookies_bidding):\n url = mk_url('superbrain', 'api/user/getCanRelationList')\n data = {\n 'userId': 1178\n }\n r = requests.post(url, data, cookies=cookies_bidding)\n return r.json()\n\n\n# 提交关联用户\ndef bidding_submit_associated_users(cookies_bidding):\n url = mk_url('superbrain', 'api/user/updateRelation')\n print('报价规则新增修改')\n data_1 = {\n 'relationUserName': '原瑞霞',\n 'relationUserId': 1189\n\n }\n data_2 = {\n 'relationUserName': '单欣欣',\n 'relationUserId': 1024\n\n }\n data = {\n 'userId': 1178,\n 'turingUserList': json.dumps(data_1),\n 'regionUserList': json.dumps(data_2)\n }\n r = requests.post(url, data, cookies=cookies_bidding)\n return r.json()\n\n\n# 数据管理-下载全年价导入模版\ndef bidding_download_annual_price_import(cookies_bidding):\n url = mk_url('superbrain', 'api/year/exportPriceYearTemplate')\n r = requests.post(url, cookies=cookies_bidding)\n date = time.strftime(\"%Y-%m-%d\", time.localtime())\n file_name = \"export_priceyear_template_%s.xls\" % date\n with open(file_name, \"wb\") as f:\n for chunk in r.iter_content(chunk_size=512):\n if chunk:\n f.write(chunk)\n f.close()\n return file_name\n\n\n# 全年价数据导入\ndef bidding_annual_price_import(cookies_bidding,file):\n url = mk_url('superbrain', 'api/year/importYearQuoteInfo')\n r = requests.post(url, files=file, cookies=cookies_bidding)\n return r.json()\n\n\n# 全年价查询\ndef bidding_annual_price_check(cookies_bidding):\n url = mk_url('superbrain', 'api/year/getList')\n data ={\n 'pageIndex': 1,\n 'pageSize': 30\n }\n r = requests.post(url, data, cookies=cookies_bidding)\n return r.json()\n\n\n# 系统配置-图灵规则-新增\ndef bidding_turing_rules_new(cookies_bidding):\n url = mk_url('superbrain', 'api/rule/addTuringRule')\n config_2 = {\n 'adminId': 1445,\n 'adminName': '孙竹叶',\n 'adminMobile': 17313157107\n }\n config_1 = {\n 'rank': 0,\n 'ruleName': '江苏始发',\n 'syncFlag': '0',\n 'lineTypeIds': 1,\n 'startProvinceIds': 20,\n 'endProvinceIds': 1,\n 'carLengthIds': 22,\n 'carModelIds': 1,\n 'adminListStr': json.dumps([config_2])\n }\n r = requests.post(url, config_1, cookies=cookies_bidding)\n return r.json()\n\n\n# 系统配置-图灵规则-修改\ndef bidding_turing_rules_update(cookies_bidding):\n url = mk_url('superbrain', 'api/rule/addTuringRule')\n config_2 = {\n 'adminId': 1445,\n 'adminName': '孙竹叶',\n 'adminMobile': 17313157107\n }\n config_1 = {\n 'id': 2,\n 'rank': 0,\n 'ruleName': '规则配置：单边，江苏-浙江，9.6-厢式车，竹叶',\n 'syncFlag': '1',\n 'lineTypeIds': 1,\n 'startProvinceIds': 16,\n 'endProvinceIds': 1,\n 'carLengthIds': 8,\n 'carModelIds': 1,\n 'adminListStr': json.dumps([config_2])\n }\n\n r = requests.post(url, config_1, cookies=cookies_bidding)\n return r.json()\n\n\n# 区域报价分配规则-新增\ndef bidding_area_rules_new(cookies_bidding):\n url = mk_url('superbrain', 'api/regionConfig/create')\n region1 = {\n 'regionId': 24,\n 'regionType': 1,\n 'regionName': '陕西省',\n 'parentId': 0\n }\n region2 = {\n 'regionId': 4,\n 'regionType': 3,\n 'regionName': '福建省',\n 'parentId': 0\n }\n data_1 = [region1, region2]\n data_2 = [{\n 'adminId': 1204,\n 'adminName': '单欣欣',\n 'adminMobile': 18310179572\n }]\n data_3 = [{\n 'companyId': 2,\n 'companyName': '欣欣'\n }]\n data = {\n 'ruleName': '图灵规则123',\n 'lineType': 1,\n 'adminList': json.dumps(data_2),\n 'regionList': json.dumps(data_1),\n 'companyList': json.dumps(data_3)\n\n }\n r = requests.post(url, data, cookies=cookies_bidding)\n return r.json()\n\n\n# 区域报价分配规则-修改\ndef bidding_area_rules_update(cookies_bidding):\n url = mk_url('superbrain', 'api/regionConfig/update')\n data_1 = {\n 'adminId': 1311,\n 'adminName': '单欣欣',\n 'adminMobile': 18310179572\n }\n data = {\n 'id': 1,\n 'ruleName': '图灵默认配置',\n 'adminList': json.dumps([data_1]),\n 'regionList': [],\n 'companyList': []\n\n }\n r = requests.post(url, data, cookies=cookies_bidding)\n return r.json()\n\n\n# 图灵规则列表\ndef bidding_rule_list(cookies_bidding):\n url = mk_url('superbrain', 'api/rule/turingRuleList')\n data = {\n 'ruleName': '规则配置：单边，江苏-浙江，9.6-厢式车，竹叶',\n 'pageIndex': 1,\n 'pageSize': 30\n }\n r = requests.post(url, data, cookies=cookies_bidding)\n return r.json()\n\n\n# 图灵规则详情\ndef bidding_rule_detall(cookies_bidding):\n url = mk_url('superbrain', 'api/rule/turingRuleDetail')\n data = {\n 'ruleId': 2\n }\n r = requests.post(url, data, cookies=cookies_bidding)\n return r.json()\n\n\n# 调用区域分配规则-根据id查询\ndef bidding_area_call_rules_id(cookies_bidding):\n url = mk_url('superbrain', 'api/regionConfig/queryById')\n data = {\n 'id': 1\n }\n r = requests.post(url, data, cookies=cookies_bidding)\n return r.json()\n\n\n# 区域分配规则-查询列表\ndef bidding_area_rules_checklist(cookies_bidding):\n url = mk_url('superbrain', 'api/regionConfig/getList')\n data = {\n 'pageIndex': 1,\n 'pageSize': 10\n }\n r = requests.post(url, data, cookies=cookies_bidding)\n return r.json()\n\n\n# 用户模糊搜索\ndef bidding_user_fuzzy_search(cookies_bidding):\n url = mk_url('superbrain', 'api/user/userSearch')\n data = {\n 'key': 15010032693,\n 'userType': 1\n }\n r = requests.post(url, data, cookies=cookies_bidding)\n return r.json()\n\n\n# 获取所有的省份\ndef bidding_obtain_all_province(cooikes_bidding):\n url = mk_url('superbrain', 'api/src/getProvinces')\n r = requests.post(url, cookies=cooikes_bidding)\n return r.json()\n\n\n# 获取所有城市\ndef bidding_obtain_all_city(cookies_bidding):\n url = mk_url('superbrain', 'api/src/getCities')\n r = requests.post(url, cookies=cookies_bidding)\n return r.json()\n\n\n# 获取省市区列表\ndef bidding_province_city_list(cookies_bidding):\n url = mk_url('superbrain', 'api/src/getAllRegionTree')\n r = requests.post(url, cookies=cookies_bidding)\n return r.json()\n\n\n# 是否开启接单以及接单状态\ndef bidding_yesno_order_state(cookies_bidding):\n url = mk_url('superbrain', 'api/rule/getCanReceive')\n r = requests.post(url, cookies=cookies_bidding)\n return r.json()\n\n\n# 开启/关闭接单\ndef bidding_start_down_order(cookies_bidding):\n url = mk_url('superbrain', 'api/rule/changeReceiveStatus')\n data = {\n 'isReceive': 1\n }\n r = requests.post(url, data, cookies=cookies_bidding)\n return r.json()\n\n\n# 标的管理-全部标的-下载线路导入模版\ndef bidding_underlying_download_import_template(cookies_bidding):\n url = mk_url('superbrain', 'api/route/exportRouteTemplate')\n r = requests.post(url, cookies=cookies_bidding)\n date = time.strftime(\"%Y-%m-%d\", time.localtime())\n file_name = \"import_project_route_template_%s.xls\" % date\n with open(file_name, \"wb\") as f:\n for chunk in r.iter_content(chunk_size=512):\n if chunk:\n f.write(chunk)\n f.close()\n return file_name\n\n\n# 标的管理-全部标的-标的线路\ndef bidding_import_underlying(cookies_bidding, file, project_id):\n url = mk_url('superbrain', 'api/route/importProjectRoute')\n data = {\n 'projectId': project_id\n }\n r = requests.post(url, data, files=file, cookies=cookies_bidding)\n return r.json()\n\n\n# 标的管理-全部标的-根据标的ID导出标的\ndef bidding_underlying_id_import(project_id, cookies_bidding):\n url = mk_url('superbrain', 'api/route/exportProjectRoute')\n data = {\n 'projectId': project_id\n }\n r = requests.post(url, data, cookies=cookies_bidding)\n date = time.strftime(\"%Y-%m-%d\", time.localtime())\n file_name = \"export_bidding_Id_template_%s.xls\" % date\n with open(file_name, \"wb\") as f:\n for chunk in r.iter_content(chunk_size=512):\n if chunk:\n f.write(chunk)\n f.close()\n return file_name\n\n\n# 标的管理-全部标的-标的结果回传\ndef bidding_result_import(project_id, cookies_bidding, file):\n url = mk_url('superbrain', 'api/route/importBidResult')\n data = {\n 'projectId': project_id\n }\n r = requests.post(url, data, files=file, cookies=cookies_bidding)\n return r.json()\n\n\n# 标的管理-全部标的-全部标的列表\ndef bidding_all_project_list(cookies_bidding):\n url = mk_url('superbrain', 'api/project/allProjectList')\n data = {\n 'pageIndex': 1,\n 'pageSize': 30\n }\n r = requests.post(url, data, cookies=cookies_bidding)\n return r.json()\n\n\n# 标的管理-全部标的-标的详情\ndef bidding_get_project_detail(project_id, cookies_bidding):\n url = mk_url('superbrain', 'api/project/getProjectDetail')\n data = {\n 'projectId': project_id\n }\n r = requests.post(url, data, cookies=cookies_bidding)\n return r.json()\n\n\n# 标的管理-全部标的-标的线路列表\ndef bidding_project_line_list_all(project_id, cookies_bidding):\n url = mk_url('superbrain', 'api/route/projectRouteList')\n data = {\n 'projectId': project_id,\n 'pageIndex': 1,\n 'pageSize': 30\n }\n r = requests.post(url, data, cookies=cookies_bidding)\n return r.json()\n\n\n# 标的管理-全部标的-线路导入错误日志\ndef bidding_line_import_error_log(project_id, cookies_bidding):\n url = mk_url('superbrain', 'api/route/projectRouteErrorList')\n data = {\n 'projectId': project_id,\n 'pageIndex': 1,\n 'pageSize': 30\n }\n r = requests.post(url, data, cookies=cookies_bidding)\n return r.json()\n\n\n# 标的管理-全部标的-线路详情\ndef bidding_line_detail(route_id, cookies_bidding):\n url = mk_url('superbrain', 'api/route/getRouteDetail')\n data = {\n 'routeId': route_id\n }\n r = requests.post(url, data, cookies=cookies_bidding)\n return r.json()\n\n\n# 标的管理-全部标的-新增标的\ndef bidding_new_bid(cookies_bidding):\n url = mk_url('superbrain', 'api/project/addProjectInfo')\n date_now = datetime.datetime.now().date()\n data = {\n 'companyId': 2197,\n 'companyName': '顺丰重货（接口）',\n 'taxRatio': 8.88,\n 'quoteStopTime': date_now,\n 'quoteTime': date_now,\n 'onlineTime': date_now,\n 'endTime': date_now,\n 'relatedUserName': '接口测试小人'\n\n }\n r = requests.post(url, data, cookies=cookies_bidding)\n return r.json()\n\n\n# 标的管理-全部标的-修改标的\ndef bidding_update_bid(bid_id, company_id, company_name, tax_ratio, cookies_bidding):\n url = mk_url('superbrain', 'api/project/addProjectInfo')\n date_now = datetime.datetime.now().date()\n print(date_now)\n data = {\n 'id': bid_id,\n 'companyId': company_id,\n 'companyName': company_name,\n 'taxRatio': tax_ratio,\n 'quoteStopTime': date_now,\n 'quoteTime': date_now,\n 'onlineTime': date_now,\n 'endTime': date_now,\n 'relatedUserName': '接口测试小人'\n\n }\n r = requests.post(url, data, cookies=cookies_bidding)\n return r.json()\n\n\n# 标的管理-全部标的-删除标的\ndef bidding_delete_bid(bid_id, cookies_bidding):\n url = mk_url('superbrain', 'api/project/delete')\n data = {\n 'projectId': bid_id\n\n }\n r = requests.post(url, data, cookies=cookies_bidding)\n return r.json()\n\n\n# 报价管理-待报价线路列表\ndef bidding_management_quote_line_list(cookies_ua):\n url = mk_url('superbrain', 'api/route/waitingForQuoteList')\n data = {\n 'pageIndex': 1,\n 'pageSize': 30\n }\n r = requests.post(url, data, cookies=cookies_ua)\n print(r.text)\n return r.json()\n\n\n# 报价管理-图灵报价提交\ndef bidding_management_turing_quote_submit(route_id, cookies_ua):\n url = mk_url('superbrain', 'api/route/turingBidRoute')\n data = {\n 'routeId': route_id,\n 'price': 5000\n }\n r = requests.post(url, data, cookies=cookies_ua)\n return r.json()\n\n\n# 报价管理—区域报价提交\ndef bidding_management_area_quote_submit(route_id, cookies_ua):\n url = mk_url('superbrain', 'api/route/regionBidRoute')\n data = {\n 'routeId': route_id,\n 'price': 5000\n }\n r = requests.post(url, data, cookies=cookies_ua)\n return r.json()\n\n\n# 报价管理-线路报价且流转\ndef bidding_line_quote_circulation(route_id, cookies_ua):\n url = mk_url('superbrain', 'api/route/bidAndChange')\n data = {\n 'routeId': route_id,\n 'price': 5000\n }\n r = requests.post(url, data, cookies=cookies_ua)\n return r.json()\n\n\n# 报价管理-线路流转\ndef bidding_line_circulation(route_id, cookies_ua):\n url = mk_url('superbrain', 'api/route/changeRoute')\n data = {\n 'routeId': route_id\n }\n r = requests.post(url, data, cookies=cookies_ua)\n return r.json()\n\n\n# 标的决策-待决策线路列表\ndef bidding_wait_decision_list(cookies_ua):\n url = mk_url('superbrain', 'api/route/waitDecisionList')\n data = {\n 'pageIndex': 1,\n 'pageSize': 30,\n 'decisionStatus': 0\n }\n r = requests.post(url, data, cookies=cookies_ua)\n return r.json()\n\n\n# 标的决策-决策（线路）\ndef bidding_making_route(route_id, cookies_ua):\n url = mk_url('superbrain', 'api/route/decision')\n data = {\n 'routeId': route_id,\n 'taxRatio': 8,\n 'required': 0,\n 'priceType': 1,\n 'price': 5000\n }\n r = requests.post(url, data, cookies=cookies_ua)\n return r.json()\n\n\n# 标的决策-稍后决策（线路）\ndef bidding_later_make_route(route_id, cookies_ua):\n url = mk_url('superbrain', 'api/route/laterDecision')\n data = {\n 'routeId': route_id,\n\n }\n r = requests.post(url, data, cookies=cookies_ua)\n return r.json()\n\n\n# 决策完成（标的）\ndef bidding_making_complete(project_id, cookies_ua):\n url = mk_url('superbrain', 'api/project/decision')\n data = {\n 'projectId': project_id\n }\n r = requests.post(url, data, cookies=cookies_ua)\n return r.json()\n\n\n# 全部决策标的列表\ndef all_making_bidding_list(cookies_ua):\n url = mk_url('superbrain', 'api/project/allDecisionList')\n data = {\n 'pageIndex': 1,\n 'pageSize': 30,\n }\n r = requests.post(url, data, cookies=cookies_ua)\n return r.json()\n\n\n# 标的内决策线路列表\ndef bidding_making_route_list(cookies_ua):\n url = mk_url('superbrain', 'api/route/projectDecisionRouteList')\n data = {\n 'projectId': 391,\n 'lineStatus': 2,\n 'pageIndex': 1,\n 'pageSize': 30\n }\n r = requests.post(url, data, cookies=cookies_ua)\n return r.json()\n\n\n# 修改决策报价\ndef bidding_update_making_quote(route_id, cookies_ua):\n url = mk_url('superbrain', 'api/route/modifyDecision')\n data = {\n 'routeId': route_id,\n 'price': 4800,\n 'taxRatio': 9,\n 'required': 0\n }\n print(data)\n r = requests.post(url, data, cookies=cookies_ua)\n return r.json()\n\n\n# 线路价格展示方式(线路决策价)\ndef route_price_show_style(route_id, cookies_ua):\n url = mk_url('superbrain', 'api/route/routeDecisionPrice')\n data = {\n 'routeId': route_id\n }\n r = requests.post(url, data, cookies=cookies_ua)\n return r.json()\n\n\n# 查询线路流转状态(图灵报价)\ndef check_route_circulation_status(route_id, cookies_ua):\n url = mk_url('superbrain', 'api/route/routeChangeStatus')\n data = {\n 'routeId': route_id\n }\n r = requests.post(url, data, cookies=cookies_ua)\n return r.json()\n\n\n# 线路决策价(待决策状态)\ndef route_make_quote_pending_state(route_id, cookies_ua):\n url = mk_url('superbrain', 'api/route/waitDecisionPrice')\n data = {\n 'routeId': route_id\n }\n r = requests.post(url, data, cookies=cookies_ua)\n return r.json()\n\n\n# 报价参考-报价员报价-牛顿线路匹配\ndef offer_reference_newton_line_matching(project_id, cookies_ua):\n url = mk_url('superbrain', 'api/match/projectLineMatch.do')\n data = {\n 'projectId': project_id,\n 'distance': 0,\n 'pageIndex': 1,\n 'pageSize': 30\n }\n r = requests.post(url, data, cookies=cookies_ua)\n return r.json()\n\n\n# 报价参考-双边线路匹配\ndef offer_reference_bilateral_line_matching(roject_id, cookies_ua):\n url = mk_url('superbrain', 'api/match/twosideLineMatch.do')\n data = {\n 'projectId': roject_id,\n 'distance': 0,\n 'pageIndex': 1,\n 'pageSize': 30\n }\n r = requests.post(url, data, cookies=cookies_ua)\n return r.json()\n\n\n# 报价员报价-零散货匹配\ndef offer_reference_bulk_line_matching(roject_id, cookies_ua):\n url = mk_url('superbrain', 'api/match/orderLineMatch.do')\n data = {\n 'projectId': roject_id,\n 'distance': 0,\n 'pageIndex': 1,\n 'pageSize': 30\n }\n r = requests.post(url, data, cookies=cookies_ua)\n return r.json()\n\n\n# 报价员报价-市场价线路匹配\ndef offer_reference_marker_line_matching(project_id, cookies_ua):\n url = mk_url('superbrain', 'api/match/collectionLineMatch.do')\n data = {\n 'projectId': project_id,\n 'distance': 0,\n 'pageIndex': 1,\n 'pageSize': 30\n }\n r = requests.post(url, data, cookies=cookies_ua)\n return r.json()\n\n\n# 报价员报价-全年价线路匹配\ndef offer_reference_all_price_line_matching(cookies_ua):\n url = mk_url('superbrain', 'api/match/yearQuoteLineMatch.do')\n data = {\n 'projectId': 19022784530000,\n 'distance': 0,\n 'pageIndex': 1,\n 'pageSize': 30\n }\n r = requests.post(url, data, cookies=cookies_ua)\n return r.json()\n\n\n# 报价员报价-历史数据匹配\ndef offer_reference_history_data_matching(project_id, cookies_ua):\n url = mk_url('superbrain', 'api/match/priceProjectLineMatch.do')\n data = {\n 'projectId': project_id,\n 'distance': 0,\n 'pageIndex': 1,\n 'pageSize': 30\n }\n r = requests.post(url, data, cookies=cookies_ua)\n return r.json()\n\n\n# 标的历史线路导入-下载历史数据导入模版\ndef download_history_data_import_template(cookies_ua):\n url = mk_url('superbrain', 'api/line/exportProjectHisTemplate')\n r = requests.post(url, cookies=cookies_ua)\n r.json()\n\n\n# 标的历史数据导入\ndef bidding_history_data_import(cookies_bidding):\n url = mk_url('superbrain', 'api/line/importHisProject')\n date = time.strftime(\"%Y-%m-%d\", time.localtime())\n file_name = \"export_priceyear_template_%s.xls\" % date\n with open(file_name, \"wb\") as f:\n for chunk in r.iter_content(chunk_size=512):\n if chunk:\n f.write(chunk)\n f.close()\n return file_name\n\n\n# 数据管理-全年价-下载全年价导入模版\ndef bidding_all_price_download_import_template(cookies_bidding):\n url = mk_url('superbrain', 'api/year/exportPriceYearTemplate')\n r = requests.post(url, cookies=cookies_bidding)\n date = time.strftime(\"%Y-%m-%d\", time.localtime())\n file_name = \"export_all_price_template_%s.xls\" % date\n with open(file_name, \"wb\") as f:\n for chunk in r.iter_content(chunk_size=512):\n if chunk:\n f.write(chunk)\n f.close()\n return file_name\n\n\n# 数据管理-全年价导入\ndef bidding_all_price_import(cookies_bidding, file=None):\n url = mk_url('superbrain', 'api/year/importYearQuoteInfo')\n r = requests.post(url, files=file, cookies=cookies_bidding)\n return r.json()\n\n\n# 数据管理-全年价查询\ndef bidding_all_price_check(cookies_bidding):\n url = mk_url('superbrain', 'api/year/getList')\n data = {\n 'pageIndex': 1,\n 'pageSize': 30\n }\n r = requests.post(url, data, cookies=cookies_bidding)\n return r.json()\n\n\nif __name__ == '__main__':\n pass\n", "sub_path": "foryou/api/api_bidding.py", "file_name": "api_bidding.py", "file_ext": "py", "file_size_in_byte": 22659, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "requests.post", "line_number": 11, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 19, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 26, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 37, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 44, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 51, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 61, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 73, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 83, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 93, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 116, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 123, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 137, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 148, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 171, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 196, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 233, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 253, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 265, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 275, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 285, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 296, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 307, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 314, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 321, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 328, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 335, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 345, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 352, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 369, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 379, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 396, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 407, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 417, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 429, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 441, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 451, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 470, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 491, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 502, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 513, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 525, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 536, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 547, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 557, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 569, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 583, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 594, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 604, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 615, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 628, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 642, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 652, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 662, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 672, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 685, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 698, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 711, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 724, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 737, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 750, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 757, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 777, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 791, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 802, "usage_type": "call"}]} +{"seq_id": "271781974", "text": "import henge\nimport logmuse\nimport os\nimport pyfaidx\nimport logging\n\nfrom .const import *\n\nfrom yacman import load_yaml\nfrom copy import copy\n\n\n_LOGGER = logging.getLogger(__name__)\nhenge.ITEM_TYPE = \"_item_type\"\nSCHEMA_FILEPATH = os.path.join(\n os.path.dirname(__file__),\n \"schemas\")\n\n\nclass RefGetHenge(henge.Henge):\n \"\"\"\n Extension of henge that accommodates refget sequences.\n \"\"\"\n\n def __init__(self, database, schemas=None, henges=None, checksum_function=henge.md5):\n \"\"\"\n A user interface to insert and retrieve decomposable recursive unique\n identifiers (DRUIDs).\n\n :param dict database: Dict-like lookup database with sequences and hashes.\n :param dict schemas: One or more jsonschema schemas describing the\n data types stored by this Henge\n :param function(str) -> str checksum_function: Default function to handle the digest of the\n serialized items stored in this henge.\n \"\"\"\n def _load_schema(name):\n return load_yaml(os.path.join(SCHEMA_FILEPATH, name))\n\n # These are the item types that this henge can understand.\n if not schemas:\n schemas = {\n \"sequence\": _load_schema(\"sequence.yaml\"),\n \"ASD\": _load_schema(\"annotated_sequence_digest.yaml\"),\n \"ASDList\": _load_schema(\"ASDList.yaml\"),\n \"ACDList\": _load_schema(\"ACDList.yaml\"),\n \"ACD\": _load_schema(\"annotated_collection_digest.yaml\")\n }\n super(RefGetHenge, self).__init__(database, schemas, henges=henges,\n checksum_function=checksum_function)\n\n def refget(self, digest, reclimit=None, postprocess=None):\n item_type = self.database[digest + henge.ITEM_TYPE]\n full_data = self.retrieve(digest, reclimit=reclimit)\n if not postprocess:\n return full_data\n\n if postprocess == \"simplify\":\n if item_type == \"sequence\":\n return full_data['sequence']\n elif item_type == \"asd\":\n asdlist = {}\n for x in full_data:\n asdlist[x['name']] = x['sequence_digest']['sequence']\n return asdlist\n elif postprocess == \"fasta\":\n if item_type == \"sequence\":\n raise Exception(\"can't postprocess a sequence into fasta\")\n elif item_type == \"asd\":\n asdlist = {}\n for x in full_data:\n asdlist[x['name']] = x['sequence_digest']['sequence'] \n return self.fasta_fmt(asdlist)\n else:\n raise NotImplementedError(\n \"This postprocessing mode is not implemented\")\n\n def fasta_fmt(self, content):\n \"\"\"\n Given a content dict return by refget for a sequence collection,\n convert it to a string that can be printed as a fasta file.\n \"\"\"\n return \"\\n\".join(\n [\"\\n\".join([\">\" + x[\"name\"], x[\"sequence_digest\"][\"sequence\"]])\n for x in content])\n\n def load_seq(self, seq):\n checksum = self.insert({'sequence': seq}, \"sequence\")\n _LOGGER.debug(\"Loaded {}\".format(checksum))\n return checksum\n\n def load_fasta(self, fa_file, lengths_only=False):\n \"\"\"\n Calculates checksums and loads each sequence in a fasta file into the\n database, and loads a level 2 collection checksum representing the\n entire collection into the database.\n \"\"\"\n fa_object = parse_fasta(fa_file)\n asdlist = []\n for k in fa_object.keys():\n seq = str(fa_object[k])\n if lengths_only:\n seq_digest = \"\"\n else:\n seq_digest = self.load_seq(seq)\n asdlist.append({'name': k,\n 'length': len(seq), \n 'topology': 'linear',\n 'sequence_digest': seq_digest})\n\n _LOGGER.debug(asdlist)\n collection_checksum = self.insert(asdlist, 'ASDList')\n return collection_checksum, asdlist\n\n def load_seqset(self, seqset):\n \"\"\"\n Convert a 'seqset', which is a dict with names as sequence names and\n values as sequences, into the 'asdlist' required for henge insert.\n \"\"\"\n seqset_new = copy(seqset)\n for k, v in seqset.items():\n if isinstance(v, str):\n seq = v\n v = {'sequence': seq}\n if 'length' not in v.keys():\n if 'sequence' not in v.keys():\n _LOGGER.warning(\n \"Each sequence must have either length or a sequence.\")\n else:\n v['length'] = len(v['sequence'])\n if 'sequence' in v.keys():\n v['sequence_digest'] = self.load_seq(seq)\n del v['sequence']\n if 'name' not in v.keys():\n v['name'] = k\n if 'toplogy' not in v.keys():\n v['toplogy'] = 'linear'\n\n seqset_new[k] = v\n\n collection_checksum = self.insert(list(seqset_new.values()), 'ASDList')\n return collection_checksum, seqset_new\n\n def compare_asds(self, asdA, asdB, explain=False):\n \"\"\"\n Compare Annotated Sequence Digests (ASDs) -- digested sequences and metadata\n\n :param str asdA: ASD for first sequence collection to compare.\n :param str asdB: ASD for second sequence collection to compare.\n :param bool explain: Print an explanation of the flag? [Default: False]\n \"\"\"\n # Not ideal, but we expect these to return lists, but if the item was\n # singular only a dict is returned\n if not isinstance(asdA, list):\n asdA = [asdA]\n if not isinstance(asdB, list):\n asdB = [asdB]\n\n def xp(prop, lst):\n \"\"\" Extract property \"\"\"\n return list(map(lambda x: x[prop], lst))\n\n ainb = [x in xp('sequence_digest', asdB) for x in\n xp('sequence_digest', asdA)]\n bina = [x in xp('sequence_digest', asdA) for x in\n xp('sequence_digest', asdB)]\n\n def index(x, lst):\n try:\n return xp('sequence_digest', lst).index(x)\n except:\n return None\n\n return_flag = 0 # initialize\n if sum(ainb) > 1:\n ordA = list(filter(None.__ne__, [index(x, asdB) for x in\n xp('sequence_digest', asdA)]))\n if ordA == sorted(ordA):\n return_flag += CONTENT_A_ORDER\n if sum(bina) > 1:\n ordB = list(filter(None.__ne__, [index(x, asdA) for x in\n xp('sequence_digest', asdB)]))\n if ordB == sorted(ordB):\n return_flag += CONTENT_B_ORDER\n\n ainb_len = [x in xp('length', asdB) for x in xp('length', asdA)]\n bina_len = [x in xp('length', asdA) for x in xp('length', asdB)]\n\n ainb_name = [x in xp('name', asdB) for x in xp('name', asdA)]\n bina_name = [x in xp('name', asdA) for x in xp('name', asdB)]\n\n if all(ainb):\n return_flag += CONTENT_ALL_A_IN_B\n\n if all(bina):\n return_flag += CONTENT_ALL_B_IN_A\n\n if all(ainb_name):\n return_flag += NAMES_ALL_A_IN_B\n if all(bina_name):\n return_flag += NAMES_ALL_B_IN_A\n\n if all(ainb_len):\n return_flag += LENGTHS_ALL_A_IN_B\n if all(bina_len):\n return_flag += LENGTHS_ALL_B_IN_A\n\n if explain:\n explain_flag(return_flag)\n return return_flag\n\n def compare(self, digestA, digestB, explain=False):\n \"\"\"\n Given two collection checksums in the database, provide some information\n about how they are related.\n\n :param str digestA: Digest for first sequence collection to compare.\n :param str digestB: Digest for second sequence collection to compare.\n :param bool explain: Print an explanation of the flag? [Default: False]\n \"\"\"\n typeA = self.database[digestA + henge.ITEM_TYPE]\n typeB = self.database[digestB + henge.ITEM_TYPE]\n\n if typeA != typeB:\n _LOGGER.error(\"Can't compare objects of different types: {} vs {}\".\n format(typeA, typeB))\n\n asdA = self.refget(digestA, reclimit=1)\n asdB = self.refget(digestB, reclimit=1)\n return self.compare_asds(asdA, asdB, explain=explain)\n\n\n# Static functions below (these don't require a database)\n\ndef explain_flag(flag):\n \"\"\" Explains a compare flag \"\"\"\n print(\"Flag: {}\\nBinary: {}\\n\".format(flag, bin(flag)))\n for e in range(0, 13):\n if flag & 2**e:\n print(FLAGS[2**e])\n\n\ndef parse_fasta(fa_file):\n _LOGGER.debug(\"Hashing {}\".format(fa_file))\n try:\n fa_object = pyfaidx.Fasta(fa_file)\n except pyfaidx.UnsupportedCompressionFormat:\n # pyfaidx can handle bgzip but not gzip; so we just hack it here and\n # unzip the file for checksumming, then rezip it for the rest of the\n # asset build.\n # TODO: streamline this to avoid repeated compress/decompress\n os.system(\"gunzip {}\".format(fa_file))\n fa_file_unzipped = fa_file.replace(\".gz\", \"\")\n fa_object = pyfaidx.Fasta(fa_file_unzipped)\n os.system(\"gzip {}\".format(fa_file_unzipped))\n return fa_object", "sub_path": "refget/refget.py", "file_name": "refget.py", "file_ext": "py", "file_size_in_byte": 9477, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "henge.ITEM_TYPE", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "henge.Henge", "line_number": 20, "usage_type": "attribute"}, {"api_name": "henge.md5", "line_number": 25, "usage_type": "attribute"}, {"api_name": "yacman.load_yaml", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "henge.ITEM_TYPE", "line_number": 52, "usage_type": "attribute"}, {"api_name": "copy.copy", "line_number": 119, "usage_type": "call"}, {"api_name": "henge.ITEM_TYPE", "line_number": 220, "usage_type": "attribute"}, {"api_name": "henge.ITEM_TYPE", "line_number": 221, "usage_type": "attribute"}, {"api_name": "pyfaidx.Fasta", "line_number": 245, "usage_type": "call"}, {"api_name": "pyfaidx.UnsupportedCompressionFormat", "line_number": 246, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 251, "usage_type": "call"}, {"api_name": "pyfaidx.Fasta", "line_number": 253, "usage_type": "call"}, {"api_name": "os.system", "line_number": 254, "usage_type": "call"}]} +{"seq_id": "332485186", "text": "# -*- coding: utf-8 -*-\n\"\"\"scenes\n\"\"\"\nimport logging\nfrom abc import ABC\nfrom typing import Tuple, Dict\n\nimport pygame as pg # type: ignore\nimport esper\nfrom tornado.util import ObjectDict\n\nfrom flappy_bird.setting import Global_Param\nfrom flappy_bird.common import load_image\nimport flappy_bird.entities as ent\nimport flappy_bird.systems as sys\n\n\nclass Carrier:\n \"\"\"不同 scene 间专递数据用\n \"\"\"\n\n def __init__(self, score, go_again):\n self.score = score\n self.go_again = go_again\n\n\nclass Scene:\n __first_run = True\n\n def __init__(self, **kwargs):\n \"\"\"\n\n :keyword fps: int, default 30\n :keyword resolution: tuple(int, int), default 800 x 600\n :keyword full_screen: bool\n :keyword show_mouse: bool\n \"\"\"\n self.fps = kwargs.get('fps', 30)\n self.resolution = kwargs.get('resolution', (800, 600))\n if self.__first_run:\n self.__first_run = False\n self._init_pygame(**kwargs)\n if not kwargs.get('show_mouse', True):\n pg.mouse.set_visible(0)\n self._setup_background()\n self.world = self._build_world()\n\n def _init_pygame(self, **kwargs):\n pg.init()\n if kwargs.get('full_screen', False):\n win_style = 0 | pg.FULLSCREEN\n else:\n win_style = 0\n best_depth = pg.display.mode_ok(self.resolution, win_style, 32)\n self.screen: pg.Surface = pg.display.set_mode(self.resolution, win_style, best_depth)\n self.background = pg.Surface(self.resolution)\n\n def _setup_background(self):\n raise NotImplementedError(\"this method should be overridden.\")\n\n def _build_world(self) -> esper.World:\n raise NotImplementedError(\"this method should be overridden.\")\n\n def _do_event(self, event):\n pass\n\n def _before_start(self):\n pass\n\n def _after_stop(self):\n pass\n\n @staticmethod\n def set_caption(name: str):\n pg.display.set_caption(name)\n\n def start(self):\n self._before_start()\n self.running = True\n self.screen.blit(self.background, (0, 0))\n pg.display.update()\n self.clock = pg.time.Clock()\n self.clock.tick(self.fps) # first kick to avoid zero dt_ms\n while self.running:\n self._main_loop()\n self._after_stop()\n\n def _main_loop(self):\n for event in pg.event.get():\n if event.type == pg.QUIT:\n self.running = False\n self._do_event(event)\n dt_ms = self.clock.get_time() # unit ms\n self.world.process(dt_ms=dt_ms)\n self.clock.tick(self.fps)\n\n def stop(self):\n self.running = False\n\n\nclass BaseScene(Scene, ABC):\n \"\"\"针对 flappy bird 游戏的 scene 统一设置集合\"\"\"\n\n def __init__(self, carrier: Carrier,\n fps: int, resolution: Tuple[int, int],\n full_screen: bool):\n self.carrier = carrier\n super().__init__(fps=fps, resolution=resolution, full_screen=full_screen)\n self.set_caption(\"Flappy Bird\")\n\n def _setup_background(self):\n self.background.fill(pg.Color(112, 197, 206))\n background_img = load_image(r'city_0.png', color_key=pg.Color(255, 0, 0), size_coef=0.5)\n background_offset = (0, Global_Param['city_y_offset'])\n self.background.blit(background_img, background_offset)\n\n\nclass GameScene(BaseScene):\n\n def _build_world(self) -> esper.World:\n \"\"\"do this before the classes are used, after screen setup\n \"\"\"\n world = esper.World()\n\n # Initialize Game Groups\n all_group = pg.sprite.LayeredDirty()\n hit_group = pg.sprite.Group()\n\n # pool set\n self.ent_pools: Dict[str, ent.EntityPool] = dict()\n\n # static ground\n self.ent_pools[\"ground_pool\"] = \\\n ent.GroundPool(all_group, world, pool_num=1)\n\n # land entity setup\n self.ent_pools[\"land_pool\"] = \\\n ent.LandPool(containers=(all_group, hit_group),\n world=world, pool_num=1,\n screen_width=self.screen.get_width())\n\n # bird entity pool\n self.ent_pools[\"bird_pool\"] = \\\n ent.BirdPool(containers=all_group,\n world=world, pool_num=1)\n\n # pipe entity pool setup\n self.ent_pools[\"up_pipe_pool\"] = \\\n ent.UpPipePool(containers=(all_group, hit_group),\n world=world, pool_num=5)\n self.ent_pools[\"dn_pipe_pool\"] = \\\n ent.DnPipePool(containers=(all_group, hit_group),\n world=world, pool_num=5)\n\n # score entity pool setup\n self.ent_pools[\"score_pool\"] = \\\n ent.ScorePool(containers=all_group,\n world=world, pool_num=1)\n\n world.add_processor(sys.SysRenderClear(all_group=all_group,\n screen=self.screen,\n background=self.background),\n priority=Global_Param['Sys_Priority_Top'])\n\n world.add_processor(sys.SysPlayerCtr(),\n priority=Global_Param['Sys_Priority_High'])\n\n world.add_processor(sys.SysCreatePipes(self.ent_pools['up_pipe_pool'],\n self.ent_pools['dn_pipe_pool']),\n priority=Global_Param['Sys_Priority_Low'])\n\n world.add_processor(sys.SysKillPipes(self.ent_pools['up_pipe_pool'],\n self.ent_pools['dn_pipe_pool']),\n priority=Global_Param['Sys_Priority_Low'])\n\n world.add_processor(sys.SysLandCircle(),\n priority=Global_Param['Sys_Priority_Default'])\n\n world.add_processor(sys.SysFrameAnimate(),\n priority=Global_Param['Sys_Priority_Default'])\n\n world.add_processor(sys.SysMove(),\n priority=Global_Param['Sys_Priority_Default'])\n\n world.add_processor(sys.SysCollide(hit_group=hit_group, scene=self),\n priority=Global_Param['Sys_Priority_Default'])\n\n world.add_processor(sys.SysRender(all_group=all_group, screen=self.screen),\n priority=Global_Param['Sys_Priority_Background'])\n\n return world\n\n def _before_start(self):\n logging.debug(\"before game scene start function\")\n assert self.ent_pools[\"ground_pool\"].create(set_alive=False)\n assert self.ent_pools[\"land_pool\"].create()\n assert self.ent_pools[\"bird_pool\"].create()\n assert self.ent_pools[\"score_pool\"].create()\n\n def _after_stop(self):\n logging.debug(\"after game scene stop function\")\n for pool in self.ent_pools.values():\n pool.reset()\n\n\nclass IntroScene(BaseScene):\n\n def _build_world(self) -> esper.World:\n \"\"\"do this before the classes are used, after screen setup\n \"\"\"\n world = esper.World()\n\n # Initialize Game Groups\n all_group = pg.sprite.LayeredDirty()\n\n # pool set\n self.ent_pools: Dict[str, ent.EntityPool] = dict()\n\n # static ground\n self.ent_pools[\"ground_pool\"] = ent.GroundPool(all_group, world, pool_num=1)\n\n # land entity setup\n self.ent_pools[\"land_pool\"] = ent.LandPool(containers=all_group,\n world=world, pool_num=1,\n screen_width=self.screen.get_width())\n\n # bird entity pool\n self.ent_pools[\"bird_pool\"] = ent.BirdPool(containers=all_group,\n world=world, pool_num=1)\n\n # score entity pool setup\n self.ent_pools[\"score_pool\"] = ent.ScorePool(containers=all_group,\n world=world, pool_num=1)\n\n # systems\n world.add_processor(sys.SysRenderClear(all_group=all_group,\n screen=self.screen,\n background=self.background),\n priority=Global_Param['Sys_Priority_Top'])\n\n world.add_processor(sys.SysIntroControlBird(scene=self),\n priority=Global_Param['Sys_Priority_High'])\n\n world.add_processor(sys.SysLandCircle(),\n priority=Global_Param['Sys_Priority_Default'])\n\n world.add_processor(sys.SysFrameAnimate(),\n priority=Global_Param['Sys_Priority_Default'])\n\n world.add_processor(sys.SysMove(),\n priority=Global_Param['Sys_Priority_Default'])\n\n world.add_processor(sys.SysRender(all_group=all_group, screen=self.screen),\n priority=Global_Param['Sys_Priority_Background'])\n\n return world\n\n def _before_start(self):\n logging.debug(\"before intro scene start function\")\n assert self.ent_pools['ground_pool'].create(set_alive=False) # 静态，不重复刷新\n assert self.ent_pools['land_pool'].create()\n assert self.ent_pools['bird_pool'].create()\n assert self.ent_pools['score_pool'].create(init_score=self.carrier.score)\n\n def _after_stop(self):\n logging.debug(\"after intro scene stop function\")\n for pool in self.ent_pools.values():\n pool.reset()\n", "sub_path": "flappy_bird/scenes.py", "file_name": "scenes.py", "file_ext": "py", "file_size_in_byte": 9457, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "pygame.mouse.set_visible", "line_number": 44, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pygame.init", "line_number": 49, "usage_type": "call"}, {"api_name": "pygame.FULLSCREEN", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pygame.display.mode_ok", "line_number": 54, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 55, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 56, "usage_type": "call"}, {"api_name": "esper.World", "line_number": 61, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 75, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 75, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 81, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 81, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 82, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 82, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 89, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 89, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 90, "usage_type": "attribute"}, {"api_name": "abc.ABC", "line_number": 101, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 105, "usage_type": "name"}, {"api_name": "pygame.Color", "line_number": 112, "usage_type": "call"}, {"api_name": "flappy_bird.common.load_image", "line_number": 113, "usage_type": "call"}, {"api_name": "pygame.Color", "line_number": 113, "usage_type": "call"}, {"api_name": "flappy_bird.setting.Global_Param", "line_number": 114, "usage_type": "name"}, {"api_name": "esper.World", "line_number": 123, "usage_type": "call"}, {"api_name": "pygame.sprite.LayeredDirty", "line_number": 126, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 126, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Group", "line_number": 127, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 127, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 130, "usage_type": "name"}, {"api_name": "flappy_bird.entities.EntityPool", "line_number": 130, "usage_type": "attribute"}, {"api_name": "flappy_bird.entities", "line_number": 130, "usage_type": "name"}, {"api_name": "flappy_bird.entities.GroundPool", "line_number": 134, "usage_type": "call"}, {"api_name": "flappy_bird.entities", "line_number": 134, "usage_type": "name"}, {"api_name": "flappy_bird.entities.LandPool", "line_number": 138, "usage_type": "call"}, {"api_name": "flappy_bird.entities", "line_number": 138, "usage_type": "name"}, {"api_name": "flappy_bird.entities.BirdPool", "line_number": 144, "usage_type": "call"}, {"api_name": "flappy_bird.entities", "line_number": 144, "usage_type": "name"}, {"api_name": "flappy_bird.entities.UpPipePool", "line_number": 149, "usage_type": "call"}, {"api_name": "flappy_bird.entities", "line_number": 149, "usage_type": "name"}, {"api_name": "flappy_bird.entities.DnPipePool", "line_number": 152, "usage_type": "call"}, {"api_name": "flappy_bird.entities", "line_number": 152, "usage_type": "name"}, {"api_name": "flappy_bird.entities.ScorePool", "line_number": 157, "usage_type": "call"}, {"api_name": "flappy_bird.entities", "line_number": 157, "usage_type": "name"}, {"api_name": "flappy_bird.systems.SysRenderClear", "line_number": 160, "usage_type": "call"}, {"api_name": "flappy_bird.systems", "line_number": 160, "usage_type": "name"}, {"api_name": "flappy_bird.setting.Global_Param", "line_number": 163, "usage_type": "name"}, {"api_name": "flappy_bird.systems.SysPlayerCtr", "line_number": 165, "usage_type": "call"}, {"api_name": "flappy_bird.systems", "line_number": 165, "usage_type": "name"}, {"api_name": "flappy_bird.setting.Global_Param", "line_number": 166, "usage_type": "name"}, {"api_name": "flappy_bird.systems.SysCreatePipes", "line_number": 168, "usage_type": "call"}, {"api_name": "flappy_bird.systems", "line_number": 168, "usage_type": "name"}, {"api_name": "flappy_bird.setting.Global_Param", "line_number": 170, "usage_type": "name"}, {"api_name": "flappy_bird.systems.SysKillPipes", "line_number": 172, "usage_type": "call"}, {"api_name": "flappy_bird.systems", "line_number": 172, "usage_type": "name"}, {"api_name": "flappy_bird.setting.Global_Param", "line_number": 174, "usage_type": "name"}, {"api_name": "flappy_bird.systems.SysLandCircle", "line_number": 176, "usage_type": "call"}, {"api_name": "flappy_bird.systems", "line_number": 176, "usage_type": "name"}, {"api_name": "flappy_bird.setting.Global_Param", "line_number": 177, "usage_type": "name"}, {"api_name": "flappy_bird.systems.SysFrameAnimate", "line_number": 179, "usage_type": "call"}, {"api_name": "flappy_bird.systems", "line_number": 179, "usage_type": "name"}, {"api_name": "flappy_bird.setting.Global_Param", "line_number": 180, "usage_type": "name"}, {"api_name": "flappy_bird.systems.SysMove", "line_number": 182, "usage_type": "call"}, {"api_name": "flappy_bird.systems", "line_number": 182, "usage_type": "name"}, {"api_name": "flappy_bird.setting.Global_Param", "line_number": 183, "usage_type": "name"}, {"api_name": "flappy_bird.systems.SysCollide", "line_number": 185, "usage_type": "call"}, {"api_name": "flappy_bird.systems", "line_number": 185, "usage_type": "name"}, {"api_name": "flappy_bird.setting.Global_Param", "line_number": 186, "usage_type": "name"}, {"api_name": "flappy_bird.systems.SysRender", "line_number": 188, "usage_type": "call"}, {"api_name": "flappy_bird.systems", "line_number": 188, "usage_type": "name"}, {"api_name": "flappy_bird.setting.Global_Param", "line_number": 189, "usage_type": "name"}, {"api_name": "esper.World", "line_number": 120, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 194, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 201, "usage_type": "call"}, {"api_name": "esper.World", "line_number": 211, "usage_type": "call"}, {"api_name": "pygame.sprite.LayeredDirty", "line_number": 214, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 214, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 217, "usage_type": "name"}, {"api_name": "flappy_bird.entities.EntityPool", "line_number": 217, "usage_type": "attribute"}, {"api_name": "flappy_bird.entities", "line_number": 217, "usage_type": "name"}, {"api_name": "flappy_bird.entities.GroundPool", "line_number": 220, "usage_type": "call"}, {"api_name": "flappy_bird.entities", "line_number": 220, "usage_type": "name"}, {"api_name": "flappy_bird.entities.LandPool", "line_number": 223, "usage_type": "call"}, {"api_name": "flappy_bird.entities", "line_number": 223, "usage_type": "name"}, {"api_name": "flappy_bird.entities.BirdPool", "line_number": 228, "usage_type": "call"}, {"api_name": "flappy_bird.entities", "line_number": 228, "usage_type": "name"}, {"api_name": "flappy_bird.entities.ScorePool", "line_number": 232, "usage_type": "call"}, {"api_name": "flappy_bird.entities", "line_number": 232, "usage_type": "name"}, {"api_name": "flappy_bird.systems.SysRenderClear", "line_number": 236, "usage_type": "call"}, {"api_name": "flappy_bird.systems", "line_number": 236, "usage_type": "name"}, {"api_name": "flappy_bird.setting.Global_Param", "line_number": 239, "usage_type": "name"}, {"api_name": "flappy_bird.systems.SysIntroControlBird", "line_number": 241, "usage_type": "call"}, {"api_name": "flappy_bird.systems", "line_number": 241, "usage_type": "name"}, {"api_name": "flappy_bird.setting.Global_Param", "line_number": 242, "usage_type": "name"}, {"api_name": "flappy_bird.systems.SysLandCircle", "line_number": 244, "usage_type": "call"}, {"api_name": "flappy_bird.systems", "line_number": 244, "usage_type": "name"}, {"api_name": "flappy_bird.setting.Global_Param", "line_number": 245, "usage_type": "name"}, {"api_name": "flappy_bird.systems.SysFrameAnimate", "line_number": 247, "usage_type": "call"}, {"api_name": "flappy_bird.systems", "line_number": 247, "usage_type": "name"}, {"api_name": "flappy_bird.setting.Global_Param", "line_number": 248, "usage_type": "name"}, {"api_name": "flappy_bird.systems.SysMove", "line_number": 250, "usage_type": "call"}, {"api_name": "flappy_bird.systems", "line_number": 250, "usage_type": "name"}, {"api_name": "flappy_bird.setting.Global_Param", "line_number": 251, "usage_type": "name"}, {"api_name": "flappy_bird.systems.SysRender", "line_number": 253, "usage_type": "call"}, {"api_name": "flappy_bird.systems", "line_number": 253, "usage_type": "name"}, {"api_name": "flappy_bird.setting.Global_Param", "line_number": 254, "usage_type": "name"}, {"api_name": "esper.World", "line_number": 208, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 259, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 266, "usage_type": "call"}]} +{"seq_id": "644075846", "text": "import os\nfrom typing import Any, Union\n\nimport requests\nfrom PIL import Image\nfrom math import floor\nfrom random import choice\nfrom zp_tools_old import get_team_data, get_team_avitars, get_team_data\n\ndef team_riders_to_csv(team_id, out_file='team_riders.csv'):\n get_team_data(team_id).to_csv(out_file)\n\nclass create_team_collage(object):\n \"\"\"\n img_path: the full path of the output image file.\n avitar_path: Folder to download and or find the avitars\n team_id: The zp team id \"2740\" # CRYO-GEN\n update_all: Suggest False unless its the first run. Get avitars for All or only those not found in the folder. Suggest False\n Example:\n create_team_collage(r'data/collage.jpeg', r'data/avitars', '2740', update_all=False)\n \"\"\"\n def __init__(self, img_path, avitar_path, team_id, update_all):\n self.img_path = img_path\n self.avitar_path = avitar_path\n self. team_id = team_id\n self.update_all = update_all\n self.image_size = None\n\n def make_collage(self):\n '''make a collage from all images in folder'''\n listofimages = [f for f in os.listdir(self.avitar_path) if f.endswith(\".jpeg\")]\n cols = (len(listofimages) ** .5)\n if cols == floor(cols):\n rows = cols\n else:\n cols = floor(cols)\n rows = cols + 1\n filler_count = cols * rows - len(listofimages)\n for c in range(filler_count):\n listofimages.append(choice(listofimages))\n thumbnail_width = self.image_size // cols\n thumbnail_height = self.image_size // rows\n size = thumbnail_width, thumbnail_height\n new_im = Image.new('RGB', (self.image_size, self.image_size))\n ims = []\n for p in listofimages:\n im = Image.open(self.avitar_path + '/' + p)\n im.thumbnail(size)\n ims.append(im)\n i = 0\n x = 0\n y = 0\n for col in range(cols):\n for row in range(rows):\n print(i, x, y)\n try:\n new_im.paste(ims[i], (x, y))\n i += 1\n y += thumbnail_height\n except:\n continue\n x += thumbnail_width\n y = 0\n\n new_im.save(self.img_path)\n\n def make(self):\n headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.88 Safari/537.36'}\n s = requests.Session()\n team_ids = get_team_data(self.team_id, headers=headers, s=s)\n team_size = len(team_ids['zwid'])\n self.image_size = floor(team_size**.5)*100\n print(f'Found {team_size} team members, image size wil be {self.image_size} square')\n get_team_avitars(team_ids['zwid'], out_path=self.avitar_path, update_all=False, headers=headers, s=s)\n self.make_collage()\n\n", "sub_path": "zp_team.py", "file_name": "zp_team.py", "file_ext": "py", "file_size_in_byte": 2883, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "zp_tools_old.get_team_data", "line_number": 11, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 31, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 33, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 36, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 40, "usage_type": "call"}, {"api_name": "PIL.Image.new", "line_number": 44, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 44, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 47, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 47, "usage_type": "name"}, {"api_name": "requests.Session", "line_number": 69, "usage_type": "call"}, {"api_name": "zp_tools_old.get_team_data", "line_number": 70, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 72, "usage_type": "call"}, {"api_name": "zp_tools_old.get_team_avitars", "line_number": 74, "usage_type": "call"}]} +{"seq_id": "212368502", "text": "import logging\n\n\n# common config param\nis_training = True\nlog_dir=\"/home/daiab/Documents/log/\"\ndata_dir=\"/home/daiab/data/\"\nsave_dir=\"/home/daiab/save_model/\"\n\ntfrecords_filename_train = [data_dir + 'train.tfrecords']\ntfrecords_filename_test = [data_dir + 'test.tfrecords']\n\ntest_txt_file = \"/home/daianbo/code/Filelist_LFW_5Pts.txt\"\nfeature_txt_file = \"/home/daianbo/data/feature.txt\"\nsave_model_path = save_dir + \"tf_model.ckpt\"\n\n\n# hyper parameters\nbase_lr = 0.03\nmomentum = 0.9\npower=0.5\nweight_decay=0.0004\nbatch_size = 448\n\n\n# model parameters\nclass_num = 10575\n\n\n# train parameters\niter_num = 80000\ndecay_steps=iter_num\nprint_loss_step = 40\nsave_model_step = 5000\nqueue_capacity = 200\nnum_threads = 2\nis_writer_summary=False\nsummary_dir=log_dir + \"summary\"\nis_sync = True\n\n\ndef get_logger(file_name):\n logging.basicConfig(level=logging.DEBUG,\n format='%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s',\n datefmt='%b %d %Y %H:%M:%S',\n filename=log_dir + 'tensorflow.log',\n filemode='w')\n return logging.getLogger(file_name)\n\n\n\n", "sub_path": "tfm/snapshot/CRU_net/net_config.py", "file_name": "net_config.py", "file_ext": "py", "file_size_in_byte": 1174, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "logging.basicConfig", "line_number": 43, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 43, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "540567770", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# Copyright (C) 2011-2014 WikiTeam\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# 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# You should have received a copy of the GNU General Public License\n# along with this program. If not, see .\n\n\nimport argparse\nimport os\nimport sys\nimport time\nimport re # regex\nfrom urllib.request import urlopen\n### TODOLIST\n# interactive\n# error generator\n# log\n# md5checker\n\n\ndef main():\n parser = argparse.ArgumentParser(description='Downloader of Wikimedia Dumps')\n parser.add_argument('-m', '--mirrors', nargs='?', type=int, help='Use mirror links instead of wikimedia. Such as 1:https://dumps.wikimedia.your.org 2:http://wikipedia.c3sl.ufpr.br', required=False)\n parser.add_argument('-d', '--dates', nargs='?', help='Set the date of the dumps. Default = latest', required=False)\n parser.add_argument('-p', '--projects', help='Choose which wikimedia projects to download (e.g. all, wikipedia, wikibooks, wiktionary, wikimedia, wikinews, wikiversity, wikiquote, wikisource, wikivoyage)', required=False)\n parser.add_argument('-r', '--maxretries', help='Max retries to download a dump when md5sum doesn\\'t fit. Default: 3', required=False)\n parser.add_argument('-l', '--locales', nargs='+', help='Choose which language dumps to download (e.g)', required=False)\n args = parser.parse_args()\n \n \n # Dumps Domain and Mirror\n if args.mirrors == 1:\n dumpsdomain = 'https://dumps.wikimedia.your.org'\n elif args.mirrors == 2:\n dumpsdomain = 'http://wikipedia.c3sl.ufpr.br'\n else:\n dumpsdomain = 'https://dumps.wikimedia.org'\n\n with urlopen('{}/backup-index.html'.format(dumpsdomain)) as url:\n html = url.read().decode('utf-8')\n\n # Dumps Date\n if args.dates:\n dates = args.dates\n else:\n dates = 20181101 # default testing\n \n # Projects selection\n if args.projects:\n proj = args.projects\n else:\n proj = ['wiki','wikibooks','wiktionary','wikiquote','wikimedia','wikisource','wikinews','wikiversity','wikivoyage']\n\n # Retry downloads when MD5 checker not match\n # Default = 3\n maxretries = 3\n if args.maxretries and int(args.maxretries) >= 0:\n maxretries = int(args.maxretries)\n\n\n # Set the locale\n allLocale = []\n if args.locales:\n allLocale = args.locales\n else:\n with open('wikilocale.txt', 'r') as filehandle:\n for line in filehandle:\n # remove linebreak which is the last character of the string\n currentPlace = line[:-1]\n allLocale.append(currentPlace)\n # print (allLocale)\n\n # I thought need to verify user input first but then I realized no need to verify..\n # if user give wrong input, the regex will not return any matches\n\n # # Get all the locale from wikimedia dumps by the format of [localewiki....]\n # REGEXLANG = r'.*)wi.*/[^<]+: Dump complete'\n # m = re.compile(REGEXLANG)\n # lang = []\n # for match in re.finditer(m, html):\n # lang.append(match.group('lang'))\n \n # locale = []\n # for x in allLocale:\n # if x in lang:\n # locale.append(x) \n\n\n locale = allLocale\n fulldumps = []\n # Regex to get date from the html page\n for l in locale:\n for p in proj:\n REGEXPROJDATE = r'%s)(?P%s)/(?P%s)\">[^<]+: Dump complete' % (l, p, dates)\n m = re.compile(REGEXPROJDATE)\n for match in re.finditer(m, html):\n # print(match)\n fulldumps.append([match.group('language'), match.group('project'),match.group('date')])\n # print(fulldumps)\n \n for locale, project, date in fulldumps:\n print (('-' * 50, '\\n', 'Checking', locale, project, date, '\\n', '-' * 50))\n time.sleep(1) # ctrl-c\n f = urlopen('%s/%s%s/%s/' % (dumpsdomain, locale, project, date))\n htmlproj = f.read().decode('utf-8')\n # print (htmlproj)\n f.close()\n\n for dumptypes in ['pages-meta-history\\d*\\.xml[^\\.]*\\.7z']:\n corrupted = False\n maxRetriesCheck = maxretries\n while (not corrupted) and maxRetriesCheck > 0:\n maxRetriesCheck -=1\n # refer \"/enwiki/20181101/enwiki-20181101-pages-meta-history1.xml-p26584p28273.7z\"\n m = re.compile(r'/%s%s/%s/%s%s-%s-%s)\">' % (locale,project,date,locale,project,date,dumptypes))\n # enwiki is have many files, looping is required\n urldumps = []\n for match in re.finditer(m, htmlproj):\n urldumps.append('%s/%s' % (dumpsdomain, match.group('urldump')))\n \n \n print (urldumps)\n for urldump in urldumps:\n dumpfilename = urldump.split('/')[-1]\n path = '%s/%s%s' % (dumpfilename[0], locale, project)\n if not os.path.exists(path):\n os.makedirs(path)\n # wget continue downloadlink log to path with dumpfilename\n os.system('wget --continue %s -O %s/%s' % (urldump, path, dumpfilename))\n\n # # md5check\n # os.system('md5sum %s/%s > md5' % (path, dumpfilename))\n # f = open('md5', 'r')\n # raw = f.read()\n # f.close()\n # md51 = re.findall(\n # r'(?P[a-f0-9]{32})\\s+%s/%s' % (path, dumpfilename), raw)[0]\n # print ((md51))\n\n # f = urlopen(\n # '%s/%s/%s/%s-%s-md5sums.txt' % (dumpsdomain, project, date, project, date)).decode('utf-8')\n # raw = f.read()\n # f.close()\n # f = open('%s/%s-%s-md5sums.txt' %\n # (path, project, date), 'w')\n # f.write(raw)\n # f.close()\n # md52 = re.findall(\n # r'(?P[a-f0-9]{32})\\s+%s' % (dumpfilename), raw)[0]\n # print ((md52))\n\n # if md51 == md52:\n # print ('md5sum is correct for this file, horay! \\o/')\n # print ('\\n' * 3)\n # corrupted = False\n # else:\n # os.remove('%s/%s' % (path, dumpfilename)) \n\nif __name__ == '__main__':\n main()\n", "sub_path": "wdcli.py", "file_name": "wdcli.py", "file_ext": "py", "file_size_in_byte": 7090, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 30, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 47, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 103, "usage_type": "call"}, {"api_name": "re.finditer", "line_number": 104, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 111, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 112, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 123, "usage_type": "call"}, {"api_name": "re.finditer", "line_number": 126, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path", "line_number": 134, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 135, "usage_type": "call"}, {"api_name": "os.system", "line_number": 137, "usage_type": "call"}]} +{"seq_id": "546724978", "text": "import numpy as np\n\nimport logging\nimport warnings\n\nfrom .visualization import vis_image, vis_grayscale, vis_categorical_mask, vis_flow, display_plt, animate_plt\nfrom .utils import isinteger, calculate_grid_layout, tensor_to_array\n\nlogger = logging.getLogger('TorchShow')\nlogger.setLevel(logging.INFO)\n\nvis_func_dict = dict(image=vis_image,\n grayscale=vis_grayscale,\n categorical_mask=vis_categorical_mask,\n flow=vis_flow)\n\ndef save(x, path=None):\n show(x, save=True, file_path=path)\n\n\ndef show(x, display=True, **kwargs):\n vis_list = None\n \n x = tensor_to_array(x)\n\n if isinstance(x, (np.ndarray)):\n x = x.copy()\n nrows = kwargs.get('nrows', None)\n ncols = kwargs.get('ncols', None)\n channel_mode = kwargs.get('channel_mode', 'auto')\n if channel_mode == 'auto':\n if x.shape[-1] in [1,2,3]:\n channel_mode = 'channel_last'\n else:\n channel_mode = 'channel_first'\n \n if x.ndim == 4: # (N, C, H, W) like array\n if channel_mode == 'channel_first':\n N, _, H, W = x.shape\n elif channel_mode == 'channel_last':\n N, H, W, _ = x.shape\n \n nrows, ncols = calculate_grid_layout(N, H, W, nrows, ncols)\n assert (nrows * ncols >= N)\n vis_list = [list(x[i:i + ncols]) for i in range(0, N, ncols)] # vis_list is now an list of list\n\n \n elif x.ndim == 3: # (C, H, W) like array\n if channel_mode == 'channel_first': # C, H, W\n C, H, W = x.shape\n elif channel_mode == 'channel_last':\n H, W, C = x.shape\n \n if C <=3:\n vis_list = [[x]] # if C is in [1,2,3], visualize it as single image\n else: # when C is greater than 3 (e.g. feature maps), visualize it in grid layout\n if channel_mode == 'channel_last':\n x = np.transpose(x, (2,0,1)) # Transpose to C, H, W because we will visualize each individual channel\n nrows, ncols = calculate_grid_layout(C, H, W, nrows, ncols)\n assert (nrows * ncols >= C)\n vis_list = [list(x[i:i + ncols]) for i in range(0, C, ncols)]\n \n elif x.ndim == 2: # (H, W)\n vis_list = [[x]]\n \n else:\n raise TypeError(\"Unsupported shape of numpy array {} .\".format(x.shape))\n \n elif isinstance(x, list):\n if isinstance(x[0], np.ndarray): # if the input is list of images [img1, img2], make it [[img1, img2]]\n vis_list = [x]\n else:\n vis_list = x\n\n else:\n raise NotImplementedError(\"Does not support input type \\\"{}\\\"\".format(type(x)))\n\n\n # vis_list: list of list. Outer list is for rows and inner list is the images in each row.\n # e.g.[[img1, img2], \n # [img3, img4]]\n \n assert isinstance(vis_list, list)\n assert np.array([isinstance(l, list) for l in vis_list]).all() # Now the input should be list of list\n\n plot_list = []\n\n for row in vis_list: \n list_per_row = []\n for img in row:\n vis, plot_cfg = visualize(img, **kwargs)\n list_per_row.append((vis, plot_cfg))\n plot_list.append(list_per_row)\n \n if display:\n display_plt(plot_list, **kwargs)\n\n\ndef show_video(x, display=True, **kwargs):\n video_list = None\n \n x = tensor_to_array(x)\n\n if isinstance(x, (np.ndarray)):\n x = x.copy()\n assert x.ndim in [3,4], \"only support 3D array (N, H, W) or 4D array (N, C, H, W) in video mode\"\n video_list = [[x]] # for a single video, make it [[vid]]\n \n elif isinstance(x, list):\n if isinstance(x[0], np.ndarray): # if the input is list of array [vid1, vid2], make it [[vid1, vid2]]\n video_list = [x]\n else:\n video_list = x\n \n else:\n raise NotImplementedError(\"Does not support input type \\\"{}\\\"\".format(type(x)))\n\n\n # video_list: list of list. Outer list is for rows and inner list is the images in each row.\n # e.g.[[img1, img2], \n # [img3, img4]]\n \n assert isinstance(video_list, list)\n assert np.array([isinstance(l, list) for l in video_list]).all() # Now the input should be list of list\n\n video_length = max([len(vid) for l in video_list for vid in l])\n\n video_vis_list = [] # Reorganize frames into [t, row, col, img]\n\n for t in range(video_length): \n frames_at_t = [] # [[frame_t_video1, frame_t_video2],\n # [frame_t_video3, frame_t_video4]]\n for row in video_list: \n frames_at_t_per_row = [] # [frame_t_video1, frame_t_video2]\n for video in row:\n if t < len(video):\n img = video[t]\n vis, plot_cfg = visualize(img, **kwargs)\n else:\n vis, plot_cfg = (None, None)\n\n frames_at_t_per_row.append((vis, plot_cfg)) # \n frames_at_t.append(frames_at_t_per_row) # \n video_vis_list.append(frames_at_t)\n \n if display:\n return animate_plt(video_vis_list, **kwargs)\n \n\ndef visualize(x, \n mode='auto',\n auto_permute=True,\n **kwargs):\n\n assert isinstance(x, np.ndarray)\n \n shape = x.shape\n ndim = len(shape)\n assert ndim <= 3\n \n if auto_permute:\n if (ndim == 3) and (shape[0] in [1, 2, 3]): # For C, H, W kind of array.\n logger.debug('Detected input shape {} is in CHW format, TorchShow will automatically convert it to HWC format'.format(shape))\n x = np.transpose(x, (1,2,0))\n\n if ndim == 2:\n x = np.expand_dims(x, axis=-1)\n \n if mode=='auto':\n mode = infer_mode(x)\n \n vis_func = vis_func_dict.get(mode, None)\n \n if vis_func == None:\n raise ValueError(\"mode {} is not supported.\".format(mode))\n \n return vis_func(x, **kwargs)\n \n\ndef infer_mode(x):\n shape = x.shape\n ndim = len(shape)\n if shape[-1] == 3:\n mode = 'image'\n if shape[-1] == 2:\n mode = 'flow'\n if shape[-1] == 1:\n if (x.min() >= 0) and (x.max() <= 1):\n mode = 'grayscale'\n if isinteger(np.unique(x)).all(): # If values are all integer\n mode = 'categorical_mask'\n else:\n mode = 'grayscale'\n return mode\n\n", "sub_path": "torchshow/torchshow.py", "file_name": "torchshow.py", "file_ext": "py", "file_size_in_byte": 6481, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 10, "usage_type": "attribute"}, {"api_name": "visualization.vis_image", "line_number": 12, "usage_type": "name"}, {"api_name": "visualization.vis_grayscale", "line_number": 13, "usage_type": "name"}, {"api_name": "visualization.vis_categorical_mask", "line_number": 14, "usage_type": "name"}, {"api_name": "visualization.vis_flow", "line_number": 15, "usage_type": "name"}, {"api_name": "utils.tensor_to_array", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 26, "usage_type": "attribute"}, {"api_name": "utils.calculate_grid_layout", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 58, "usage_type": "call"}, {"api_name": "utils.calculate_grid_layout", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 70, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 84, "usage_type": "call"}, {"api_name": "visualization.display_plt", "line_number": 96, "usage_type": "call"}, {"api_name": "utils.tensor_to_array", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 104, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 110, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 124, "usage_type": "call"}, {"api_name": "visualization.animate_plt", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 155, "usage_type": "attribute"}, {"api_name": "numpy.transpose", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 167, "usage_type": "call"}, {"api_name": "utils.isinteger", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 190, "usage_type": "call"}]} +{"seq_id": "290055861", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom mathutils import Quaternion\nfrom blender_to_traj import Blender_to_Traj\nfrom msg_to_traj import Msg_to_Traj\n\n\ndef cal_scalar_error(br_list, mr_list):\n skalar = br_list / mr_list\n skalar = np.delete(skalar, 0, 0)\n return skalar, np.average(skalar)\n\n\ndef scale_corrector(pose_vslam, pose_gps):\n window = 11\n steps = len(pose_gps)\n i = 0\n pose_corrected = np.copy(pose_vslam)\n opt_scale = 1\n while ((window + i) < steps):\n x_mat = pose_vslam[i:window + i]\n y_mat = pose_gps[i:window + i]\n\n sigmax2 = get_sigma(x_mat)\n sigmay2 = get_sigma(y_mat)\n sigmaxy = np.sqrt(sigmax2 * sigmay2)\n\n sxx = sigmay2 * np.sum(x_mat ** 2)\n syy = sigmax2 * np.sum(y_mat ** 2)\n sxy = sigmaxy * np.trace(x_mat.T @ y_mat)\n\n subsxy = sxx - syy\n opt_scale = subsxy + np.sign(sxy) * np.sqrt(subsxy ** 2 + 4 * sxy ** 2)\n opt_scale = opt_scale * 0.5 * np.sqrt(sigmax2 / sigmay2) / sxy\n scale_norm = opt_scale ** 2 * sigmay2 + sigmax2\n pose_corrected[i] = (opt_scale * sigmay2 * pose_vslam[i] + sigmax2 * pose_gps[i]) / scale_norm\n # pose_corrected[i] *=opt_scale\n i += 1\n\n pose_corrected[-window:] = opt_scale * pose_vslam[-window:]\n\n return pose_corrected\n\n\ndef get_sigma(sample):\n l_sample = len(sample)\n sigma_mu2 = 0\n for i in range(1, l_sample - 1):\n sigma_mu2 += np.sum((sample[i - 1] - 2 * sample[i] + sample[i + 1]) ** 2)\n\n return sigma_mu2 / ((l_sample - 3) * 3)\n\n\ndef get_corresponding_frame(b_list, m_list, skip_fr=3):\n framelist = np.array(skip_fr * m_list[:, 1], dtype=int)\n return b_list[framelist, 1:3]\n\n\ndef plot_traj(pose_b, pose_m, pose_scaled):\n fig = plt.figure()\n\n ax = fig.gca()\n # draw groud truth trajectory\n ax.plot(pose_b[:, 0], pose_b[:, 1], \"-\", color='tab:red', label='groud truth trajectory')\n ax.plot(pose_b[0, 0], pose_b[0, 1], color=\"red\")\n # ax.scatter(pose_b[10, 0], pose_b[10, 1], color=\"green\")\n ax.scatter(pose_b[-1, 0], pose_b[-1, 1], color=\"purple\")\n\n # draw OpenVSLAM trajectory\n ax.plot(pose_m[:, 0], pose_m[:, 1], \"-\", color='tab:blue', label='OpenVSLAM trajectory')\n ax.plot(pose_m[0, 0], pose_m[0, 1], color=\"red\")\n # ax.scatter(pose_m[10, 0], pose_m[10, 1], color=\"green\")\n ax.scatter(pose_m[-1, 0], pose_m[-1, 1], color=\"purple\")\n\n # draw scaled trajectory\n ax.plot(pose_scaled[:, 0], pose_scaled[:, 1], \"-\", color='g', label='scaled trajectory')\n ax.plot(pose_scaled[0, 0], pose_scaled[0, 1], color=\"red\")\n # ax.scatter(pose_b[10, 0], pose_b[10, 1], color=\"green\")\n ax.scatter(pose_scaled[-1, 0], pose_scaled[-1, 1], color=\"purple\")\n\n ax.legend() # 画一条空间曲线\n ax.set_xlabel('X/m', fontsize=16)\n ax.set_ylabel('Y/m', fontsize=16)\n\n # plt.show()\n\n\ndef plot_all(traj_b, traj_m, baseline_b, baseline_m, scalar_list, ave):\n fig = plt.figure()\n figManager = plt.get_current_fig_manager()\n figManager.window.showMaximized()\n\n ###################### plot ground truth baseline info #####################################\n ax = fig.add_subplot(231)\n ax.plot(baseline_b, color='tab:red', label='ground truth')\n ax.set_ylabel('baseline/m', fontsize=12)\n ax.set_title('Ground Truth', fontsize=16)\n ax.tick_params(axis='x', rotation=0, labelsize=12)\n ax.tick_params(axis='y', rotation=0)\n ax.grid(alpha=.4)\n ax.grid(True)\n\n # ax.set_ylim(0, 8) # skip3 16\n b_sum = 0\n for i, _ in enumerate(baseline_b):\n b_sum += baseline_b[i] ** 2\n b_RMSE = np.sqrt(b_sum / len(baseline_b))\n b_str = 'Groud Truth baseline:{:.3f}m'.format(b_RMSE)\n plt.text(0, 7, b_str, fontsize=14) # skip3 14\n\n ##################### plot groud truth distribution ########################################\n ax = fig.add_subplot(232)\n counts, bins = np.histogram(baseline_b, bins=30)\n ax.hist(bins[:-1], bins, weights=counts, facecolor='tab:red', alpha=0.75)\n ax.set_ylabel(\"frequency\", fontsize=12)\n ax.tick_params(axis='y') # , labelcolor='tab:blue'\n ax.set_title(\"Distribution of Baseline Length\", fontsize=16)\n\n ##################### draw groud truth trajectory ########################################\n ax = fig.add_subplot(233)\n ax.plot(traj_b[:, 0], traj_b[:, 1], \"-\", color='tab:red', label='groud truth trajectory')\n ax.plot(traj_b[0, 0], traj_b[0, 1], color=\"red\")\n # ax.scatter(traj_b[10, 0], traj_b[10, 1], color=\"green\")\n ax.scatter(traj_b[-1, 0], traj_b[-1, 1], color=\"purple\")\n\n # draw OpenVSLAM trajectory\n ax.plot(traj_m[:, 0], traj_m[:, 1], \"-\", color='tab:blue', label='corrected OpenVSLAM trajectory')\n ax.plot(traj_m[0, 0], traj_m[0, 1], color=\"red\")\n ax.scatter(traj_m[-1, 0], traj_m[-1, 1], color=\"purple\")\n ax.set_title('trajectory compare', fontsize=16)\n ax.legend(fontsize=14) # 画一条空间曲线\n ax.set_xlabel('X/m', fontsize=10)\n ax.set_ylabel('Y/m', fontsize=10)\n\n ##################### plot corrected baseline info ########################################\n ax = fig.add_subplot(234)\n ax.plot(baseline_m, color='tab:blue', label='corrected OpenVSLAM')\n ax.set_xlabel('frame', fontsize=12)\n ax.set_ylabel('baseline/m', fontsize=12)\n ax.set_title('corrected OpenVSLAM', fontsize=16)\n ax.tick_params(axis='x', rotation=0, labelsize=12)\n ax.tick_params(axis='y', rotation=0)\n ax.grid(alpha=.4)\n ax.grid(True)\n m_sum = 0\n for i, _ in enumerate(baseline_m):\n m_sum += baseline_m[i] ** 2\n m_RMSE = np.sqrt(m_sum / len(baseline_m))\n m_str = 'corrected OpenVSLAM baseline:{:.3f}m'.format(m_RMSE)\n plt.text(0, 0.75, m_str, fontsize=14) # skip3 1.75\n # ax.set_ylim(0, 7) # skip3 1.95\n\n ##################### draw groud truth trajectory ########################################\n ax = fig.add_subplot(235)\n counts, bins = np.histogram(baseline_m, bins=30)\n ax.hist(bins[:-1], bins, weights=counts, facecolor='tab:blue', alpha=0.75)\n ax.set_ylabel(\"frequency\", fontsize=12)\n ax.tick_params(axis='y')\n ax.set_xlabel('baseline/m', fontsize=12)\n\n ############################### draw scalar error ########################################\n ax = fig.add_subplot(236)\n line = np.ones((len(scalar_list), 1)) * ave / ave\n ax.plot(scalar_list, 'g+')\n ax.plot(line, '_')\n ax.set_title('scalar compare', fontsize=16)\n ax.set_xlabel(\"keyframe\", fontsize=12)\n s_str = 'scalar error mean:{:.3f}'.format(ave)\n plt.text(0, 1.5, s_str, fontsize=14) # skip3 9\n ax.set_ylim(0, 3) # skip3 0,10\n\n\ndef main():\n if USE_RESIDENT:\n msgpath = 'resident_EQT_0p5_skip0_kp2000_noloop.msg'\n blenderpath = \"Camera_Resident_noisy_blender.csv\"\n # blenderpath = 'Camera_Resident_ideal_blender.csv'\n if USE_UNI:\n blenderpath = 'Camera_Uni_ideal_blender.csv'\n\n msgfolderpath = 'saved_data/from_msg_data/'\n blenderfolderpath = 'saved_data/from_blender_csv/'\n msgpath = 'resident_EQT_0p5_skip0_kp2000_noloop.msg'\n msgtraj = Msg_to_Traj(msgpath)\n blendertraj = Blender_to_Traj(blenderfolderpath + blenderpath)\n\n # loading msg data\n if LOAD_MSGDATA:\n if USE_RESIDENT:\n load_data = np.load(msgfolderpath + 'saved_resident_EQT_0p5_skip0_kp2000_noloop.npz')\n elif USE_UNI:\n load_data = np.load(msgfolderpath + 'saved_uni_EQT_1200_1p05_skip0_kp4000_loop_s1p9.npz')\n print('file loaded successfully...')\n kf_pose_cw = load_data['keyframe_pose_cw']\n\n else:\n landmarks, keyframe_scale, kf_pose_cw, keyframe_undists = msgtraj.msg_unpack_to_array()\n\n m_pose = kf_pose_cw[kf_pose_cw[:, 0].argsort()] # 按第'1'列排序\n\n m_traj = msgtraj.get_trajectory(m_pose[:, 2:])\n m_baseline = msgtraj.cal_baseline(m_traj)\n\n # adjust map orientation\n m_traj = m_traj[:, ::2] # (82, 2)\n m_traj[:, 1] = -m_traj[:, 1]\n m_traj = blendertraj.rot_traj(m_traj, np.pi * 0 / 4) # resident pi * 2/4 #\n # loading blender data\n b_pose = blendertraj.csv_to_pose()\n if \"noisy\" in blenderpath:\n b_pose[:, [2, 3]] = b_pose[:, [3, 2]]\n\n b_traj = b_pose[:, 1:3]\n b_traj = blendertraj.rot_traj(b_traj, -np.pi * 0 / 4) # resident pi * 0/4 #\n b_baseline = blendertraj.cal_baseline(b_pose[:, 1:4])\n start_bias = b_traj[0, :]\n\n # calculate scalar koefficient\n if USE_RESIDENT:\n b_traj_cp = get_corresponding_frame(b_pose , m_pose, skip_fr=1) - start_bias # resident\n elif USE_UNI:\n b_traj_cp = get_corresponding_frame(b_pose, m_pose, skip_fr=2) - start_bias # uni\n b_traj_cp = blendertraj.rot_traj(b_traj_cp, -np.pi * 5 / 4) # uni -pi * 5/4\n\n br_baseline = blendertraj.cal_baseline(b_traj_cp)\n # scalar, ave_val = cal_scalar_error(br_baseline, m_baseline)\n\n # correct the scalar\n corrected_traj = scale_corrector(m_traj, b_traj_cp)\n corrected_baseline = blendertraj.cal_baseline(corrected_traj)\n scalar, ave_val = cal_scalar_error(br_baseline, corrected_baseline)\n if PLOT_TRAJ:\n plot_traj(b_traj_cp, m_traj, corrected_traj)\n\n if PLOT_ALL:\n plot_all(b_traj_cp, corrected_traj, br_baseline, corrected_baseline, scalar, ave_val)\n plt.show()\n\n\nif __name__ == '__main__':\n USE_ORIGINAL_MAP = True\n USE_RESIDENT = False\n USE_UNI = True\n LOAD_MSGDATA = True\n PLOT_TRAJ = True\n PLOT_BASELINE = False\n PLOT_ALL = False\n main()\n", "sub_path": "compare_traj/scale_corretor.py", "file_name": "scale_corretor.py", "file_ext": "py", "file_size_in_byte": 9420, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "numpy.delete", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.trace", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.get_current_fig_manager", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.text", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "numpy.histogram", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.text", "line_number": 150, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 150, "usage_type": "name"}, {"api_name": "numpy.histogram", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.text", "line_number": 169, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 169, "usage_type": "name"}, {"api_name": "msg_to_traj.Msg_to_Traj", "line_number": 184, "usage_type": "call"}, {"api_name": "blender_to_traj.Blender_to_Traj", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 207, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 214, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 223, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 237, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 237, "usage_type": "name"}]} +{"seq_id": "173600680", "text": "\"\"\"client info new\n\nRevision ID: 241af73796de\nRevises: 58c7ebc5184d\nCreate Date: 2016-11-21 22:06:04.254000\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '241af73796de'\ndown_revision = '58c7ebc5184d'\n\nfrom alembic import op\nimport sqlalchemy as sa\n\n\ndef upgrade():\n ### commands auto generated by Alembic - please adjust! ###\n op.add_column('TClientInfo', sa.Column('email', sa.String(), nullable=True))\n op.add_column('TClientInfo', sa.Column('qq', sa.Integer(), nullable=True))\n op.add_column('TClientInfo', sa.Column('weixin', sa.String(), nullable=True))\n op.create_index('ix_TClientInfo_id', 'TClientInfo', ['id'], unique=False)\n ### end Alembic commands ###\n\n\ndef downgrade():\n ### commands auto generated by Alembic - please adjust! ###\n op.drop_index('ix_TClientInfo_id', 'TClientInfo')\n op.drop_column('TClientInfo', 'weixin')\n op.drop_column('TClientInfo', 'qq')\n op.drop_column('TClientInfo', 'email')\n ### end Alembic commands ###\n", "sub_path": "migrations/versions/241af73796de_client_info_new.py", "file_name": "241af73796de_client_info_new.py", "file_ext": "py", "file_size_in_byte": 991, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "alembic.op.add_column", "line_number": 19, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 19, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 19, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 19, "usage_type": "call"}, {"api_name": "alembic.op.add_column", "line_number": 20, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 20, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 20, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 20, "usage_type": "call"}, {"api_name": "alembic.op.add_column", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op.create_index", "line_number": 22, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 22, "usage_type": "name"}, {"api_name": "alembic.op.drop_index", "line_number": 28, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 28, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 29, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 29, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 30, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 30, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 31, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 31, "usage_type": "name"}]} +{"seq_id": "396246305", "text": "from django.db import models\nfrom django.utils.translation import ugettext_lazy as _\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.core.cache import cache\n\nfrom ella.core.cache import CachedForeignKey, CachedGenericForeignKey\nfrom ella_hub.models.permissions import Permission\nfrom ella_hub.managers import StateObjectRelationManager, StateManager\n\n\nclass Workflow(models.Model):\n\n title = models.CharField(_(\"Title\"), max_length=128, blank=False, unique=True)\n description = models.TextField(_(\"Description\"), blank=True)\n initial_state = CachedForeignKey(\n \"State\",\n verbose_name=_(\"Initial state\"),\n blank=True,\n null=True,\n related_name=\"workflow_initial_state\"\n )\n\n permissions = models.ManyToManyField(\n Permission,\n verbose_name=_(\"Permissions\"),\n through=\"WorkflowPermissionRelation\"\n )\n\n def get_initial_state(self):\n if self.initial_state:\n return self.initial_state\n else:\n try:\n return self.states.all()[0]\n except IndexError:\n return None\n\n def set_to_model(self, model):\n content_type = ContentType.objects.get_for_model(model)\n try:\n relation = WorkflowModelRelation.objects.get(content_type=content_type)\n except WorkflowModelRelation.DoesNotExist:\n WorkflowModelRelation.objects.create(content_type=content_type, workflow=self)\n else:\n relation.workflow = self\n relation.save()\n\n def __unicode__(self):\n if self.initial_state:\n return u\"%s : %s\" % (self.title, self.initial_state.title)\n else:\n return u\"%s\" % self.title\n\n class Meta:\n app_label = \"ella_hub\"\n verbose_name = _(\"Workflow\")\n verbose_name_plural = _(\"Workflows\")\n\n\nclass State(models.Model):\n\n title = models.CharField(_(\"Title\"), max_length=128, blank=False)\n codename = models.CharField(_(\"Codename\"), max_length=128, blank=False)\n description = models.TextField(_(\"Description\"), blank=True)\n workflow = CachedForeignKey(\n \"Workflow\",\n verbose_name=_(\"Workflow\"),\n blank=True,\n null=True,\n related_name=\"states\"\n )\n transitions = models.ManyToManyField(\n \"Transition\",\n verbose_name=_(\"Transitions\"),\n blank=True,\n )\n\n objects = StateManager()\n\n def __unicode__(self):\n return u\"%s\" % self.title\n\n class Meta:\n app_label = \"ella_hub\"\n verbose_name = _(\"State\")\n verbose_name_plural = _(\"States\")\n\n\nclass Transition(models.Model):\n\n title = models.CharField(_(\"Title\"), max_length=128, blank=False)\n description = models.TextField(_(\"Description\"), blank=True)\n workflow = CachedForeignKey(\"Workflow\", verbose_name=_(\"Workflow\"), blank=True)\n destination = CachedForeignKey(\"State\", verbose_name=_(\"Destination\"), blank=False)\n\n def __unicode__(self):\n return u\"%s (-> %s)\" % (self.title, self.destination.title)\n\n class Meta:\n app_label = \"ella_hub\"\n verbose_name = _(\"Transition\")\n verbose_name_plural = _(\"Transition\")\n\n\nclass StateObjectRelation(models.Model):\n\n content_type = CachedForeignKey(\n ContentType,\n verbose_name=_(\"Content type\"),\n related_name=\"state_object\",\n blank=True,\n null=True\n )\n content_id = models.PositiveIntegerField(_(\"Content id\"), blank=True, null=True)\n content_object = CachedGenericForeignKey(\"content_type\", \"content_id\")\n state = CachedForeignKey(State, verbose_name=_(\"State\"))\n\n objects = StateObjectRelationManager()\n\n def __unicode__(self):\n return \"%s %s - %s\" % (self.content_type.name, self.content_id, self.state.title)\n\n class Meta:\n app_label = \"ella_hub\"\n unique_together = (\"content_type\", \"content_id\", \"state\")\n verbose_name = _(\"State-Object Relation\")\n verbose_name_plural = _(\"State-Object Relations\")\n\n\nclass StatePermissionRelation(models.Model):\n\n state = CachedForeignKey(\"State\", verbose_name=_(\"State\"))\n permission = CachedForeignKey(\"Permission\", verbose_name=_(\"Permission\"))\n role = CachedForeignKey(\"Role\", verbose_name=_(\"Role\"))\n\n def __unicode__(self):\n return \"%s / %s / %s\" % (self.state.title, self.role.title, self.permission.title)\n\n class Meta:\n app_label = \"ella_hub\"\n unique_together = (('state', 'permission', 'role'),)\n verbose_name = _(\"State-Permission Relation\")\n verbose_name_plural = _(\"State-Permission Relations\")\n\n\nclass WorkflowModelRelation(models.Model):\n\n content_type = CachedForeignKey(\n ContentType,\n verbose_name=_(\"Content Type\"),\n unique=True\n )\n workflow = CachedForeignKey(\n Workflow,\n verbose_name=_(\"Workflow\"),\n related_name=\"wmr_workflow\"\n )\n\n @staticmethod\n def cache_key(pk):\n return \"HUB_get_workflow_%s\" % pk\n\n def save(self, *args, **kwargs):\n if self.pk:\n cache.delete(WorkflowModelRelation.cache_key(self.pk))\n return super(WorkflowModelRelation, self).save(*args, **kwargs)\n\n def __unicode__(self):\n return \"%s / %s\" % (self.content_type.name, self.workflow.title)\n\n class Meta:\n app_label = \"ella_hub\"\n unique_together = (('content_type', 'workflow'),)\n verbose_name = _(\"Workflow-Model Relation\")\n verbose_name_plural = _(\"Workflow-Model Relations\")\n\n\nclass WorkflowPermissionRelation(models.Model):\n\n workflow = CachedForeignKey(\n Workflow,\n verbose_name=_(\"Workflow\"),\n related_name=\"wpr_workflow\"\n )\n permission = CachedForeignKey(Permission, related_name=\"permissions\")\n\n def __unicode__(self):\n return \"%s / %s\" % (self.workflow.title, self.permission.title)\n\n class Meta:\n app_label = \"ella_hub\"\n unique_together = (('workflow', 'permission'),)\n verbose_name = _(\"Workflow-Permission Relation\")\n verbose_name_plural = _(\"Workflow-Permission Relations\")\n", "sub_path": "ella_hub/models/workflow.py", "file_name": "workflow.py", "file_ext": "py", "file_size_in_byte": 6082, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "django.db.models.Model", "line_number": 11, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 11, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 13, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.models.TextField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 14, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 14, "usage_type": "call"}, {"api_name": "ella.core.cache.CachedForeignKey", "line_number": 15, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 23, "usage_type": "call"}, {"api_name": "ella_hub.models.permissions.Permission", "line_number": 24, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 25, "usage_type": "call"}, {"api_name": "django.contrib.contenttypes.models.ContentType.objects.get_for_model", "line_number": 39, "usage_type": "call"}, {"api_name": "django.contrib.contenttypes.models.ContentType.objects", "line_number": 39, "usage_type": "attribute"}, {"api_name": "django.contrib.contenttypes.models.ContentType", "line_number": 39, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 56, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 57, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 60, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 60, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 62, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 62, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 62, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 63, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 63, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 63, "usage_type": "call"}, {"api_name": "django.db.models.TextField", "line_number": 64, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 64, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 64, "usage_type": "call"}, {"api_name": "ella.core.cache.CachedForeignKey", "line_number": 65, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 67, "usage_type": "call"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 72, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 72, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 74, "usage_type": "call"}, {"api_name": "ella_hub.managers.StateManager", "line_number": 78, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 85, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 86, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 89, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 89, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 91, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 91, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 91, "usage_type": "call"}, {"api_name": "django.db.models.TextField", "line_number": 92, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 92, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 92, "usage_type": "call"}, {"api_name": "ella.core.cache.CachedForeignKey", "line_number": 93, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 93, "usage_type": "call"}, {"api_name": "ella.core.cache.CachedForeignKey", "line_number": 94, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 94, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 101, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 102, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 105, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 105, "usage_type": "name"}, {"api_name": "ella.core.cache.CachedForeignKey", "line_number": 107, "usage_type": "call"}, {"api_name": "django.contrib.contenttypes.models.ContentType", "line_number": 108, "usage_type": "argument"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 109, "usage_type": "call"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 114, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 114, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 114, "usage_type": "call"}, {"api_name": "ella.core.cache.CachedGenericForeignKey", "line_number": 115, "usage_type": "call"}, {"api_name": "ella.core.cache.CachedForeignKey", "line_number": 116, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 116, "usage_type": "call"}, {"api_name": "ella_hub.managers.StateObjectRelationManager", "line_number": 118, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 126, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 127, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 130, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 130, "usage_type": "name"}, {"api_name": "ella.core.cache.CachedForeignKey", "line_number": 132, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 132, "usage_type": "call"}, {"api_name": "ella.core.cache.CachedForeignKey", "line_number": 133, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 133, "usage_type": "call"}, {"api_name": "ella.core.cache.CachedForeignKey", "line_number": 134, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 134, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 142, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 143, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 146, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 146, "usage_type": "name"}, {"api_name": "ella.core.cache.CachedForeignKey", "line_number": 148, "usage_type": "call"}, {"api_name": "django.contrib.contenttypes.models.ContentType", "line_number": 149, "usage_type": "argument"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 150, "usage_type": "call"}, {"api_name": "ella.core.cache.CachedForeignKey", "line_number": 153, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 155, "usage_type": "call"}, {"api_name": "django.core.cache.cache.delete", "line_number": 165, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 165, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 174, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 175, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 178, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 178, "usage_type": "name"}, {"api_name": "ella.core.cache.CachedForeignKey", "line_number": 180, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 182, "usage_type": "call"}, {"api_name": "ella.core.cache.CachedForeignKey", "line_number": 185, "usage_type": "call"}, {"api_name": "ella_hub.models.permissions.Permission", "line_number": 185, "usage_type": "argument"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 193, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 194, "usage_type": "call"}]} +{"seq_id": "29462221", "text": "#!/usr/bin/python3\n\"\"\"sends a request to the URL and displays the value of the X-Request-Id\"\"\"\nimport requests\nfrom sys import argv\n\n\nif __name__ == \"__main__\":\n aut = argv[2]\n url = \"https://api.github.com/repos/\" + aut + \"/\" + argv[1] + \"/commits\"\n req = requests.get(url).json()\n commits = 0\n for line in req:\n print(\"{}: {}\".format(line['sha'], line['commit']['author']['name']))\n commits += 1\n if commits == 10:\n exit()\n", "sub_path": "0x11-python-network_1/100-github_commits.py", "file_name": "100-github_commits.py", "file_ext": "py", "file_size_in_byte": 472, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sys.argv", "line_number": 8, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 9, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "430338089", "text": "from ..plugins.plugin import SimStatePlugin\nfrom ..s_action_object import SimActionObject\n\nimport logging\nl = logging.getLogger(\"simuvex.storage.file\")\n\n# TODO: symbolic file positions\nimport itertools\nfile_counter = itertools.count()\n\nclass Flags: # pylint: disable=W0232,\n O_RDONLY = 0\n O_WRTONLY = 1\n O_RDWR = 2\n O_APPEND = 4096\n O_ASYNC = 64\n O_CLOEXEC = 512\n # TODO mode for this flag\n O_CREAT = 256\n O_DIRECT = 262144\n O_DIRECTORY = 2097152\n O_EXCL = 2048\n O_LARGEFILE = 1048576\n O_NOATIME = 16777216\n O_NOCTTY = 1024\n O_NOFOLLOW = 4194304\n O_NONBLOCK = 8192\n O_NODELAY = 8192\n O_SYNC = 67174400\n O_TRUNC = 1024\n\n\ndef _deps_unpack(a):\n if isinstance(a, SimActionObject):\n return a.ast, a.reg_deps, a.tmp_deps\n else:\n return a, None, None\n\n\nclass SimFile(SimStatePlugin):\n # Creates a SimFile\n def __init__(self, name, mode):\n super(SimFile, self).__init__()\n self.name = name\n self.mode = mode\n self.pos = 0\n\n # TODO: handle symbolic names, special cases for stdin/out/err\n # TODO: read content for existing files\n\n @property\n def read_pos(self):\n return self.pos\n\n @read_pos.setter\n def read_pos(self, val):\n self.pos = val\n\n @property\n def write_pos(self):\n return self.pos\n\n @write_pos.setter\n def write_pos(self, val):\n self.pos = val\n\n def _read(self, length, pos, dst_addr=None):\n raise NotImplementedError(\"SimFile._read must be implemented by subclass\")\n\n def read(self, length, pos=None, dst_addr=None):\n '''\n Reads some data from the current (or provided) position of the file.\n If dst_addr is specified, write it to that address.\n '''\n if pos is None:\n load_data = self._read(self.read_pos, length, dst_addr=dst_addr)\n self.read_pos += _deps_unpack(length)[0]\n else:\n load_data = self._read(pos, length, dst_addr=dst_addr)\n\n return load_data\n\n def _write(self, content, length, pos):\n raise NotImplementedError(\"SimFile._write must be implemented by subclass\")\n\n # Writes some data to the current position of the file.\n def write(self, content, length, pos=None):\n # TODO: error handling\n # TODO: symbolic length?\n if pos is None:\n self._write(self.write_pos, content, length)\n self.write_pos += _deps_unpack(length)[0]\n else:\n self._write(pos, content, length)\n\n return length\n\n # Seeks to a position in the file.\n def seek(self, where):\n raise NotImplementedError(\"SimFile.seek must be implemented by subclass\")\n\n # Copies the SimFile object.\n def copy(self):\n raise NotImplementedError(\"SimFile.copy must be implemented by subclass\")\n\n def all_bytes(self):\n raise NotImplementedError(\"SimFile.all_bytes must be implemented by subclass\")\n\n # Merges the SimFile object with others\n def merge(self, others, merge_flag, flag_values):\n raise NotImplementedError(\"SimFile.merge must be implemented by subclass\")\n\n\nclass SimSymbolicFile(SimFile):\n def __init__(self, name, mode, pos=0, content=None):\n super(SimSymbolicFile, self).__init__(name, mode)\n self.pos = pos\n self.content = SimSymbolicMemory(memory_id=\"file_%s_%d\" % (name, file_counter.next())) if content is None else content\n\n def set_state(self, st):\n super(SimSymbolicFile, self).set_state(st)\n self.content.set_state(st)\n\n def _read(self, pos, length, dst_addr=None):\n if dst_addr is None:\n return self.content.load(pos, length)\n else:\n return self.content.copy_contents(dst_addr, pos, length, dst_memory=self.state.memory)\n\n def _write(self, pos, content, length):\n # TODO: something about length\n self.content.store(pos, content)\n\n def seek(self, where):\n self.pos = where\n\n def copy(self):\n return SimSymbolicFile(self.name, self.mode, pos=self.pos, content=self.content.copy())\n\n def all_bytes(self):\n indexes = self.content.mem.keys()\n if len(indexes) == 0:\n raise SimFileError('no content in file %s' % self.name)\n\n min_idx = min(indexes)\n max_idx = max(indexes)\n buff = [ ]\n for i in range(min_idx, max_idx+1):\n buff.append(self.content.load(i, 1))\n return self.state.se.Concat(*buff)\n\n def merge(self, others, merge_flag, flag_values):\n if not all(isinstance(oth, SimSymbolicFile) for oth in others):\n raise SimMergeError(\"merging files of different types is not supported\")\n\n all_files = list(others) + [ self ]\n\n if len(set(o.pos for o in all_files)) > 1:\n l.warning(\"Cheap HACK to support multiple file positions in a merge.\")\n # self.pos = max(o.pos for o in all_files)\n # max cannot be used as file positions might be symbolic.\n max_pos = None\n for o in all_files:\n if max_pos is not None:\n comp = self.state.se.simplify(max_pos >= o.pos)\n if self.state.se.symbolic(comp):\n import ipdb; ipdb.set_trace()\n raise SimMergeError(\"merging file positions with symbolic max position is not ye supported (TODO)\")\n\n max_pos = o.pos if self.state.se.is_false(comp) else max_pos\n else:\n max_pos = o.pos\n self.pos = max_pos\n\n #if len(set(o.name for o in all_files)) > 1:\n # raise SimMergeError(\"merging file names is not yet supported (TODO)\")\n\n #if len(set(o.mode for o in all_files)) > 1:\n # raise SimMergeError(\"merging modes is not yet supported (TODO)\")\n\n return self.content.merge([ o.content for o in others ], merge_flag, flag_values)\n\n\nclass SimConcreteFile(SimFile):\n def __init__(self, name, mode, content, pos=0, tag=False):\n super(SimConcreteFile, self).__init__(name, mode)\n self.pos = pos\n self.content = content\n self.tag = tag\n\n def _read(self, pos, length, dst_addr=None):\n # worry about symbolic later...\n pos = self.state.se.any_int(pos)\n length = self.state.se.any_int(length)\n data = self.content[pos:pos+length]\n data += '\\x00'*(length - len(data))\n if self.tag:\n parts = (self.state.se.BVV(ord(c), 8, name=('%s_%d' % (self.name, pos + i)))\n for (i, c)\n in enumerate(data))\n bv_data = self.state.se.Concat(*parts)\n else:\n bv_data = self.state.BVV(data)\n\n if dst_addr is None:\n return bv_data\n else:\n self.state.memory.store(dst_addr, bv_data, size=length)\n return bv_data # is this necessary?\n\n def _write(self, pos, content, length):\n data = self._read(pos, length)\n self.state.add_constraints(content == data)\n\n def copy(self):\n return SimConcreteFile(self.name, self.mode, self.content, pos=self.pos, tag=self.tag)\n\n def all_bytes(self):\n return self._read(0, len(self.content))\n\n\nclass SimPCAPFile(SimFile):\n def __init__(self, name, mode, pcap, pos=0):\n super(SimPCAPFile, self).__init__(name, mode, pos=pos)\n\n\nfrom ..plugins.symbolic_memory import SimSymbolicMemory\nfrom ..s_errors import SimMergeError, SimFileError\n", "sub_path": "simuvex/storage/file.py", "file_name": "file.py", "file_ext": "py", "file_size_in_byte": 7469, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "logging.getLogger", "line_number": 5, "usage_type": "call"}, {"api_name": "itertools.count", "line_number": 9, "usage_type": "call"}, {"api_name": "s_action_object.SimActionObject", "line_number": 34, "usage_type": "argument"}, {"api_name": "plugins.plugin.SimStatePlugin", "line_number": 40, "usage_type": "name"}, {"api_name": "ipdb.set_trace", "line_number": 167, "usage_type": "call"}]} +{"seq_id": "393253720", "text": "# pylint: disable=invalid-name\n# pylint: disable=line-too-long\nfrom array import array as Array\nimport time\nimport struct\nimport sys\nimport traceback\nimport serial\nimport struct\nimport ecc\nimport flashdevice_defs\n\nclass IO:\n def __init__(self, device='/dev/ttyACM0', use_sd = False, debug = 0, simulation_mode = False):\n self.Debug = debug\n self.PageSize = 0\n self.OOBSize = 0\n self.PageCount = 0\n self.BlockCount = 0\n self.PagePerBlock = 0\n self.BitsPerCell = 0\n self.WriteProtect = True\n self.CheckBadBlock = True\n self.RemoveOOB = False\n self.UseSequentialMode = False\n self.UseAnsi = False\n self.Identified = False\n self.SimulationMode = simulation_mode\n\n self.ser = serial.Serial(device)\n self.use_sd = use_sd\n\n for i in range(20):\n self.ser.write(b'\\x00')\n if b\"BBIO1\" not in self.ser.read(5):\n print(\"Could not get into bbIO mode\")\n quit()\n else:\n if self.Debug > 0:\n print(\"Into BBIO mode\")\n\n self.ser.write(b'\\x0A')\n if b\"FLA1\" not in self.ser.read(4):\n print(\"Cannot set flash mode\")\n quit()\n else:\n if self.Debug > 0:\n print(\"Switched to flash mode\")\n\n self.ser.write(b'\\x02')\n if self.ser.read(1) != b'\\x01':\n print(\"Error setting chip en low\")\n quit()\n else:\n if self.Debug > 0:\n print(\"NAND flash enabled\")\n\n self.__wait_ready()\n self.__get_id()\n\n def __del__(self):\n if self.use_sd:\n self.__disable_sd()\n self.ser.write(b'\\x00')\n self.ser.write(b'\\x0f\\n')\n self.ser.close()\n\n def __enable_sd(self):\n if not self.use_sd:\n self.ser.write(b'\\x0b')\n if self.ser.read(1) != b'\\x01':\n print(\"Error enabling SD card write\")\n\n def __disable_sd(self):\n if self.use_sd:\n self.ser.write(b'\\x0a')\n self.ser.read(1)\n\n\n def __wait_ready(self):\n while 1:\n self.ser.write(b'\\x08')\n if self.ser.read(1) != b'\\x01':\n if self.Debug > 0:\n print('Not Ready')\n else:\n return\n\n def __send_cmd(self, cmd):\n self.ser.write(b'\\x06'+ bytes([cmd]))\n if self.ser.read(1) != b'\\x01':\n print(\"Error setting command\")\n if self.Debug > 0:\n print(\"Was \" + hex(cmd))\n quit()\n\n\n def __send_address(self, addr, count):\n data = b''\n\n for _ in range(0, count, 1):\n data += bytes([(addr & 0xff)])\n addr = addr>>8\n\n self.ser.write(bytes([0x10+(count-1)]))\n self.ser.write(data)\n if self.ser.read(1) != b'\\x01':\n print(\"Error setting address\")\n if self.Debug > 0:\n print(\"Was \" + data.encode('hex'))\n quit()\n\n def __get_status(self):\n self.__send_cmd(0x70)\n status = self.__read_data(1)[0]\n return status\n\n def __read_data(self, count):\n self.ser.write(b\"\\x04\\x00\\x00\"+struct.pack('>h',count))\n if self.ser.read(1) == b'\\x01':\n if not self.use_sd:\n data = self.ser.read(count)\n else:\n data = bytes(count)\n return data\n else:\n print(\"Error getting data\")\n if self.Debug > 0:\n print(\"Should have read \" + str(count) + \" bytes\")\n quit()\n\n def __write_data(self, data):\n self.ser.write(b'\\x04'+struct.pack('>h',len(data))+b\"\\x00\\x00\")\n self.ser.write(data)\n\n #TODO There's a bug in hydrafw, where the return value is not send directly after the command\n # For the moment, we just send a IDENTIFY command and discard unused bytes\n self.ser.write(b'\\x01')\n if self.ser.read(1) != b'\\x01':\n print(\"Error sending data\")\n quit()\n self.ser.read(4)\n return\n\n def __get_id(self):\n self.Name = ''\n self.ID = 0\n self.PageSize = 0\n self.ChipSizeMB = 0\n self.EraseSize = 0\n self.Options = 0\n self.AddrCycles = 0\n\n self.__send_cmd(flashdevice_defs.NAND_CMD_READID)\n self.__send_address(0, 1)\n flash_identifiers = self.__read_data(8)\n\n if not flash_identifiers:\n return False\n\n for device_description in flashdevice_defs.DEVICE_DESCRIPTIONS:\n if device_description[1] == flash_identifiers[0]:\n (self.Name, self.ID, self.PageSize, self.ChipSizeMB, self.EraseSize, self.Options, self.AddrCycles) = device_description\n self.Identified = True\n break\n\n if not self.Identified:\n return False\n\n #Check ONFI\n self.__send_cmd(flashdevice_defs.NAND_CMD_READID)\n self.__send_address(0x20, 1)\n onfitmp = self.__read_data(4)\n\n onfi = (onfitmp == [0x4F, 0x4E, 0x46, 0x49])\n\n if onfi:\n self.__send_cmd(flashdevice_defs.NAND_CMD_ONFI)\n self.__send_address(0, 1)\n self.__wait_ready()\n onfi_data = self.__read_data(0x100)\n onfi = onfi_data[0:4] == [0x4F, 0x4E, 0x46, 0x49]\n\n if flash_identifiers[0] == 0x98:\n self.Manufacturer = 'Toshiba'\n elif flash_identifiers[0] == 0xec:\n self.Manufacturer = 'Samsung'\n elif flash_identifiers[0] == 0x04:\n self.Manufacturer = 'Fujitsu'\n elif flash_identifiers[0] == 0x8f:\n self.Manufacturer = 'National Semiconductors'\n elif flash_identifiers[0] == 0x07:\n self.Manufacturer = 'Renesas'\n elif flash_identifiers[0] == 0x20:\n self.Manufacturer = 'ST Micro'\n elif flash_identifiers[0] == 0xad:\n self.Manufacturer = 'Hynix'\n elif flash_identifiers[0] == 0x2c:\n self.Manufacturer = 'Micron'\n elif flash_identifiers[0] == 0x01:\n self.Manufacturer = 'AMD'\n elif flash_identifiers[0] == 0xc2:\n self.Manufacturer = 'Macronix'\n else:\n self.Manufacturer = 'Unknown'\n\n idstr = ''\n for idbyte in flash_identifiers:\n idstr += \"%X\" % idbyte\n if idstr[0:4] == idstr[-4:]:\n idstr = idstr[:-4]\n if idstr[0:2] == idstr[-2:]:\n idstr = idstr[:-2]\n self.IDString = idstr\n self.IDLength = int(len(idstr) / 2)\n self.BitsPerCell = self.get_bits_per_cell(flash_identifiers[2])\n if self.PageSize == 0:\n extid = flash_identifiers[3]\n if ((self.IDLength == 6) and (self.Manufacturer == \"Samsung\") and (self.BitsPerCell > 1)):\n self.Pagesize = 2048 << (extid & 0x03)\n extid >>= 2\n if (((extid >> 2) & 0x04) | (extid & 0x03)) == 1:\n self.OOBSize = 128\n if (((extid >> 2) & 0x04) | (extid & 0x03)) == 2:\n self.OOBSize = 218\n if (((extid >> 2) & 0x04) | (extid & 0x03)) == 3:\n self.OOBSize = 400\n if (((extid >> 2) & 0x04) | (extid & 0x03)) == 4:\n self.OOBSize = 436\n if (((extid >> 2) & 0x04) | (extid & 0x03)) == 5:\n self.OOBSize = 512\n if (((extid >> 2) & 0x04) | (extid & 0x03)) == 6:\n self.OOBSize = 640\n else:\n self.OOBSize = 1024\n extid >>= 2\n self.EraseSize = (128 * 1024) << (((extid >> 1) & 0x04) | (extid & 0x03))\n elif ((self.IDLength == 6) and (self.Manufacturer == 'Hynix') and (self.BitsPerCell > 1)):\n self.PageSize = 2048 << (extid & 0x03)\n extid >>= 2\n if (((extid >> 2) & 0x04) | (extid & 0x03)) == 0:\n self.OOBSize = 128\n elif (((extid >> 2) & 0x04) | (extid & 0x03)) == 1:\n self.OOBSize = 224\n elif (((extid >> 2) & 0x04) | (extid & 0x03)) == 2:\n self.OOBSize = 448\n elif (((extid >> 2) & 0x04) | (extid & 0x03)) == 3:\n self.OOBSize = 64\n elif (((extid >> 2) & 0x04) | (extid & 0x03)) == 4:\n self.OOBSize = 32\n elif (((extid >> 2) & 0x04) | (extid & 0x03)) == 5:\n self.OOBSize = 16\n else:\n self.OOBSize = 640\n tmp = ((extid >> 1) & 0x04) | (extid & 0x03)\n if tmp < 0x03:\n self.EraseSize = (128 * 1024) << tmp\n elif tmp == 0x03:\n self.EraseSize = 768 * 1024\n else: self.EraseSize = (64 * 1024) << tmp\n else:\n self.PageSize = 1024 << (extid & 0x03)\n extid >>= 2\n self.OOBSize = (8 << (extid & 0x01)) * (self.PageSize >> 9)\n extid >>= 2\n self.EraseSize = (64 * 1024) << (extid & 0x03)\n if ((self.IDLength >= 6) and (self.Manufacturer == \"Toshiba\") and (self.BitsPerCell > 1) and ((flash_identifiers[5] & 0x7) == 0x6) and not flash_identifiers[4] & 0x80):\n self.OOBSize = 32 * self.PageSize >> 9\n else:\n self.OOBSize = int(self.PageSize / 32)\n\n if self.PageSize > 0:\n self.PageCount = int(self.ChipSizeMB*1024*1024 / self.PageSize)\n self.RawPageSize = self.PageSize + self.OOBSize\n self.BlockSize = self.EraseSize\n self.BlockCount = int((self.ChipSizeMB*1024*1024) / self.BlockSize)\n\n if self.BlockCount <= 0:\n self.PagePerBlock = 0\n self.RawBlockSize = 0\n return False\n\n self.PagePerBlock = int(self.PageCount / self.BlockCount)\n self.RawBlockSize = self.PagePerBlock*(self.PageSize + self.OOBSize)\n\n return True\n\n def is_initialized(self):\n return self.Identified\n\n def set_use_ansi(self, use_ansi):\n self.UseAnsi = use_ansi\n\n def get_bits_per_cell(self, cellinfo):\n bits = cellinfo & flashdevice_defs.NAND_CI_CELLTYPE_MSK\n bits >>= flashdevice_defs.NAND_CI_CELLTYPE_SHIFT\n return bits+1\n\n def dump_info(self):\n print('Full ID:\\t', self.IDString)\n print('ID Length:\\t', self.IDLength)\n print('Name:\\t\\t', self.Name)\n print('ID:\\t\\t0x%x' % self.ID)\n print('Page size:\\t 0x{0:x}({0:d})'.format(self.PageSize))\n print('OOB size:\\t0x{0:x} ({0:d})'.format(self.OOBSize))\n print('Page count:\\t0x%x' % self.PageCount)\n print('Size:\\t\\t0x%x' % self.ChipSizeMB)\n print('Erase size:\\t0x%x' % self.EraseSize)\n print('Block count:\\t', self.BlockCount)\n print('Options:\\t', self.Options)\n print('Address cycle:\\t', self.AddrCycles)\n print('Bits per Cell:\\t', self.BitsPerCell)\n print('Manufacturer:\\t', self.Manufacturer)\n print('')\n\n def check_bad_blocks(self):\n bad_blocks = {}\n# end_page = self.PageCount\n\n if self.PageCount%self.PagePerBlock > 0.0:\n self.BlockCount += 1\n\n curblock = 1\n for block in range(0, self.BlockCount):\n page += self.PagePerBlock\n curblock = curblock + 1\n if self.UseAnsi:\n sys.stdout.write('Checking bad blocks %d Block: %d/%d\\n\\033[A' % (curblock / self.BlockCount*100.0, curblock, self.BlockCount))\n else:\n sys.stdout.write('Checking bad blocks %d Block: %d/%d\\n' % (curblock / self.BlockCount*100.0, curblock, self.BlockCount))\n for pageoff in range(0, 2, 1):\n oob = self.read_oob(page+pageoff)\n\n if oob[5] != b'\\xff':\n print('Bad block found:', block)\n bad_blocks[page] = 1\n break\n print('Checked %d blocks and found %d bad blocks' % (block+1, len(bad_blocks)))\n return bad_blocks\n\n def read_oob(self, pageno):\n bytes_to_send = b''\n if self.Options & flashdevice_defs.LP_OPTIONS:\n self.__send_cmd(flashdevice_defs.NAND_CMD_READ0)\n self.__send_address((pageno<<16), self.AddrCycles)\n self.__send_cmd(flashdevice_defs.NAND_CMD_READSTART)\n self.__wait_ready()\n bytes_to_send += self.__read_data(self.OOBSize)\n else:\n self.__send_cmd(flashdevice_defs.NAND_CMD_READ_OOB)\n self.__wait_ready()\n self.__send_address(pageno<<8, self.AddrCycles)\n self.__wait_ready()\n bytes_to_send += self.__read_data(self.OOBSize)\n\n return bytes_to_send\n\n def read_page(self, pageno, remove_oob = False):\n bytes_to_read = bytearray()\n\n if self.use_sd:\n self.__enable_sd()\n if self.Options & flashdevice_defs.LP_OPTIONS:\n self.__send_cmd(flashdevice_defs.NAND_CMD_READ0)\n self.__send_address(pageno<<16, self.AddrCycles)\n self.__send_cmd(flashdevice_defs.NAND_CMD_READSTART)\n if self.PageSize > 0x1000:\n length = self.PageSize + self.OOBSize\n while length > 0:\n read_len = 0x1000\n if length < 0x1000:\n read_len = length\n bytes_to_read += self.__read_data(read_len)\n length -= 0x1000\n else:\n bytes_to_read = self.__read_data(self.PageSize+self.OOBSize)\n\n #d: Implement remove_oob\n else:\n self.__send_cmd(flashdevice_defs.NAND_CMD_READ0)\n self.__wait_ready()\n self.__send_address(pageno<<8, self.AddrCycles)\n self.__wait_ready()\n bytes_to_read += self.__read_data(self.PageSize/2)\n\n self.__send_cmd(flashdevice_defs.NAND_CMD_READ1)\n self.__wait_ready()\n self.__send_address(pageno<<8, self.AddrCycles)\n self.__wait_ready()\n bytes_to_read += self.__read_data(self.PageSize/2)\n\n if not remove_oob:\n self.__send_cmd(flashdevice_defs.NAND_CMD_READ_OOB)\n self.__wait_ready()\n self.__send_address(pageno<<8, self.AddrCycles)\n self.__wait_ready()\n bytes_to_read += self.__read_data(self.OOBSize)\n\n return bytes_to_read\n\n def read_seq(self, pageno, remove_oob = False, raw_mode = False):\n page = []\n self.__send_cmd(flashdevice_defs.NAND_CMD_READ0)\n self.__wait_ready()\n self.__send_address(pageno<<8, self.AddrCycles)\n self.__wait_ready()\n\n bad_block = False\n\n for i in range(0, self.PagePerBlock, 1):\n page_data = self.__read_data(self.RawPageSize)\n\n if i in (0, 1):\n if page_data[self.PageSize + 5] != 0xff:\n bad_block = True\n\n if remove_oob:\n page += page_data[0:self.PageSize]\n else:\n page += page_data\n\n self.__wait_ready()\n\n #if self.ftdi is None or not self.ftdi.is_connected:\n # return ''\n\n #self.ftdi.write_data(Array('B', [ftdi.Ftdi.SET_BITS_HIGH, 0x1, 0x1]))\n #self.ftdi.write_data(Array('B', [ftdi.Ftdi.SET_BITS_HIGH, 0x0, 0x1]))\n\n data = ''\n\n if bad_block and not raw_mode:\n print('\\nSkipping bad block at %d' % (pageno / self.PagePerBlock))\n else:\n for ch in page:\n data += chr(ch)\n\n return data\n\n def erase_block_by_page(self, pageno):\n self.WriteProtect = False\n self.__send_cmd(flashdevice_defs.NAND_CMD_ERASE1)\n self.__send_address(pageno, self.AddrCycles)\n self.__send_cmd(flashdevice_defs.NAND_CMD_ERASE2)\n self.__wait_ready()\n err = self.__get_status()\n self.WriteProtect = True\n\n return err\n\n def write_page(self, pageno, data):\n err = 0\n self.WriteProtect = False\n\n if self.Options & flashdevice_defs.LP_OPTIONS:\n self.__send_cmd(flashdevice_defs.NAND_CMD_SEQIN)\n self.__wait_ready()\n self.__send_address(pageno<<16, self.AddrCycles)\n self.__wait_ready()\n self.__write_data(data)\n self.__send_cmd(flashdevice_defs.NAND_CMD_PAGEPROG)\n self.__wait_ready()\n else:\n while 1:\n self.__send_cmd(flashdevice_defs.NAND_CMD_READ0)\n self.__send_cmd(flashdevice_defs.NAND_CMD_SEQIN)\n self.__wait_ready()\n self.__send_address(pageno<<8, self.AddrCycles)\n self.__wait_ready()\n self.__write_data(data[0:256])\n self.__send_cmd(flashdevice_defs.NAND_CMD_PAGEPROG)\n err = self.__get_status()\n if err & flashdevice_defs.NAND_STATUS_FAIL:\n print('Failed to write 1st half of ', pageno, err)\n continue\n break\n\n while 1:\n self.__send_cmd(flashdevice_defs.NAND_CMD_READ1)\n self.__send_cmd(flashdevice_defs.NAND_CMD_SEQIN)\n self.__wait_ready()\n self.__send_address(pageno<<8, self.AddrCycles)\n self.__wait_ready()\n self.__write_data(data[self.PageSize/2:self.PageSize])\n self.__send_cmd(flashdevice_defs.NAND_CMD_PAGEPROG)\n err = self.__get_status()\n if err & flashdevice_defs.NAND_STATUS_FAIL:\n print('Failed to write 2nd half of ', pageno, err)\n continue\n break\n\n while 1:\n self.__send_cmd(flashdevice_defs.NAND_CMD_READ_OOB)\n self.__send_cmd(flashdevice_defs.NAND_CMD_SEQIN)\n self.__wait_ready()\n self.__send_address(pageno<<8, self.AddrCycles)\n self.__wait_ready()\n self.__write_data(data[self.PageSize:self.RawPageSize])\n self.__send_cmd(flashdevice_defs.NAND_CMD_PAGEPROG)\n err = self.__get_status()\n if err & flashdevice_defs.NAND_STATUS_FAIL:\n print('Failed to write OOB of ', pageno, err)\n continue\n break\n\n self.WriteProtect = True\n return err\n\n# def write_block(self, block_data):\n# nand_tool.erase_block_by_page(0) #need to fix\n# page = 0\n# for i in range(0, len(data), self.RawPageSize):\n# nand_tool.write_page(pageno, data[i:i+self.RawPageSize])\n# page += 1\n\n def write_pages(self, filename, offset = 0, start_page = -1, end_page = -1, add_oob = False, add_jffs2_eraser_marker = False, raw_mode = False):\n fd = open(filename, 'rb')\n fd.seek(offset)\n data = fd.read()\n\n if start_page == -1:\n start_page = 0\n\n if end_page == -1:\n end_page = self.PageCount-1\n\n end_block = end_page/self.PagePerBlock\n\n if end_page % self.PagePerBlock > 0:\n end_block += 1\n\n start = time.time()\n ecc_calculator = ecc.Calculator()\n\n page = start_page\n block = page / self.PagePerBlock\n current_data_offset = 0\n length = 0\n\n while page <= end_page and current_data_offset < len(data) and block < self.BlockCount:\n oob_postfix = b'\\xff' * 13\n if page%self.PagePerBlock == 0:\n\n if not raw_mode:\n bad_block_found = False\n for pageoff in range(0, 2, 1):\n oob = self.read_oob(page+pageoff)\n\n if oob[5] != 0xff:\n bad_block_found = True\n break\n\n if bad_block_found:\n print('\\nSkipping bad block at ', block)\n page += self.PagePerBlock\n block += 1\n continue\n\n if add_jffs2_eraser_marker:\n oob_postfix = b\"\\xFF\\xFF\\xFF\\xFF\\xFF\\x85\\x19\\x03\\x20\\x08\\x00\\x00\\x00\"\n\n self.erase_block_by_page(page)\n\n if add_oob:\n orig_page_data = data[current_data_offset:current_data_offset + self.PageSize]\n current_data_offset += self.PageSize\n length += len(orig_page_data)\n orig_page_data += (self.PageSize - len(orig_page_data)) * b'\\x00'\n (ecc0, ecc1, ecc2) = ecc_calculator.calc(orig_page_data)\n\n oob = struct.pack('BBB', ecc0, ecc1, ecc2) + oob_postfix\n page_data = orig_page_data+oob\n else:\n page_data = data[current_data_offset:current_data_offset + self.RawPageSize]\n current_data_offset += self.RawPageSize\n length += len(page_data)\n\n if len(page_data) != self.RawPageSize:\n print('Not enough source data')\n break\n\n current = time.time()\n\n if end_page == start_page:\n progress = 100\n else:\n progress = (page-start_page) * 100 / (end_page-start_page)\n\n lapsed_time = current-start\n\n if lapsed_time > 0:\n if self.UseAnsi:\n sys.stdout.write('Writing %d%% Page: %d/%d Block: %d/%d Speed: %d bytes/s\\n\\033[A' % (progress, page, end_page, block, end_block, length/lapsed_time))\n else:\n sys.stdout.write('Writing %d%% Page: %d/%d Block: %d/%d Speed: %d bytes/s\\n' % (progress, page, end_page, block, end_block, length/lapsed_time))\n self.write_page(page, page_data)\n\n if page%self.PagePerBlock == 0:\n block = page / self.PagePerBlock\n page += 1\n\n fd.close()\n\n print('\\nWritten %x bytes / %x byte' % (length, len(data)))\n\n def erase(self):\n block = 0\n while block < self.BlockCount:\n if self.UseAnsi:\n sys.stdout.write('Erasing block %d%% Block: %d/%d\\n\\033[A' % (block / self.BlockCount*100.0, block, self.BlockCount))\n else:\n sys.stdout.write('Erasing block %d%% Block: %d/%d\\n' % (block / self.BlockCount*100.0, block, self.BlockCount))\n self.erase_block_by_page(block * self.PagePerBlock)\n block += 1\n\n def erase_block(self, start_block, end_block):\n print('Erasing Block: 0x%x ~ 0x%x' % (start_block, end_block))\n for block in range(start_block, end_block+1, 1):\n print(\"Erasing block\", block)\n self.erase_block_by_page(block * self.PagePerBlock)\n", "sub_path": "dumpflash/flashdevice.py", "file_name": "flashdevice.py", "file_ext": "py", "file_size_in_byte": 22834, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "serial.Serial", "line_number": 30, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 119, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 133, "usage_type": "call"}, {"api_name": "flashdevice_defs.NAND_CMD_READID", "line_number": 154, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.DEVICE_DESCRIPTIONS", "line_number": 161, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.NAND_CMD_READID", "line_number": 171, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.NAND_CMD_ONFI", "line_number": 178, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.NAND_CI_CELLTYPE_MSK", "line_number": 295, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.NAND_CI_CELLTYPE_SHIFT", "line_number": 296, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 328, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 328, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 330, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 330, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.LP_OPTIONS", "line_number": 343, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.NAND_CMD_READ0", "line_number": 344, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.NAND_CMD_READSTART", "line_number": 346, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.NAND_CMD_READ_OOB", "line_number": 350, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.LP_OPTIONS", "line_number": 363, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.NAND_CMD_READ0", "line_number": 364, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.NAND_CMD_READSTART", "line_number": 366, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.NAND_CMD_READ0", "line_number": 380, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.NAND_CMD_READ1", "line_number": 386, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.NAND_CMD_READ_OOB", "line_number": 393, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.NAND_CMD_READ0", "line_number": 403, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.NAND_CMD_ERASE1", "line_number": 442, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.NAND_CMD_ERASE2", "line_number": 444, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.LP_OPTIONS", "line_number": 455, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.NAND_CMD_SEQIN", "line_number": 456, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.NAND_CMD_PAGEPROG", "line_number": 461, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.NAND_CMD_READ0", "line_number": 465, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.NAND_CMD_SEQIN", "line_number": 466, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.NAND_CMD_PAGEPROG", "line_number": 471, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.NAND_STATUS_FAIL", "line_number": 473, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.NAND_CMD_READ1", "line_number": 479, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.NAND_CMD_SEQIN", "line_number": 480, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.NAND_CMD_PAGEPROG", "line_number": 485, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.NAND_STATUS_FAIL", "line_number": 487, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.NAND_CMD_READ_OOB", "line_number": 493, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.NAND_CMD_SEQIN", "line_number": 494, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.NAND_CMD_PAGEPROG", "line_number": 499, "usage_type": "attribute"}, {"api_name": "flashdevice_defs.NAND_STATUS_FAIL", "line_number": 501, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 532, "usage_type": "call"}, {"api_name": "ecc.Calculator", "line_number": 533, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 571, "usage_type": "call"}, {"api_name": "time.time", "line_number": 582, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 593, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 593, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 595, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 595, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 610, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 610, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 612, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 612, "usage_type": "attribute"}]} +{"seq_id": "35478266", "text": "try:\n from src.models import kmodel\nexcept:\n import kmodel\n\ntry:\n import src.preprocessing as pp\nexcept:\n import preprocessing as pp\n\nimport pickle\nimport nltk\nimport numpy as np\nimport sklearn\nimport os\n\nSEED = 2\n\nCURRENT_DIR = os.getcwd()\n\nDATA_DIR = CURRENT_DIR.split('cs175-project')[0] + \"cs175-project/data\"\n\nstopwords = set(nltk.corpus.stopwords.words('english'))\nvocab = {w: i for i, w in kmodel.word_map.items() if i <= pp.NUM_VOCAB}\n\n\ndef load():\n \"\"\"\n Loads the Keras LSTM RNN and loads the news article data set and returns them.\n :return: LSTM, Article Data Set\n \"\"\"\n return kmodel.define_model(), pp.load_data()\n\n\ndef gen_fake_article(model, tokenizer, article, temperature=1.):\n \"\"\"\n Takes a trained model, a tokenizer that has a sequence of word tokens and a template article then synthesizes a fake\n article based on seed words from the template article. The sentence length and article length will be identical but\n words will be different.\n :param model: Keras LSTM RNN\n :param tokenizer: Keras Text Tokenizer\n :param article: Template article for seeding\n :param temperature: Increase sampling variance\n :return: returns a fake article\n \"\"\"\n parsed_sentences = nltk.sent_tokenize(article)\n\n fake_article = \"\"\n for sent in parsed_sentences:\n words = sent.split()\n seed = np.array(tokenizer.texts_to_sequences([words[0]]))\n\n # print(seed)\n # print(words)\n if seed.shape != (1, 1):\n continue\n start_word = words[0]\n sentence = start_word[0].upper() + start_word[1:]\n for _ in range(len(words) - 1):\n word_prob = model.predict(seed)[0]\n\n word_prob = np.log(word_prob) / temperature\n word_prob = np.exp(word_prob) / np.sum(np.exp(word_prob))\n seed = np.random.choice(range(word_prob.shape[0]), p=word_prob)\n seed = np.array([seed])\n\n sentence += ' ' + kmodel.word_map[seed[0]]\n model.predict(seed)\n fake_article += sentence + \". \"\n return fake_article\n\n\ndef get_fake_article_set(model, data, tokenizer, lim=10, overwrite=False, ):\n \"\"\"\n This method will generate a set of fake articles based on the model and data set provided.\n :param model: Keras LSTM RNN\n :param data: Pandas data set of articles\n :param tokenizer: Keras Text Tokenizer\n :param overwrite: Overwrite the already saved fake articles\n :param lim: Limit the number of articles to generate\n :return:\n \"\"\"\n print('Generating %d articles' % lim)\n fake_articles = []\n real_articles = data['content'][:lim].tolist()\n generated_data_dir = DATA_DIR + \"/generated_articles\"\n generated_articles = set(os.listdir(generated_data_dir))\n\n for i, art in enumerate(real_articles):\n if not overwrite and 'a{}.txt'.format(i) in generated_articles:\n print('Loading article #%d' % i)\n f = open(generated_data_dir + '/a{}.txt'.format(i), 'r')\n fake_articles.append(f.readline())\n f.close()\n else:\n print('Generating article #%d' % i)\n fake_article = gen_fake_article(model, tokenizer, art)\n fake_articles.append(fake_article)\n f = open(generated_data_dir + '/a{}.txt'.format(i), 'w')\n f.write(fake_article)\n f.close()\n\n # check for bad simulations\n print(\"Creating bag of words for articles\")\n fake_articles = np.array([create_bag_of_words(a) for a in fake_articles])\n real_articles = np.array([create_bag_of_words(a) for a in real_articles])\n\n # Remove articles that could be generate since the seed was not in the vocab\n good_sims = np.any(fake_articles, axis=1)\n print('Found', np.count_nonzero(good_sims), 'Good articles')\n\n fake_articles = fake_articles[good_sims]\n real_articles = real_articles[good_sims]\n\n fake_train, fake_test, ftrain_target, ftest_target = get_train_data(fake_articles, np.ones(len(fake_articles)))\n real_train, real_test, rtrain_target, rtest_target = get_train_data(real_articles, np.zeros(len(real_articles)))\n\n # X = real_articles + fake_articles\n # Y = np.zeros(2 * lim)\n # Y[lim:] = 1\n\n return np.concatenate([fake_train, real_train]), np.concatenate([fake_test, real_test]), np.concatenate(\n [ftrain_target, rtrain_target]), \\\n np.concatenate([ftest_target, rtest_target])\n\n\ndef get_train_data(X, Y):\n \"\"\"\n Given X and Y data, shuffle the data and create a X,Y Training set and X,Y Testing set.\n :param X: Data points\n :param Y: Labels\n :return: Xtrain, Ytrain, Xtest, Ytest\n \"\"\"\n print(\"Shuffling Data\")\n x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(X, Y, random_state=SEED)\n return x_train, x_test, y_train, y_test\n\n\ndef create_bag_of_words(article):\n \"\"\"\n Create a bag of words for the articles\n :param article:\n :return:\n \"\"\"\n non_stopwords = [i for w, i in vocab.items() if w not in stopwords]\n\n bag_of_words = np.zeros(pp.NUM_VOCAB + 1)\n\n tokens = kmodel.tokenizer.texts_to_sequences([article])[0]\n\n tokens = list(set(tokens))\n\n bag_of_words[tokens] = 1\n bag_of_words = bag_of_words[non_stopwords]\n bag_of_words = bag_of_words[1:]\n\n return bag_of_words\n\n\nif __name__ == \"__main__\":\n model, data = load()\n\n x_train, x_test, y_train, y_test = get_fake_article_set(model, data, kmodel.tokenizer, 1000)\n\n pickle.dump(x_test[:200], open(DATA_DIR + \"/x_test_sample.txt\", \"wb\"))\n pickle.dump(y_test[:200], open(DATA_DIR + \"/y_test_sample.txt\", \"wb\"))\n print(x_train.shape)\n print('Starting Training')\n lr = sklearn.linear_model.LogisticRegression(n_jobs=4)\n lr.fit(x_train, y_train)\n pickle.dump(lr, open(DATA_DIR + \"/logistic_regression.model\", \"wb\"))\n score = lr.score(x_test, y_test)\n print('Score', score)\n", "sub_path": "src/models/classifier.py", "file_name": "classifier.py", "file_ext": "py", "file_size_in_byte": 5881, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.getcwd", "line_number": 19, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 23, "usage_type": "call"}, {"api_name": "nltk.corpus", "line_number": 23, "usage_type": "attribute"}, {"api_name": "kmodel.word_map.items", "line_number": 24, "usage_type": "call"}, {"api_name": "kmodel.word_map", "line_number": 24, "usage_type": "attribute"}, {"api_name": "preprocessing.NUM_VOCAB", "line_number": 24, "usage_type": "attribute"}, {"api_name": "kmodel.define_model", "line_number": 32, "usage_type": "call"}, {"api_name": "preprocessing.load_data", "line_number": 32, "usage_type": "call"}, {"api_name": "nltk.sent_tokenize", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 64, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 65, "usage_type": "call"}, {"api_name": "kmodel.word_map", "line_number": 67, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.count_nonzero", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 124, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 135, "usage_type": "call"}, {"api_name": "sklearn.model_selection", "line_number": 135, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 147, "usage_type": "call"}, {"api_name": "preprocessing.NUM_VOCAB", "line_number": 147, "usage_type": "attribute"}, {"api_name": "kmodel.tokenizer.texts_to_sequences", "line_number": 149, "usage_type": "call"}, {"api_name": "kmodel.tokenizer", "line_number": 149, "usage_type": "attribute"}, {"api_name": "kmodel.tokenizer", "line_number": 163, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 165, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 166, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 169, "usage_type": "call"}, {"api_name": "sklearn.linear_model", "line_number": 169, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 171, "usage_type": "call"}]} +{"seq_id": "422146702", "text": "from django.db import models\n\nfrom wagtail.images import get_image_model_string\nfrom wagtail.images.edit_handlers import ImageChooserPanel\n\n\nclass TeaserImageMixin(models.Model):\n \"\"\"Mixin to add teaser_image attribute to a Page.\"\"\"\n\n teaser_image = models.ForeignKey(\n get_image_model_string(),\n null=True,\n blank=True,\n on_delete=models.SET_NULL,\n related_name=\"+\",\n )\n\n class Meta:\n abstract = True\n\n promote_panels = [ImageChooserPanel(\"teaser_image\")]\n", "sub_path": "etna/teasers/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 514, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "django.db.models.Model", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}, {"api_name": "wagtail.images.get_image_model_string", "line_number": 11, "usage_type": "call"}, {"api_name": "django.db.models.SET_NULL", "line_number": 14, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 14, "usage_type": "name"}, {"api_name": "wagtail.images.edit_handlers.ImageChooserPanel", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "138475558", "text": "import numpy as np\nimport matplotlib as mpl\nimport seaborn as sns\n\nfrom matplotlib import pyplot as plt\nfrom matplotlib import cm\nfrom skimage import io as skio\n\nimport keras\nfrom keras import backend as K\nfrom keras import metrics\nfrom keras.models import Model\nfrom keras.datasets import mnist\nfrom keras.layers import Input\nfrom keras.layers import Dense\nfrom keras.layers import Lambda\n\n\ndef main():\n sns.set_context('talk', font_scale=1.5)\n sns.set_style('white') # for image\n mpl.rc('image', cmap='inferno', interpolation='nearest') # for image\n pass\n\nclass VAE():\n def __init__(self):\n\n self.input_dim = 784\n self.latent_dim = 2\n nch = 256\n\n # encoder\n self.x_input = Input(shape=(self.input_dim,))\n x = Dense(nch, activation='relu')(self.x_input)\n x = Dense(nch, activation='relu')(x)\n\n self.z_mean = Dense(self.latent_dim)(x)\n self.z_log_var = Dense(self.latent_dim)(x)\n self.z = Lambda(self.sampling, output_shape=(self.latent_dim,))([self.z_mean, self.z_log_var])\n\n # decoder\n self.dec1 = Dense(nch, activation='relu')\n self.dec2 = Dense(nch, activation='relu')\n self.dec_out = Dense(self.input_dim, activation='sigmoid')\n\n x = self.dec1(self.z)\n x = self.dec2(x)\n self.x_out = self.dec_out(x)\n\n def sampling(self, args):\n z_mean, z_log_var = args\n nd = K.shape(z_mean)[0]\n nc = self.latent_dim\n eps = K.random_normal(shape=(nd, nc), mean=0., stddev=1.0)\n return z_mean + K.exp(z_log_var / 2) * eps\n\n def vae(self):\n return Model(self.x_input, self.x_out)\n\n def encoder(self):\n return Model(self.x_input, self.z_mean)\n\n def decoder(self):\n z = Input(shape=(self.latent_dim,))\n x = self.dec1(z)\n x = self.dec2(x)\n x_out = self.dec_out(x)\n return Model(z, x_out)\n\n def loss(self):\n bce = metrics.binary_crossentropy(self.x_input, self.x_out)\n xent_loss = self.input_dim * bce\n kl = 1 + self.z_log_var - K.square(self.z_mean) - K.exp(self.z_log_var)\n kl_loss = - 0.5 * K.sum(kl, axis=-1)\n vae_loss = K.mean(xent_loss + kl_loss)\n return vae_loss\n\n\nif __name__ == '__main__':\n main()\n\n seed = 0 # for network init\n\n batch_size = 32\n epochs = 2000\n nd = 200 # number of images for training\n\n # train the VAE on MNIST digits\n (x_train, y_train), (x_test, y_test) = mnist.load_data()\n x_train = x_train.astype('float32') / 255.\n x_train = x_train[:nd]\n n, h, w = x_train.shape\n x_train = x_train.reshape(n, -1)\n\n # make vae model and train\n np.random.seed(seed)\n vae = VAE()\n model = vae.vae()\n model.summary()\n model.add_loss(vae.loss())\n np.random.seed(seed)\n model.compile(optimizer='adam', loss=None)\n model.fit(x_train, epochs=epochs, batch_size=batch_size)\n\n # decode images from 2-dim latent z\n ndiv = 15 # number of images for axis\n dec = vae.decoder()\n gx = np.linspace(-3.0, 3.0, ndiv)\n gxx, gyy = np.meshgrid(gx, gx[::-1])\n z = np.append(gxx.reshape(-1,1), gyy.reshape(-1,1), axis=1)\n x = dec.predict(z)\n dst = x.reshape(ndiv,ndiv,28,28)\n dst = dst.transpose(0,2,1,3)\n dst = dst.reshape(ndiv*28, ndiv*28)\n\n # plot images\n plt.imshow(dst)\n plt.tight_layout();plt.show()\n\n # output images\n out = (cm.inferno(dst)[:,:,:3]*255).astype(np.uint8)\n #skio.imsave('vae_out_%03d.png' % seed, out)\n", "sub_path": "vae.py", "file_name": "vae.py", "file_ext": "py", "file_size_in_byte": 3484, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "seaborn.set_context", "line_number": 20, "usage_type": "call"}, {"api_name": "seaborn.set_style", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.rc", "line_number": 22, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 33, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 34, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 35, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 37, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 38, "usage_type": "call"}, {"api_name": "keras.layers.Lambda", "line_number": 39, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 42, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 43, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 44, "usage_type": "call"}, {"api_name": "keras.backend.shape", "line_number": 52, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 52, "usage_type": "name"}, {"api_name": "keras.backend.random_normal", "line_number": 54, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 54, "usage_type": "name"}, {"api_name": "keras.backend.exp", "line_number": 55, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 55, "usage_type": "name"}, {"api_name": "keras.models.Model", "line_number": 58, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 61, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 64, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 68, "usage_type": "call"}, {"api_name": "keras.metrics.binary_crossentropy", "line_number": 71, "usage_type": "call"}, {"api_name": "keras.metrics", "line_number": 71, "usage_type": "name"}, {"api_name": "keras.backend.square", "line_number": 73, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 73, "usage_type": "name"}, {"api_name": "keras.backend.exp", "line_number": 73, "usage_type": "call"}, {"api_name": "keras.backend.sum", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 74, "usage_type": "name"}, {"api_name": "keras.backend.mean", "line_number": 75, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 75, "usage_type": "name"}, {"api_name": "keras.datasets.mnist.load_data", "line_number": 89, "usage_type": "call"}, {"api_name": "keras.datasets.mnist", "line_number": 89, "usage_type": "name"}, {"api_name": "numpy.random.seed", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 96, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 101, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.cm.inferno", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 121, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 121, "usage_type": "attribute"}]} +{"seq_id": "623358353", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('focus', '0018_auto_20160703_1144'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='entry',\n name='date_created',\n field=models.DateTimeField(auto_now_add=True, null=True),\n ),\n migrations.AddField(\n model_name='entry',\n name='date_modified',\n field=models.DateTimeField(auto_now=True, null=True),\n ),\n migrations.AddField(\n model_name='task',\n name='date_created',\n field=models.DateTimeField(auto_now_add=True, null=True),\n ),\n migrations.AddField(\n model_name='task',\n name='date_modified',\n field=models.DateTimeField(auto_now=True, null=True),\n ),\n migrations.AlterField(\n model_name='task',\n name='highlight_chart',\n field=models.CharField(default=b'_display_full_history', max_length=25, choices=[(b'_display_full_history', b'Full History'), (b'_display_resampled_avg_m', b'Average Resampled by Month'), (b'_display_resampled_sum_w', b'Total Resampled by Week'), (b'_display_dual_moving_avg', b'Dual Moving Average'), (b'_display_moving_env', b'Moving Envelope'), (b'_display_this_month', b'This Month'), (b'_display_year_weekly_sum', b'Year Total Resampled by Week')]),\n ),\n migrations.AlterField(\n model_name='task',\n name='resample_period',\n field=models.CharField(default=b'D', max_length=1, choices=[(b'd', b'days'), (b'W', b'weeks'), (b'M', b'months'), (b'Q', b'quarters'), (b'Y', b'years')]),\n ),\n ]\n", "sub_path": "focus/migrations/0019_auto_20160703_1311.py", "file_name": "0019_auto_20160703_1311.py", "file_ext": "py", "file_size_in_byte": 1791, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 34, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 37, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 39, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 39, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 42, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 42, "usage_type": "name"}]} +{"seq_id": "203344540", "text": "from ConfigParser import SafeConfigParser\nfrom qds_sdk.qubole import Qubole\nfrom lib.utils import *\nfrom qds_sdk.cluster import *\nimport pytest\nimport logging\nimport sys\nimport json\nfrom os import environ\nimport requests\n\n\ndef pytest_addoption(parser):\n \"\"\"Add option --suite if you want to run specific suites.\n\n E.g. --suite=storage or use all\n Add option --env to specify environment found in setup.cfg\n \"\"\"\n parser.addoption('--env', dest='environment', default='qa',\n help='Specify environment: \"qa\", \"staging\", \"multicluster\"')\n parser.addoption('--env_version', dest='env_version', default='v1.2',\n help='Api version')\n\n\ndef load_settings():\n \"\"\"Settings read from the setup.cfg file\n\n TODO: Module level/suite level setup.cfg\n \"\"\"\n sparse = SafeConfigParser()\n sparse.read('../../../tests/doc_tests/setup.cfg')\n return sparse\n\n\ndef pytest_configure(config):\n\n cfg = load_settings()\n Qubole.configure(\n api_token=cfg.get(config.option.environment, 'auth_token'),\n api_url=cfg.get(config.option.environment, 'api_url'),\n skip_ssl_cert_check=True\n )\n\n@pytest.fixture(scope=\"function\")\ndef config(request):\n \"\"\" Read all the configuration from setup.cfg and return a dict\n\n Provided to tests that mark the fixture for use\n \"\"\"\n # turn off logging from requests module\n requests_log = logging.getLogger(\"requests\")\n requests_log.setLevel(logging.ERROR)\n cfg = load_settings()\n settings =dict(cfg._sections[request.config.option.environment])\n settings['version'] = str(request.config.option.env_version)\n return settings\n\n@pytest.fixture(scope=\"function\")\ndef logger(request):\n LOG = logging.getLogger(request.function.__name__)\n LOG.setLevel(logging.DEBUG)\n stream = logging.StreamHandler(sys.stdout)\n stream.setLevel(logging.DEBUG)\n formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n stream.setFormatter(formatter)\n LOG.addHandler(stream)\n return LOG\n\n@pytest.fixture\ndef cluster_info(config):\n \"\"\" set basic cluster ClusterInfo\n \"\"\"\n clabel = random_sequence()+\"_doc_test\"\n cluster_settings = ClusterInfo(\n label=clabel,\n aws_access_key_id=environ.get('TES_ACCESS_KEY_ID'),\n aws_secret_access_key=environ.get('TES_SECRET_ACCESS_KEY')\n )\n cluster_settings.set_ec2_settings(\n aws_region='us-east-1'\n )\n\n master_instance_type = 'c3.large'\n slave_instance_type = 'c3.large'\n\n cluster_settings.set_hadoop_settings(\n master_instance_type=master_instance_type,\n slave_instance_type=slave_instance_type,\n initial_nodes=1,\n max_nodes=2,\n slave_request_type='spot',\n\n )\n cluster_settings.set_spot_instance_settings(\n timeout_for_request=10,\n maximum_bid_price_percentage=100)\n cluster_settings.set_stable_spot_instance_settings(\n timeout_for_request=10,\n maximum_bid_price_percentage=150,\n allow_fallback=False)\n\n return cluster_settings\n", "sub_path": "sphinx/rest-api/conftest.py", "file_name": "conftest.py", "file_ext": "py", "file_size_in_byte": 3076, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "ConfigParser.SafeConfigParser", "line_number": 30, "usage_type": "call"}, {"api_name": "qds_sdk.qubole.Qubole.configure", "line_number": 38, "usage_type": "call"}, {"api_name": "qds_sdk.qubole.Qubole", "line_number": 38, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 51, "usage_type": "call"}, {"api_name": "logging.ERROR", "line_number": 52, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 44, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 60, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 61, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 62, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 62, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 63, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 64, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 58, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 76, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 76, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 77, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 77, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 69, "usage_type": "attribute"}]} +{"seq_id": "556530763", "text": "from django.urls import path, include\nfrom . import views\nfrom django.contrib.auth import views as auth_views\n\n\n\nurlpatterns = [\n\n# Shared URL's\n path('', views.login_form, name='home'),\npath('login/', views.loginView, name='login'),\npath('regform/', views.register_form, name='regform'),\npath('register/', views.registerView, name='register'),\npath('admin12/', views.admin12, name='admin12'),\npath('client/', views.client, name='client'),\n ]", "sub_path": "demo1/demo/app1/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 445, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "309260461", "text": "#!/usr/bin/python3.7\n#\n# Nuls.ac_getAccountList\"\n\n\nimport json\nfrom nulsws_python.src.nulsws_python.make_top import get_top_section\nfrom nulsws_python.src.nulsws_python.user_settings.usersettings import *\nfrom nulsws_python.src.nulsws_python.constants.nulsws_api_labels import NulsWsApiLabel\nfrom nulsws_python.src.nulsws_python.constants.nulsws_other_labels import *\n\n\nclass AcGetAcctList(object):\n\n def __init__(self):\n self.chainid_val = 1\n self.n = NulsWsApiLabel()\n\n def req_ac_get_account_list(self, m_index): #\n stat_msg_top = get_top_section(3, m_index) # 3 is a Request\n\n bottom = {\n msg_data_label: {\n request_type_label: \"1\", # 1 = normal response only\n subscrip_evnt_ct_label: ZERO, # not implemented yet in Nulstar\n subscrip_period_label: ZERO, # not implemented yet in Nulstar\n subscriptn_range_label: ZERO, # not implemented yet in Nulstar\n response_max_size_label: ZERO,\n req_methods_label:\n {\n self.n.AC_GET_ACCOUNT_LIST: {CHAINID_LABEL: self.chainid_val}\n }\n }}\n stat_msg_top.update(bottom)\n return json.dumps(stat_msg_top)\n\n\nif __name__ == '__main__':\n ac = AcGetAcctList()\n ac.req_ac_get_account_list()\n\n\n\n\n# \"Nuls.ac_getAccountList\": {\n# \"MethodDescription\": \"\\u83b7\\u53d6\\u6240\\u6709\\u8d26\\u6237\\u96c6\\u5408,\\u5e76\\u653e\\u5165\\u7f13\\u5b58/query all account collections and put them in cache\",\n# \"MethodMinEvent\": \"0\",\n# \"MethodMinPeriod\": \"0\",\n# \"MethodName\": \"ac_getAccountList\",\n# \"MethodScope\": \"public\",\n# \"Parameters\": [\n# {\n# \"ParameterName\": \"chainId\",\n# \"ParameterType\": \"int\",\n# \"ParameterValidRange\": \"\",\n# \"ParameterValidRegExp\": \"\"\n# }\n# ]\n# },\n", "sub_path": "docs/examples/query/ac_get_acct_list.py", "file_name": "ac_get_acct_list.py", "file_ext": "py", "file_size_in_byte": 1915, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "nulsws_python.src.nulsws_python.constants.nulsws_api_labels.NulsWsApiLabel", "line_number": 17, "usage_type": "call"}, {"api_name": "nulsws_python.src.nulsws_python.make_top.get_top_section", "line_number": 20, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "572531917", "text": "# Project Clearwater - IMS in the Cloud\n# Copyright (C) 2015 Metaswitch Networks Ltd\n#\n# This program is free software: you can redistribute it and/or modify it\n# under the terms of the GNU General Public License as published by the\n# Free Software Foundation, either version 3 of the License, or (at your\n# option) any later version, along with the \"Special Exception\" for use of\n# the program along with SSL, set forth below. This program is distributed\n# in the hope that it will be useful, but WITHOUT ANY WARRANTY;\n# without even the implied warranty of MERCHANTABILITY or FITNESS FOR\n# A PARTICULAR PURPOSE. See the GNU General Public License for more\n# details. You should have received a copy of the GNU General Public\n# License along with this program. If not, see\n# .\n#\n# The author can be reached by email at clearwater@metaswitch.com or by\n# post at Metaswitch Networks Ltd, 100 Church St, Enfield EN2 6BQ, UK\n#\n# Special Exception\n# Metaswitch Networks Ltd grants you permission to copy, modify,\n# propagate, and distribute a work formed by combining OpenSSL with The\n# Software, or a work derivative of such a combination, even if such\n# copying, modification, propagation, or distribution would otherwise\n# violate the terms of the GPL. You must comply with the GPL in all\n# respects for all of the code used other than OpenSSL.\n# \"OpenSSL\" means OpenSSL toolkit software distributed by the OpenSSL\n# Project and licensed under the OpenSSL Licenses, or a work based on such\n# software and licensed under the OpenSSL Licenses.\n# \"OpenSSL Licenses\" means the OpenSSL License and Original SSLeay License\n# under which the OpenSSL Project distributes the OpenSSL toolkit software,\n# as those licenses appear in the file LICENSE-OPENSSL.\n\n\nimport logging\nimport socket\nimport time\nimport yaml\nfrom textwrap import dedent\nfrom metaswitch.clearwater.cluster_manager import constants\nfrom metaswitch.clearwater.etcd_shared.plugin_utils import run_command\n\n_log = logging.getLogger(\"cluster_manager.plugin_utils\")\n\nWARNING_HEADER = dedent(\"\"\"\\\n# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n#\n# WARNING - THIS FILE IS GENERATED BY ETCD AND SHOULD NOT BE EDITED DIRECTLY\n#\n# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\"\"\")\n\ndef write_memcached_cluster_settings(filename, cluster_view):\n \"\"\"Writes out the memcached cluster_settings file\"\"\"\n valid_servers_states = [constants.LEAVING_ACKNOWLEDGED_CHANGE,\n constants.LEAVING_CONFIG_CHANGED,\n constants.NORMAL_ACKNOWLEDGED_CHANGE,\n constants.NORMAL_CONFIG_CHANGED,\n constants.NORMAL]\n valid_new_servers_states = [constants.NORMAL,\n constants.NORMAL_ACKNOWLEDGED_CHANGE,\n constants.NORMAL_CONFIG_CHANGED,\n constants.JOINING_ACKNOWLEDGED_CHANGE,\n constants.JOINING_CONFIG_CHANGED]\n servers_ips = sorted([\"{}:11211\".format(k)\n for k, v in cluster_view.iteritems()\n if v in valid_servers_states])\n\n new_servers_ips = sorted([\"{}:11211\".format(k)\n for k, v in cluster_view.iteritems()\n if v in valid_new_servers_states])\n\n new_file_contents = WARNING_HEADER + \"\\n\"\n if new_servers_ips == servers_ips:\n new_file_contents += \"servers={}\\n\".format(\",\".join(servers_ips))\n else:\n new_file_contents += \"servers={}\\nnew_servers={}\\n\".format(\n \",\".join(servers_ips),\n \",\".join(new_servers_ips))\n\n _log.debug(\"Writing out cluster_settings file '{}'\".format(\n new_file_contents))\n with open(filename, \"w\") as f:\n f.write(new_file_contents)\n\n\n# Edits cassandra.yaml and restarts Cassandra in order to join a Cassandra\n# cluster. If there is an existing Cassandra cluster formed, we use the nodes in\n# that cluster as the seeds; otherwise, we use the all the joining nodes as the\n# seeds._\ndef join_cassandra_cluster(cluster_view,\n cassandra_yaml_file,\n cassandra_topology_file,\n ip,\n site_name):\n seeds_list = []\n\n for seed, state in cluster_view.items():\n if (state == constants.NORMAL_ACKNOWLEDGED_CHANGE or\n state == constants.NORMAL_CONFIG_CHANGED):\n seeds_list.append(seed)\n\n if len(seeds_list) == 0:\n for seed, state in cluster_view.items():\n if (state == constants.JOINING_ACKNOWLEDGED_CHANGE or\n state == constants.JOINING_CONFIG_CHANGED):\n seeds_list.append(seed)\n\n if len(seeds_list) > 0:\n seeds_list_str = ','.join(map(str, seeds_list))\n _log.info(\"Cassandra seeds list is {}\".format(seeds_list_str))\n\n # Read cassandra.yaml.\n with open(cassandra_yaml_file) as f:\n doc = yaml.load(f)\n\n # Fill in the correct listen_address and seeds values in the yaml\n # document.\n doc[\"listen_address\"] = ip\n doc[\"seed_provider\"][0][\"parameters\"][0][\"seeds\"] = seeds_list_str\n doc[\"endpoint_snitch\"] = \"GossipingPropertyFileSnitch\"\n\n # Write back to cassandra.yaml.\n with open(cassandra_yaml_file, \"w\") as f:\n f.write(WARNING_HEADER + \"\\n\")\n yaml.dump(doc, f)\n\n topology = WARNING_HEADER + \"\\n\" + \"dc={}\\nrack=RAC1\\n\".format(site_name)\n\n with open(cassandra_topology_file, \"w\") as f:\n f.write(topology)\n\n # Restart Cassandra and make sure it picks up the new list of seeds.\n _log.debug(\"Restarting Cassandra\")\n run_command(\"monit unmonitor -g cassandra\")\n run_command(\"service cassandra stop\")\n run_command(\"killall $(cat /var/lib/cassandra/cassandra.pid)\", log_error=False)\n run_command(\"rm -rf /var/lib/cassandra/\")\n run_command(\"mkdir -m 755 /var/lib/cassandra\")\n run_command(\"chown -R cassandra /var/lib/cassandra\")\n\n start_cassandra()\n\n _log.debug(\"Cassandra node successfully clustered\")\n\n else:\n # Something has gone wrong - the local node should be WAITING_TO_JOIN in\n # etcd (at the very least).\n _log.warning(\"No Cassandra cluster defined in etcd - unable to join\")\n\ndef can_contact_cassandra():\n # Use poll-tcp to allow us to contact the signalling namespace\n rc = run_command(\"/usr/share/clearwater/bin/poll-tcp 9160 127.0.0.1\")\n return (rc == 0)\n\ndef leave_cassandra_cluster(namespace=None):\n # We need Cassandra to be running so that we can connect on port 9160 and\n # decommission it. Check if we can connect on port 9160.\n if not can_contact_cassandra():\n start_cassandra()\n\n run_command(\"monit unmonitor -g cassandra\")\n run_command(\"nodetool decommission\", namespace)\n\n\ndef start_cassandra():\n cassandra_not_monitored = True\n\n # Wait until we can connect on port 9160 - i.e. Cassandra is running.\n while True:\n if cassandra_not_monitored:\n # The monit command can fail because monit is still processing\n # the unmonitor command from before (even though it has\n # finished unmonitoring cassandra)\n rc = run_command(\"monit monitor -g cassandra\")\n cassandra_not_monitored = (rc != 0)\n elif can_contact_cassandra():\n break\n\n # Sleep so we don't tight loop\n time.sleep(1)\n\n\ndef write_chronos_cluster_settings(filename, cluster_view, current_server):\n current_or_joining = [constants.JOINING_ACKNOWLEDGED_CHANGE,\n constants.JOINING_CONFIG_CHANGED,\n constants.NORMAL_ACKNOWLEDGED_CHANGE,\n constants.NORMAL_CONFIG_CHANGED,\n constants.NORMAL]\n leaving = [constants.LEAVING_ACKNOWLEDGED_CHANGE,\n constants.LEAVING_CONFIG_CHANGED]\n\n staying_servers = ([k for k, v in cluster_view.iteritems()\n if v in current_or_joining])\n leaving_servers = ([k for k, v in cluster_view.iteritems()\n if v in leaving])\n\n with open(filename, 'w') as f:\n f.write(dedent('''\\\n {}\n [cluster]\n localhost = {}\n ''').format(WARNING_HEADER, current_server))\n for node in staying_servers:\n f.write('node = {}\\n'.format(node))\n for node in leaving_servers:\n f.write('leaving = {}\\n'.format(node))\n", "sub_path": "src/metaswitch/clearwater/cluster_manager/plugin_utils.py", "file_name": "plugin_utils.py", "file_ext": "py", "file_size_in_byte": 8632, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "logging.getLogger", "line_number": 42, "usage_type": "call"}, {"api_name": "textwrap.dedent", "line_number": 44, "usage_type": "call"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants.LEAVING_ACKNOWLEDGED_CHANGE", "line_number": 53, "usage_type": "attribute"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants", "line_number": 53, "usage_type": "name"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants.LEAVING_CONFIG_CHANGED", "line_number": 54, "usage_type": "attribute"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants", "line_number": 54, "usage_type": "name"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants.NORMAL_ACKNOWLEDGED_CHANGE", "line_number": 55, "usage_type": "attribute"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants", "line_number": 55, "usage_type": "name"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants.NORMAL_CONFIG_CHANGED", "line_number": 56, "usage_type": "attribute"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants", "line_number": 56, "usage_type": "name"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants.NORMAL", "line_number": 57, "usage_type": "attribute"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants", "line_number": 57, "usage_type": "name"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants.NORMAL", "line_number": 58, "usage_type": "attribute"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants", "line_number": 58, "usage_type": "name"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants.NORMAL_ACKNOWLEDGED_CHANGE", "line_number": 59, "usage_type": "attribute"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants", "line_number": 59, "usage_type": "name"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants.NORMAL_CONFIG_CHANGED", "line_number": 60, "usage_type": "attribute"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants", "line_number": 60, "usage_type": "name"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants.JOINING_ACKNOWLEDGED_CHANGE", "line_number": 61, "usage_type": "attribute"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants", "line_number": 61, "usage_type": "name"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants.JOINING_CONFIG_CHANGED", "line_number": 62, "usage_type": "attribute"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants", "line_number": 62, "usage_type": "name"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants.NORMAL_ACKNOWLEDGED_CHANGE", "line_number": 97, "usage_type": "attribute"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants", "line_number": 97, "usage_type": "name"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants.NORMAL_CONFIG_CHANGED", "line_number": 98, "usage_type": "attribute"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants", "line_number": 98, "usage_type": "name"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants.JOINING_ACKNOWLEDGED_CHANGE", "line_number": 103, "usage_type": "attribute"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants", "line_number": 103, "usage_type": "name"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants.JOINING_CONFIG_CHANGED", "line_number": 104, "usage_type": "attribute"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants", "line_number": 104, "usage_type": "name"}, {"api_name": "yaml.load", "line_number": 113, "usage_type": "call"}, {"api_name": "yaml.dump", "line_number": 124, "usage_type": "call"}, {"api_name": "metaswitch.clearwater.etcd_shared.plugin_utils.run_command", "line_number": 133, "usage_type": "call"}, {"api_name": "metaswitch.clearwater.etcd_shared.plugin_utils.run_command", "line_number": 134, "usage_type": "call"}, {"api_name": "metaswitch.clearwater.etcd_shared.plugin_utils.run_command", "line_number": 135, "usage_type": "call"}, {"api_name": "metaswitch.clearwater.etcd_shared.plugin_utils.run_command", "line_number": 136, "usage_type": "call"}, {"api_name": "metaswitch.clearwater.etcd_shared.plugin_utils.run_command", "line_number": 137, "usage_type": "call"}, {"api_name": "metaswitch.clearwater.etcd_shared.plugin_utils.run_command", "line_number": 138, "usage_type": "call"}, {"api_name": "metaswitch.clearwater.etcd_shared.plugin_utils.run_command", "line_number": 151, "usage_type": "call"}, {"api_name": "metaswitch.clearwater.etcd_shared.plugin_utils.run_command", "line_number": 160, "usage_type": "call"}, {"api_name": "metaswitch.clearwater.etcd_shared.plugin_utils.run_command", "line_number": 161, "usage_type": "call"}, {"api_name": "metaswitch.clearwater.etcd_shared.plugin_utils.run_command", "line_number": 173, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 179, "usage_type": "call"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants.JOINING_ACKNOWLEDGED_CHANGE", "line_number": 183, "usage_type": "attribute"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants", "line_number": 183, "usage_type": "name"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants.JOINING_CONFIG_CHANGED", "line_number": 184, "usage_type": "attribute"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants", "line_number": 184, "usage_type": "name"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants.NORMAL_ACKNOWLEDGED_CHANGE", "line_number": 185, "usage_type": "attribute"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants", "line_number": 185, "usage_type": "name"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants.NORMAL_CONFIG_CHANGED", "line_number": 186, "usage_type": "attribute"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants", "line_number": 186, "usage_type": "name"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants.NORMAL", "line_number": 187, "usage_type": "attribute"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants", "line_number": 187, "usage_type": "name"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants.LEAVING_ACKNOWLEDGED_CHANGE", "line_number": 188, "usage_type": "attribute"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants", "line_number": 188, "usage_type": "name"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants.LEAVING_CONFIG_CHANGED", "line_number": 189, "usage_type": "attribute"}, {"api_name": "metaswitch.clearwater.cluster_manager.constants", "line_number": 189, "usage_type": "name"}, {"api_name": "textwrap.dedent", "line_number": 197, "usage_type": "call"}]} +{"seq_id": "444413301", "text": "from discord.ext import commands\n\nfrom cogs import utils\n\n\nclass Mee6Data(utils.Cog):\n\n mee6_exp_by_level = {}\n\n def __init__(self, bot:utils.Bot):\n super().__init__(bot)\n\n @classmethod\n def get_exp_by_level(cls, level:int) -> int:\n \"\"\"Gets the amount of exp associated with a level\"\"\"\n\n # https://mee6.github.io/Mee6-documentation/levelxp/\n # 5 * (lvl ^ 2) + 50 * lvl + 100\n\n if level in cls.mee6_exp_by_level:\n return cls.mee6_exp_by_level[level]\n if level == 0:\n return 0\n exp = 5 * ((level - 1) ** 2) + 50 * (level - 1) + 100 + cls.get_exp_by_level(level - 1)\n cls.mee6_exp_by_level[level] = exp\n return exp\n\n @classmethod\n def get_messages_by_level(cls, level:int) -> int:\n \"\"\"Gets the amount of exp associated with a level\"\"\"\n\n return int(cls.get_exp_by_level(level) / 20)\n\n @classmethod\n def get_level_by_messages(cls, messages:int) -> int:\n \"\"\"Gets the amount of exp associated with a level\"\"\"\n\n level = 0\n while cls.get_messages_by_level(level) <= messages:\n level += 1\n return level - 1\n\n @commands.command(cls=utils.Command)\n @commands.guild_only()\n async def getmessages(self, ctx:utils.Context, level:int):\n \"\"\"Gives you the amount of messages associated with a given Mee6 level\"\"\"\n\n return await ctx.send(f\"To get to level **{level}** you need to send **{self.get_messages_by_level(level)}** tracked messages\")\n\n @commands.command(cls=utils.Command, hidden=True)\n @commands.guild_only()\n async def listmee6roles(self, ctx:utils.Context):\n \"\"\"Lists the roles set up with Mee6\"\"\"\n\n # Get data from the Mee6 API\n base = \"https://mee6.xyz/api/plugins/levels/leaderboard/\"\n async with self.bot.session.get(base + str(ctx.guild.id)) as r:\n data = await r.json()\n if str(r.status) == '404':\n return await ctx.send(\"The leaderboard page for this guild is either not public or not present - Mee6 must be on your server for this to work.\")\n self.logger.info(f\"Grabbed Mee6 role data for guild {ctx.guild.id}\")\n\n # Output to user\n role_rewards = sorted(data['role_rewards'], key=lambda r: r['rank'])\n lines = [f\"You have {len(role_rewards)} roles set up to be given out by Mee6.\"]\n for role in role_rewards:\n lines.append(f\"Role at level {role['rank']}: **{role['role']['name']}**\")\n return await ctx.send('\\n'.join(lines))\n\n @commands.command(cls=utils.Command)\n @commands.has_permissions(manage_guild=True)\n @commands.guild_only()\n async def copymee6roles(self, ctx:utils.Context):\n \"\"\"Copies the Mee6 roles into your static role handling\"\"\"\n\n async with ctx.typing():\n\n # Get data from the Mee6 API\n base = \"https://mee6.xyz/api/plugins/levels/leaderboard/\"\n async with self.bot.session.get(base + str(ctx.guild.id)) as r:\n data = await r.json()\n if str(r.status) == '404':\n return await ctx.send(\"The leaderboard page for this guild is either not public or not present - Mee6 must be on your server for this to work.\")\n await ctx.send(f\"Grabbed {len(data['role_rewards'])} roles from Mee6 - now saving to database...\")\n\n # Save to db\n role_rewards = data['role_rewards']\n async with self.bot.database() as db:\n for role in role_rewards:\n await db(\n \"\"\"INSERT INTO static_role_gain (guild_id, role_id, threshold)\n VALUES ($1, $2, $3) ON CONFLICT (role_id) DO NOTHING\"\"\",\n ctx.guild.id, int(role['role']['id']), self.get_messages_by_level(role['rank'])\n )\n\n # Remove cached roles for the guild\n cog = self.bot.get_cog(\"RoleHandler\")\n if cog:\n cog.static_role_handles[ctx.guild.id] = None\n\n # Output to user\n return await ctx.send(f\"Saved {len(role_rewards)} role rewards from Mee6.\")\n\n @commands.command(cls=utils.Command, enabled=False)\n @commands.has_permissions(manage_guild=True)\n @commands.guild_only()\n async def copymee6exp(self, ctx:utils.Context):\n \"\"\"Copies the Mee6 exp into Cerberus\"\"\"\n\n # Check that they're not already copied\n async with self.bot.database() as db:\n data = await db(\"SELECT * FROM copied_mee6_exp WHERE guild_id=$1\", ctx.guild.id)\n if data:\n return await ctx.send(\"You've already copied over your exp from Mee6.\")\n\n async with ctx.typing():\n\n # Get data from the Mee6 API\n base = \"https://mee6.xyz/api/plugins/levels/leaderboard/\"\n user_data = []\n i = 0\n while True:\n async with self.bot.session.get(base + str(ctx.guild.id), params={'page': i, 'limit': 1000}) as r:\n data = await r.json()\n if str(r.status) == '404':\n return await ctx.send(\"The leaderboard page for this guild is either not public or not present - Mee6 must be on your server for this to work.\")\n elif str(r.status)[0] != '2':\n return await ctx.send(data)\n self.logger.info(f\"Grabbed Mee6 leaderboard data for guild {ctx.guild.id} page {i}\")\n if data['players']:\n user_data.extend(data['players'])\n else:\n break\n i += 1\n await ctx.send(f\"Grabbed data from Mee6, now putting {len(user_data)} fields into the database - this may take a few minutes.\")\n\n # Store in database\n async with self.bot.database() as db:\n await db(\"INSERT INTO copied_mee6_exp VALUES ($1) ON CONFLICT (guild_id) DO NOTHING\", ctx.guild.id)\n for user in user_data:\n self.bot.message_count[(int(user['id']), ctx.guild.id)] += user['message_count']\n await db(\n \"\"\"INSERT INTO static_user_messages (user_id, guild_id, message_count)\n VALUES ($1, $2, $3) ON CONFLICT (user_id, guild_id) DO UPDATE SET message_count=$3\"\"\",\n int(user['id']), ctx.guild.id, self.bot.message_count[(int(user['id']), ctx.guild.id)]\n )\n\n return await ctx.send(f\"Copied over {len(user_data)} users' exp from Mee6.\")\n\n\ndef setup(bot:utils.Bot):\n x = Mee6Data(bot)\n bot.add_cog(x)\n", "sub_path": "cogs/mee6_data.py", "file_name": "mee6_data.py", "file_ext": "py", "file_size_in_byte": 6566, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "cogs.utils.Cog", "line_number": 6, "usage_type": "attribute"}, {"api_name": "cogs.utils", "line_number": 6, "usage_type": "name"}, {"api_name": "cogs.utils.Bot", "line_number": 10, "usage_type": "attribute"}, {"api_name": "cogs.utils", "line_number": 10, "usage_type": "name"}, {"api_name": "cogs.utils.Context", "line_number": 45, "usage_type": "attribute"}, {"api_name": "cogs.utils", "line_number": 45, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 43, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 43, "usage_type": "name"}, {"api_name": "cogs.utils.Command", "line_number": 43, "usage_type": "attribute"}, {"api_name": "cogs.utils", "line_number": 43, "usage_type": "name"}, {"api_name": "discord.ext.commands.guild_only", "line_number": 44, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 44, "usage_type": "name"}, {"api_name": "cogs.utils.Context", "line_number": 52, "usage_type": "attribute"}, {"api_name": "cogs.utils", "line_number": 52, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 50, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 50, "usage_type": "name"}, {"api_name": "cogs.utils.Command", "line_number": 50, "usage_type": "attribute"}, {"api_name": "cogs.utils", "line_number": 50, "usage_type": "name"}, {"api_name": "discord.ext.commands.guild_only", "line_number": 51, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 51, "usage_type": "name"}, {"api_name": "cogs.utils.Context", "line_number": 73, "usage_type": "attribute"}, {"api_name": "cogs.utils", "line_number": 73, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 70, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 70, "usage_type": "name"}, {"api_name": "cogs.utils.Command", "line_number": 70, "usage_type": "attribute"}, {"api_name": "cogs.utils", "line_number": 70, "usage_type": "name"}, {"api_name": "discord.ext.commands.has_permissions", "line_number": 71, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 71, "usage_type": "name"}, {"api_name": "discord.ext.commands.guild_only", "line_number": 72, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 72, "usage_type": "name"}, {"api_name": "cogs.utils.Context", "line_number": 107, "usage_type": "attribute"}, {"api_name": "cogs.utils", "line_number": 107, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 104, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 104, "usage_type": "name"}, {"api_name": "cogs.utils.Command", "line_number": 104, "usage_type": "attribute"}, {"api_name": "cogs.utils", "line_number": 104, "usage_type": "name"}, {"api_name": "discord.ext.commands.has_permissions", "line_number": 105, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 105, "usage_type": "name"}, {"api_name": "discord.ext.commands.guild_only", "line_number": 106, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 106, "usage_type": "name"}, {"api_name": "cogs.utils.Bot", "line_number": 151, "usage_type": "attribute"}, {"api_name": "cogs.utils", "line_number": 151, "usage_type": "name"}]} +{"seq_id": "596556328", "text": "from . import Status\nfrom config import Config\nimport sys, os, subprocess, re\n\nsys.path.append(Config.DOOR_PATH)\nimport octopus\nsys.path.pop()\n\nACL = octopus.Octopus(os.path.join(Config.DOOR_PATH, \"users.txt\"))\nBLOCK_PATTERN = re.compile(\"#blocked\")\n\ndef register(email):\n try:\n subprocess.call(['sudo', '/etc/init.d/doord', 'stop'])\n key = ACL.read_user()\n ACL.users[email] = key\n ACL.save_users()\n return True\n except Exception as error:\n return False\n finally:\n subprocess.call(['sudo', '/etc/init.d/doord', 'start'])\n pass\n\ndef deregister(email):\n ACL.rm_user(email)\n return True\n\ndef block(email):\n key = ACL.users[email]\n ACL.users[email] = key + BLOCK_PATTERN.pattern\n ACL.save_users()\n return True\n\ndef is_blocked(email):\n if BLOCK_PATTERN.search(ACL.users[email]):\n return True\n else:\n return False\n\ndef unblock(email):\n if isBlocked(email):\n ACL.users[email] = BLOCK_PATTERN.sub('', ACL.users[email])\n ACL.save_users()\n return True\n return False\n\ndef check_status(email):\n if email in ACL.users:\n if isBlocked(email):\n return Status.blocked\n else:\n return Status.active\n else:\n return Status.non_existent\n", "sub_path": "app/services/door.py", "file_name": "door.py", "file_ext": "py", "file_size_in_byte": 1294, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "sys.path.append", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "config.Config.DOOR_PATH", "line_number": 5, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 5, "usage_type": "name"}, {"api_name": "sys.path.pop", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "octopus.Octopus", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "config.Config.DOOR_PATH", "line_number": 9, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 9, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 10, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 14, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "547063950", "text": "from flask import Flask\nfrom prometheus_flask_exporter import PrometheusMetrics\nfrom prometheus_client.core import GaugeMetricFamily\nimport pandas as pd\nimport os\nimport sys\n\n\nclass FailureExporter(object):\n\n def __init__(self, file_path, logger):\n self.data = self.get_data(file_path)\n self.logger = logger\n self.offset = 5\n self.current_row = -1\n self.length = self.data.shape[0] - 1\n self.columns = self.data.columns\n self.name_current = 'collectd_smart_smart_attribute_current'\n self.name_pretty = 'collectd_smart_smart_attribute_pretty'\n self.desc_current = \"Collectd_exporter: 'smart' Type: 'smart_attribute' Dstype: 'api.Gauge' Dsname: 'current'\"\n self.desc_pretty = \"Collectd_exporter: 'smart' Type: 'smart_attribute' Dstype: 'api.Gauge' Dsname: 'pretty'\"\n self.labels = ['instance', 'smart', 'type', 'index']\n self.instance = 'localhost'\n self.device = 'sda'\n\n def get_data(self, file_path):\n if file_path is None:\n raise FileNotFoundError(f\"'{file_path} 'not found\")\n data = pd.read_csv(file_path)\n # nan만 있는 컬럼 drop\n data.dropna(axis=1, how='all', inplace=True)\n # 결측치 0으로 치환\n data.fillna(0, inplace=True)\n return data\n \n def collect(self):\n metric_current = GaugeMetricFamily(name=self.name_current, documentation=self.desc_current, labels=self.labels)\n metric_pretty = GaugeMetricFamily(name=self.name_pretty, documentation=self.desc_pretty, labels=self.labels)\n if self.current_row > self.length:\n self.current_row = 0\n if self.current_row > -1:\n # 인스턴스 올라갈 때 값 들어가지 않도록 self.current_row 0부터 시작\n row = self.data.iloc[self.current_row]\n for index, value in row.items():\n if self.columns.get_loc(index) < self.offset:\n continue\n attr_name = index.replace('-raw', '') if index.endswith('raw') else index.replace('-normal', '')\n labels = [self.instance, self.device, attr_name, str(self.current_row)]\n if index.endswith('raw'):\n metric_pretty.add_metric(labels, value)\n else:\n metric_current.add_metric(labels, value)\n self.current_row += 1\n yield metric_current\n yield metric_pretty\n\n\napp = Flask(__name__)\nexporter = PrometheusMetrics(app)\n\n\n@app.route('/favicon.ico')\n@exporter.do_not_track()\ndef favicon():\n return 'ok'\n\n\n@app.route('/')\n@exporter.do_not_track()\ndef main():\n \"\"\" context root \"\"\"\n return \"\"\"\n \n Failure Exporter\n \n

Failure Exporter

\n

Metrics

\n \n \n \"\"\"\n\n\nif __name__ == '__main__':\n # app에 debug=True 파라미터 그냥 전달하면 /metrics 맵핑 안됨\n # DEBUG_METRICS 환경변수 추가 필요\n # https://github.com/rycus86/prometheus_flask_exporter/issues/40\n os.environ['DEBUG_METRICS'] = 'true'\n file_path = os.environ.get('path')\n exporter.registry.register(FailureExporter(file_path, app.logger))\n app.run(host='0.0.0.0', port='8002', debug=True)", "sub_path": "failure/failure-exporter.py", "file_name": "failure-exporter.py", "file_ext": "py", "file_size_in_byte": 3345, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pandas.read_csv", "line_number": 29, "usage_type": "call"}, {"api_name": "prometheus_client.core.GaugeMetricFamily", "line_number": 37, "usage_type": "call"}, {"api_name": "prometheus_client.core.GaugeMetricFamily", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 58, "usage_type": "call"}, {"api_name": "prometheus_flask_exporter.PrometheusMetrics", "line_number": 59, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 87, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 88, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 88, "usage_type": "attribute"}]} +{"seq_id": "34860606", "text": "import time\n\nimport cv2 as cv\nimport matplotlib.image as mpimg\nimport numpy as np\nfrom skimage.feature import hog\n\nfrom configuration import Configuration\n\nconfig = Configuration().__dict__\n\n\nclass FeatureExtraction:\n @staticmethod\n def bin_spatial(img, size=(32, 32)):\n color1 = cv.resize(img[:, :, 0], size).ravel()\n color2 = cv.resize(img[:, :, 1], size).ravel()\n color3 = cv.resize(img[:, :, 2], size).ravel()\n return np.hstack((color1, color2, color3))\n\n @staticmethod\n def color_hist(img, nbins=32): # bins_range=(0, 256)\n # Compute the histogram of the color channels separately\n channel1_hist = np.histogram(img[:, :, 0], bins=nbins)[0]\n channel2_hist = np.histogram(img[:, :, 1], bins=nbins)[0]\n channel3_hist = np.histogram(img[:, :, 2], bins=nbins)[0]\n # Concatenate the histograms into a single feature vector\n hist_features = np.concatenate((channel1_hist, channel2_hist, channel3_hist))\n # Return the individual histograms, bin_centers and feature vector\n return hist_features\n\n @staticmethod\n def get_hog_features(img, feature_vec=False, folder=\"\", filename=None):\n # Call with two outputs if vis==True\n features = hog(img,\n orientations=config[\"orient\"],\n pixels_per_cell=(config[\"pix_per_cell\"],\n config[\"pix_per_cell\"]),\n cells_per_block=(config[\"cell_per_block\"],\n config[\"cell_per_block\"]),\n transform_sqrt=True,\n visualise=False,\n feature_vector=feature_vec)\n return features\n\n @staticmethod\n def extract_features(img_files):\n from helper import Helper\n \"\"\"\n combine spatial bin, color histogram and gradient histogram features\n \"\"\"\n # Create a list to append feature vectors to\n features = []\n # Iterate through the list of images\n for img_file in img_files:\n\n # Read in each one by one\n img = mpimg.imread(img_file)\n\n # apply color conversion if other than 'RGB'\n feature_image = Helper.change_cspace(img)\n\n # get hog features for either specific channel or for all channels\n if config[\"hog_channel\"] == 'ALL':\n hog_features = []\n # get features for all 3 channels\n seconds = int(time.time() % 60)\n for channel in range(feature_image.shape[2]):\n single_channel_img = feature_image[:, :, channel]\n filename = \"{}-channel-{}\".format(seconds, str(channel))\n hog_features.append(FeatureExtraction.get_hog_features(single_channel_img,\n folder=\"../buffer/hog-train-features/\",\n filename=filename,\n feature_vec=True))\n hog_features = np.ravel(hog_features)\n else:\n # get features for specific channel\n single_channel_img = feature_image[:, :, config[\"hog_channel\"]]\n hog_features = FeatureExtraction.get_hog_features(single_channel_img, feature_vec=True)\n\n # Apply bin_spatial() to get spatial color features\n bin_features = FeatureExtraction.bin_spatial(feature_image, config[\"spatial_size\"])\n\n # Apply color_hist() to get color histogram features\n color_hist_features = FeatureExtraction.color_hist(feature_image, config[\"hist_bins\"])\n\n # concatenate all 3 types of features\n feature = np.concatenate((bin_features, color_hist_features, hog_features), axis=0)\n\n # Append the new feature vector to the features list\n features.append(feature)\n\n # Return list of feature vectors\n return features\n", "sub_path": "implementation/feature_extraction.py", "file_name": "feature_extraction.py", "file_ext": "py", "file_size_in_byte": 4085, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "configuration.Configuration", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.histogram", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.histogram", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.histogram", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 28, "usage_type": "call"}, {"api_name": "skimage.feature.hog", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.image.imread", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.image", "line_number": 58, "usage_type": "name"}, {"api_name": "helper.Helper.change_cspace", "line_number": 61, "usage_type": "call"}, {"api_name": "helper.Helper", "line_number": 61, "usage_type": "name"}, {"api_name": "time.time", "line_number": 67, "usage_type": "call"}, {"api_name": "{'Helper': 'helper.Helper'}.get_hog_features", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 75, "usage_type": "call"}, {"api_name": "{'Helper': 'helper.Helper'}.get_hog_features", "line_number": 79, "usage_type": "call"}, {"api_name": "{'Helper': 'helper.Helper'}.bin_spatial", "line_number": 82, "usage_type": "call"}, {"api_name": "{'Helper': 'helper.Helper'}.color_hist", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 88, "usage_type": "call"}]} +{"seq_id": "806603", "text": "import argparse\nimport os\nimport pickle\n\nimport torch\nfrom train_iq import TrainIQ\nfrom utils.data_loader import get_loader\nfrom torchvision.transforms import transforms\nimport pytorch_lightning as pl\n\ntransform = transforms.Compose([\n transforms.ToTensor(),\n transforms.ToPILImage(),\n transforms.RandomResizedCrop(224,\n scale=(1.00, 1.2),\n ratio=(0.75, 1.3333333333333333)),\n transforms.ToTensor(),\n transforms.Normalize(mean=[0.485, 0.456, 0.406],\n std=[0.229, 0.224, 0.225])])\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--emb_dim\", type=int, default=300,\n help=\"Embedding dimensionality of the model\")\n parser.add_argument(\"--hidden_dim\", type=int, default=300,\n help=\"Hidden dimensionality of the model\")\n parser.add_argument(\"--latent_dim\", type=int, default=300,\n help=\"Size of latent dimension\")\n parser.add_argument(\"--pwffn_dim\", type=int, default=600,\n help=\"Size of postionwise feedforward network in transformer\")\n parser.add_argument(\"--num_layers\", type=int, default=4,\n help=\"Number of transformer layers in encoder and decoder\")\n parser.add_argument(\"--num_heads\", type=int, default=4,\n help=\"Number of heads in the multi-head attention\")\n parser.add_argument(\"--lr\", type=float, default=3e-5,\n help=\"Learning rate of the network\")\n parser.add_argument(\"--num_pretraining_steps\", type=float, default=12000,\n help=\"Number of pretraining steps before turning on latent transformer\")\n parser.add_argument(\"--total_training_steps\", type=int, default=35000,\n help=\"Total number of training steps for the model\")\n parser.add_argument(\"--full_kl_step\", type=int, default=15000,\n help=\"Number of steps until KLD is annealed\")\n parser.add_argument(\"--kl_ceiling\", type=float, default=0.5)\n parser.add_argument(\"--aux_ceiling\", type=float, default=1.0)\n parser.add_argument(\"--image_recon_lambda\", type=float, default=0.1,\n help=\"How much to scale the image reconstruction loss by\")\n parser.add_argument(\"--batch_size\", type=int, default=128)\n # Data args\n parser.add_argument(\"--emb_file\", type=str, default=\"vectors/glove.6B.300d.txt\",\n help=\"Filepath for pretrained embeddings\")\n parser.add_argument(\"--dataset\", type=str,\n default=\"data/processed/iq_dataset.hdf5\")\n parser.add_argument(\"--val_dataset\", type=str,\n default=\"data/processed/iq_val_dataset.hdf5\")\n parser.add_argument(\"--vocab\", type=str, default=\"vocab.pkl\")\n parser.add_argument(\"--use_gpu\", type=bool, default=True)\n parser.add_argument(\"--num_gpus\", type=int, default=1)\n parser.add_argument(\"--print_note\", type=str, default=\"\")\n parser.add_argument(\"--input_mode\", type=str, default=\"ans\")\n\n args = parser.parse_args()\n\n device = torch.device('cuda' if torch.cuda.is_available()\n and args.use_gpu else 'cpu')\n args = parser.parse_args()\n args.device = device\n args.root_dir = os.getcwd()\n\n vocab = pickle.load(open(args.vocab, \"rb\"))\n trainGVT = TrainIQ(vocab, args).load_from_checkpoint() .to(args.device)\n trainer = pl.Trainer(max_steps=args.total_training_steps, gradient_clip_val=5,\n val_check_interval=500, limit_val_batches=100, gpus=args.num_gpus)\n test_data_loader = get_loader(os.path.join(os.getcwd(), args.val_dataset), transform, 128, shuffle=False, num_workers=8)\n trainer.test(trainGVT, test_dataloaders=test_data_loader)\n", "sub_path": "test_iq.py", "file_name": "test_iq.py", "file_ext": "py", "file_size_in_byte": 3838, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "torchvision.transforms.transforms.Compose", "line_number": 11, "usage_type": "call"}, {"api_name": "torchvision.transforms.transforms", "line_number": 11, "usage_type": "name"}, {"api_name": "torchvision.transforms.transforms.ToTensor", "line_number": 12, "usage_type": "call"}, {"api_name": "torchvision.transforms.transforms", "line_number": 12, "usage_type": "name"}, {"api_name": "torchvision.transforms.transforms.ToPILImage", "line_number": 13, "usage_type": "call"}, {"api_name": "torchvision.transforms.transforms", "line_number": 13, "usage_type": "name"}, {"api_name": "torchvision.transforms.transforms.RandomResizedCrop", "line_number": 14, "usage_type": "call"}, {"api_name": "torchvision.transforms.transforms", "line_number": 14, "usage_type": "name"}, {"api_name": "torchvision.transforms.transforms.ToTensor", "line_number": 17, "usage_type": "call"}, {"api_name": "torchvision.transforms.transforms", "line_number": 17, "usage_type": "name"}, {"api_name": "torchvision.transforms.transforms.Normalize", "line_number": 18, "usage_type": "call"}, {"api_name": "torchvision.transforms.transforms", "line_number": 18, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 64, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 68, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 70, "usage_type": "call"}, {"api_name": "train_iq.TrainIQ", "line_number": 71, "usage_type": "call"}, {"api_name": "pytorch_lightning.Trainer", "line_number": 72, "usage_type": "call"}, {"api_name": "utils.data_loader.get_loader", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 74, "usage_type": "call"}]} +{"seq_id": "1109106", "text": "#!/usr/bin/env python3\n\n# b85encode - a quick-and-dirty utility to Base85 encode a file or data from stdin\n\n# Released under the terms of the MIT license\n# ©2019-2020 Jon Yoder \n\nfrom base64 import b85encode\nimport os.path as path\nimport sys\n\ndef encode_file(file_name):\n\t'''Quickie command to Base85 encode a file'''\n\ttry:\n\t\tread_handle = open(file_name, 'rb')\n\t\tdata = read_handle.read()\n\texcept Exception as e:\n\t\tprint('Unable to open %s: %s' % (file_name, e))\n\t\n\tdest_name = file_name + '.b85'\n\tif path.exists(dest_name):\n\t\tresponse = input(\"%s exists. Overwrite? [y/N]: \" % dest_name)\n\t\tif not response or response.casefold()[0] != 'y':\n\t\t\treturn\n\t\n\ttry:\n\t\tout = open(dest_name, 'wb')\n\texcept Exception as e:\n\t\tprint('Unable to save %s: %s' % (dest_name, e))\n\n\tout.write(b85encode(data))\n\n\nif __name__ == '__main__':\n\tif len(sys.argv) == 2:\n\t\tencode_file(sys.argv[1])\n\telse:\n\t\tsys.stdout.buffer.write(b85encode(sys.stdin.buffer.read()))", "sub_path": "utils/b85encode.py", "file_name": "b85encode.py", "file_ext": "py", "file_size_in_byte": 965, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.path.exists", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "name"}, {"api_name": "base64.b85encode", "line_number": 31, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 35, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 36, "usage_type": "attribute"}, {"api_name": "sys.stdout.buffer.write", "line_number": 38, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 38, "usage_type": "attribute"}, {"api_name": "base64.b85encode", "line_number": 38, "usage_type": "call"}, {"api_name": "sys.stdin.buffer.read", "line_number": 38, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 38, "usage_type": "attribute"}]} +{"seq_id": "470756384", "text": "#################################################################################\n# WaterTAP Copyright (c) 2020-2023, The Regents of the University of California,\n# through Lawrence Berkeley National Laboratory, Oak Ridge National Laboratory,\n# National Renewable Energy Laboratory, and National Energy Technology\n# Laboratory (subject to receipt of any required approvals from the U.S. Dept.\n# of Energy). All rights reserved.\n#\n# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license\n# information, respectively. These files are also available online at the URL\n# \"https://github.com/watertap-org/watertap/\"\n#################################################################################\n\"\"\"\nThis module contains a zero-order representation of a clarifier unit\noperation.\n\"\"\"\n\nimport pyomo.environ as pyo\nfrom pyomo.environ import units as pyunits, Var\nfrom idaes.core import declare_process_block_class\n\nfrom watertap.core import build_sido, constant_intensity, ZeroOrderBaseData\n\n# Some more information about this module\n__author__ = \"Adam Atia\"\n\n\n@declare_process_block_class(\"ClarifierZO\")\nclass ClarifierZOData(ZeroOrderBaseData):\n \"\"\"\n Zero-Order model for a Clarifier unit operation.\n \"\"\"\n\n CONFIG = ZeroOrderBaseData.CONFIG()\n\n def build(self):\n super().build()\n\n self._tech_type = \"clarifier\"\n\n build_sido(self)\n constant_intensity(self)\n\n if self.config.process_subtype == \"HRCS_clarifier\":\n\n self.ferric_chloride_dose = Var(\n self.flowsheet().time,\n units=pyunits.mg / pyunits.L,\n bounds=(0, None),\n doc=\"Dosing rate of ferric chloride\",\n )\n self._fixed_perf_vars.append(self.ferric_chloride_dose)\n\n self.ferric_chloride_demand = Var(\n self.flowsheet().time,\n units=pyunits.kg / pyunits.hr,\n bounds=(0, None),\n doc=\"Consumption rate of ferric chloride\",\n )\n self._perf_var_dict[\"Ferric Chloride Demand\"] = self.ferric_chloride_demand\n\n @self.Constraint(\n self.flowsheet().time, doc=\"ferric chloride demand constraint\"\n )\n def ferric_chloride_demand_equation(b, t):\n return b.ferric_chloride_demand[t] == pyunits.convert(\n b.ferric_chloride_dose[t] * b.properties_in[t].flow_vol,\n to_units=pyunits.kg / pyunits.hr,\n )\n\n @property\n def default_costing_method(self):\n return self.cost_clarifier\n\n @staticmethod\n def cost_clarifier(blk, number_of_parallel_units=1):\n \"\"\"\n General method for costing clarifiers. Costing is carried out\n using either the general_power_law form or the standard form which\n computes HRT, sizing costs, and chemical input costs.\n Args:\n number_of_parallel_units (int, optional) - cost this unit as\n number_of_parallel_units parallel units (default: 1)\n \"\"\"\n # Get cost method for this technology\n cost_method = blk.unit_model._get_unit_cost_method(blk)\n valid_methods = [\"cost_power_law_flow\", \"cost_HRCS_clarifier\"]\n if cost_method == \"cost_power_law_flow\":\n blk.unit_model.cost_power_law_flow(blk, number_of_parallel_units)\n elif cost_method == \"cost_HRCS_clarifier\":\n # NOTE: number of units does not matter for cost_HRCS_clarifier\n # as its a linear function of membrane area\n blk.unit_model.cost_HRCS_clarifier(blk)\n else:\n raise KeyError(\n f\"{cost_method} is not a relevant cost method for \"\n f\"{blk.unit_model._tech_type}. Specify one of the following \"\n f\"cost methods in the unit's YAML file: {valid_methods}\"\n )\n\n @staticmethod\n def cost_HRCS_clarifier(blk):\n \"\"\"\n Method for costing a clarifier unit in a high-rate contact stabilization (HRCS) process.\n \"\"\"\n t0 = blk.flowsheet().time.first()\n\n # Get parameter dict from database\n parameter_dict = blk.unit_model.config.database.get_unit_operation_parameters(\n blk.unit_model._tech_type, subtype=blk.unit_model.config.process_subtype\n )\n\n # Get costing parameter sub-block for this technology\n HRT, size_cost = blk.unit_model._get_tech_parameters(\n blk,\n parameter_dict,\n blk.unit_model.config.process_subtype,\n [\"HRT\", \"sizing_cost\"],\n )\n\n # Add cost variable and constraint\n blk.capital_cost = pyo.Var(\n initialize=1,\n units=blk.config.flowsheet_costing_block.base_currency,\n bounds=(0, None),\n doc=\"Capital cost of unit operation\",\n )\n\n expr = pyo.units.convert(\n blk.unit_model.properties_in[t0].flow_vol * HRT * size_cost,\n to_units=blk.config.flowsheet_costing_block.base_currency,\n )\n\n # Determine if a costing factor is required\n blk.unit_model._add_cost_factor(\n blk, parameter_dict[\"capital_cost\"][\"cost_factor\"]\n )\n\n blk.capital_cost_constraint = pyo.Constraint(\n expr=blk.capital_cost == blk.cost_factor * expr\n )\n\n # Register flows\n blk.config.flowsheet_costing_block.cost_flow(\n blk.unit_model.electricity[t0], \"electricity\"\n )\n\n blk.config.flowsheet_costing_block.cost_flow(\n blk.unit_model.ferric_chloride_demand[t0], \"ferric_chloride\"\n )\n", "sub_path": "watertap/unit_models/zero_order/clarifier_zo.py", "file_name": "clarifier_zo.py", "file_ext": "py", "file_size_in_byte": 5634, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "watertap.core.ZeroOrderBaseData", "line_number": 28, "usage_type": "name"}, {"api_name": "watertap.core.ZeroOrderBaseData.CONFIG", "line_number": 33, "usage_type": "call"}, {"api_name": "watertap.core.ZeroOrderBaseData", "line_number": 33, "usage_type": "name"}, {"api_name": "watertap.core.build_sido", "line_number": 40, "usage_type": "call"}, {"api_name": "watertap.core.constant_intensity", "line_number": 41, "usage_type": "call"}, {"api_name": "pyomo.environ.Var", "line_number": 45, "usage_type": "call"}, {"api_name": "pyomo.environ.units.mg", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pyomo.environ.units", "line_number": 47, "usage_type": "name"}, {"api_name": "pyomo.environ.units.L", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pyomo.environ.Var", "line_number": 53, "usage_type": "call"}, {"api_name": "pyomo.environ.units.kg", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pyomo.environ.units", "line_number": 55, "usage_type": "name"}, {"api_name": "pyomo.environ.units.hr", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pyomo.environ.units.convert", "line_number": 65, "usage_type": "call"}, {"api_name": "pyomo.environ.units", "line_number": 65, "usage_type": "name"}, {"api_name": "pyomo.environ.units.kg", "line_number": 67, "usage_type": "attribute"}, {"api_name": "pyomo.environ.units", "line_number": 67, "usage_type": "name"}, {"api_name": "pyomo.environ.units.hr", "line_number": 67, "usage_type": "attribute"}, {"api_name": "pyomo.environ.Var", "line_number": 121, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 121, "usage_type": "name"}, {"api_name": "pyomo.environ.units.convert", "line_number": 128, "usage_type": "call"}, {"api_name": "pyomo.environ.units", "line_number": 128, "usage_type": "attribute"}, {"api_name": "pyomo.environ", "line_number": 128, "usage_type": "name"}, {"api_name": "pyomo.environ.Constraint", "line_number": 138, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 138, "usage_type": "name"}, {"api_name": "idaes.core.declare_process_block_class", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "451388212", "text": "import os\nimport sounddevice as sd\n\n\nSAMPLE_RATE = 16000\nNUM_CHANNELS = 1\nBLOCK_SIZE = 1024\nBATCH_SIZE = 32\nMODELS_BASE_PATH = os.path.join(\"rt_pie\", \"serialized_models\")\n\n\ndef __check_device_selection(devices, selection):\n try:\n device_number = int(selection)\n if device_number in range(0, len(devices)):\n return device_number\n raise ValueError()\n except Exception as e:\n print(f\"Invalid input, expected number between 0 and {len(devices) - 1}.\")\n exit(-1)\n\n\ndef prompt_audio_device(msg):\n audio_devices = sd.query_devices()\n print(f\"{msg} Available audio devices:\")\n if len(audio_devices) < 2:\n print(f\"Using the only available audio device:\")\n print(audio_devices[0][\"name\"])\n return audio_devices[0]\n print(audio_devices)\n selection = input('Choose your audio device: ')\n print(\"\\n\")\n return __check_device_selection(audio_devices, selection)\n", "sub_path": "rt_pie/config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 941, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "sounddevice.query_devices", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "117953333", "text": "import json\nimport unittest\n\nfrom mycroft.skills.intent_services.commonqa_service import CommonQAService\nfrom ovos_tskill_fakewiki import FakeWikiSkill\nfrom ovos_utils.messagebus import FakeBus, Message\n\n\nclass TestCommonQuery(unittest.TestCase):\n def setUp(self):\n self.bus = FakeBus()\n self.bus.emitted_msgs = []\n\n def get_msg(msg):\n self.bus.emitted_msgs.append(json.loads(msg))\n\n self.skill = FakeWikiSkill()\n self.skill._startup(self.bus, \"wiki.test\")\n\n self.cc = CommonQAService(self.bus)\n\n self.bus.on(\"message\", get_msg)\n\n def test_common_query_events(self):\n self.bus.emitted_msgs = []\n self.assertEqual(self.cc.skill_id, \"common_query.openvoiceos\")\n\n self.bus.emit(Message(\"common_query.question\",\n {\"utterance\": \"what is the speed of light\"}))\n\n expected = [\n # original query\n {'context': {},\n 'data': {'utterance': 'what is the speed of light'},\n 'type': 'common_query.question'},\n # thinking animation\n {'type': 'enclosure.mouth.think',\n 'data': {},\n 'context': {'destination': ['enclosure'],\n 'skill_id': self.cc.skill_id}},\n # send query\n {'type': 'question:query',\n 'data': {'phrase': 'what is the speed of light'},\n 'context': {'skill_id': self.cc.skill_id}},\n # skill announces its searching\n {'type': 'question:query.response',\n 'data': {'phrase': 'what is the speed of light',\n 'skill_id': 'wiki.test',\n 'searching': True},\n 'context': {'skill_id': 'wiki.test'}},\n # skill context set by skill for continuous dialog\n {'type': 'add_context',\n 'data': {'context': 'wiki_testFakeWikiKnows',\n 'word': 'what is the speed of light',\n 'origin': ''},\n 'context': {'skill_id': 'wiki.test'}},\n # final response\n {'type': 'question:query.response',\n 'data': {'phrase': 'what is the speed of light',\n 'skill_id': 'wiki.test',\n 'answer': \"answer 1\",\n 'callback_data': {'query': 'what is the speed of light',\n 'answer': \"answer 1\"},\n 'conf': 0.74},\n 'context': {'skill_id': 'wiki.test'}},\n # stop thinking animation\n {'type': 'enclosure.mouth.reset',\n 'data': {},\n 'context': {'destination': ['enclosure'],\n 'skill_id': self.cc.skill_id}\n },\n # tell enclosure about active skill (speak method)\n {'type': 'enclosure.active_skill',\n 'data': {'skill_id': self.cc.skill_id},\n 'context': {'destination': ['enclosure'],\n 'skill_id': self.cc.skill_id}},\n # execution of speak method\n {'type': 'speak',\n 'data': {'utterance': 'answer 1',\n 'expect_response': False,\n 'meta': {'skill': self.cc.skill_id},\n 'lang': 'en-us'},\n 'context': {'skill_id': self.cc.skill_id}},\n # skill callback event\n {'type': 'question:action',\n 'data': {'skill_id': 'wiki.test',\n 'phrase': 'what is the speed of light',\n 'callback_data': {'query': 'what is the speed of light',\n 'answer': 'answer 1'}},\n 'context': {'skill_id': self.cc.skill_id}}\n ]\n\n for ctr, msg in enumerate(expected):\n m = self.bus.emitted_msgs[ctr]\n self.assertEqual(msg, m)\n", "sub_path": "test/unittests/common_query/test_common_query.py", "file_name": "test_common_query.py", "file_ext": "py", "file_size_in_byte": 3876, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "unittest.TestCase", "line_number": 9, "usage_type": "attribute"}, {"api_name": "ovos_utils.messagebus.FakeBus", "line_number": 11, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 15, "usage_type": "call"}, {"api_name": "ovos_tskill_fakewiki.FakeWikiSkill", "line_number": 17, "usage_type": "call"}, {"api_name": "mycroft.skills.intent_services.commonqa_service.CommonQAService", "line_number": 20, "usage_type": "call"}, {"api_name": "ovos_utils.messagebus.Message", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "455980259", "text": "\"\"\"\nTest the creation and manipulation of GMT data containers.\n\"\"\"\nimport pytest\n\nfrom ..exceptions import GMTCLibError\nfrom ..clib.core import load_libgmt, create_session, destroy_session, \\\n get_constant\nfrom ..clib.io import create_data, _parse_data_family, \\\n DATA_FAMILIES, DATA_VIAS\n\n\ndef test_parse_data_family_single():\n \"Parsing a single family argument correctly.\"\n lib = load_libgmt()\n for family in DATA_FAMILIES:\n assert _parse_data_family(lib, family) == get_constant(family, lib)\n\n\ndef test_parse_data_family_via():\n \"Parsing a composite family argument (separated by |) correctly.\"\n lib = load_libgmt()\n test_cases = ((family, via)\n for family in DATA_FAMILIES\n for via in DATA_VIAS)\n for family, via in test_cases:\n composite = '|'.join([family, via])\n expected = get_constant(family, lib) + get_constant(via, lib)\n assert _parse_data_family(lib, composite) == expected\n\n\ndef test_parse_data_family_fails():\n \"Check if the function fails when given bad input\"\n lib = load_libgmt()\n test_cases = [\n 'SOME_random_STRING',\n 'GMT_IS_DATASET|GMT_VIA_MATRIX|GMT_VIA_VECTOR',\n 'GMT_IS_DATASET|NOT_A_PROPER_VIA',\n 'NOT_A_PROPER_FAMILY|GMT_VIA_MATRIX',\n 'NOT_A_PROPER_FAMILY|ALSO_INVALID',\n ]\n for test_case in test_cases:\n with pytest.raises(GMTCLibError):\n _parse_data_family(lib, test_case)\n\n\ndef test_create_data_dataset():\n \"Run the function to make sure it doesn't fail badly.\"\n lib = load_libgmt()\n session = create_session('test_create_data', lib)\n # Dataset from vectors\n data_vector = create_data(\n libgmt=lib,\n session=session,\n family='GMT_IS_DATASET|GMT_VIA_VECTOR',\n geometry='GMT_IS_POINT',\n mode='GMT_CONTAINER_ONLY',\n dim=[10, 20, 1, 0], # columns, rows, layers, dtype\n )\n # Dataset from matrices\n data_matrix = create_data(\n libgmt=lib,\n session=session,\n family='GMT_IS_DATASET|GMT_VIA_MATRIX',\n geometry='GMT_IS_POINT',\n mode='GMT_CONTAINER_ONLY',\n dim=[10, 20, 1, 0],\n )\n destroy_session(session, lib)\n assert data_vector != data_matrix\n\n\ndef test_create_data_grid_dim():\n \"Run the function to make sure it doesn't fail badly.\"\n lib = load_libgmt()\n session = create_session('test_create_data', lib)\n # Grids from matrices using dim\n create_data(\n libgmt=lib,\n session=session,\n family='GMT_IS_GRID|GMT_VIA_MATRIX',\n geometry='GMT_IS_SURFACE',\n mode='GMT_CONTAINER_ONLY',\n dim=[10, 20, 1, 0],\n )\n destroy_session(session, lib)\n\n\ndef test_create_data_grid_range():\n \"Run the function to make sure it doesn't fail badly.\"\n lib = load_libgmt()\n session = create_session('test_create_data', lib)\n # Grids from matrices using range and int\n create_data(\n libgmt=lib,\n session=session,\n family='GMT_IS_GRID|GMT_VIA_MATRIX',\n geometry='GMT_IS_SURFACE',\n mode='GMT_CONTAINER_ONLY',\n dim=[0, 0, 1, 0],\n ranges=[150., 250., -20., 20.],\n inc=[0.1, 0.2],\n )\n destroy_session(session, lib)\n\n\ndef test_create_data_fails():\n \"Test for failures on bad input\"\n lib = load_libgmt()\n session = create_session('test_create_data', lib)\n # Passing in invalid mode\n with pytest.raises(GMTCLibError):\n create_data(\n libgmt=lib,\n session=session,\n family='GMT_IS_DATASET',\n geometry='GMT_IS_SURFACE',\n mode='Not_a_valid_mode',\n dim=[0, 0, 1, 0],\n ranges=[150., 250., -20., 20.],\n inc=[0.1, 0.2],\n )\n # Passing in invalid geometry\n with pytest.raises(GMTCLibError):\n create_data(\n libgmt=lib,\n session=session,\n family='GMT_IS_GRID',\n geometry='Not_a_valid_geometry',\n mode='GMT_CONTAINER_ONLY',\n dim=[0, 0, 1, 0],\n ranges=[150., 250., -20., 20.],\n inc=[0.1, 0.2],\n )\n destroy_session(session, lib)\n", "sub_path": "gmt/tests/test_clib_io.py", "file_name": "test_clib_io.py", "file_ext": "py", "file_size_in_byte": 4145, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "clib.core.load_libgmt", "line_number": 15, "usage_type": "call"}, {"api_name": "clib.io.DATA_FAMILIES", "line_number": 16, "usage_type": "name"}, {"api_name": "clib.io._parse_data_family", "line_number": 17, "usage_type": "call"}, {"api_name": "clib.core.get_constant", "line_number": 17, "usage_type": "call"}, {"api_name": "clib.core.load_libgmt", "line_number": 22, "usage_type": "call"}, {"api_name": "clib.io.DATA_FAMILIES", "line_number": 24, "usage_type": "name"}, {"api_name": "clib.io.DATA_VIAS", "line_number": 25, "usage_type": "name"}, {"api_name": "clib.core.get_constant", "line_number": 28, "usage_type": "call"}, {"api_name": "clib.io._parse_data_family", "line_number": 29, "usage_type": "call"}, {"api_name": "clib.core.load_libgmt", "line_number": 34, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 43, "usage_type": "call"}, {"api_name": "exceptions.GMTCLibError", "line_number": 43, "usage_type": "argument"}, {"api_name": "clib.io._parse_data_family", "line_number": 44, "usage_type": "call"}, {"api_name": "clib.core.load_libgmt", "line_number": 49, "usage_type": "call"}, {"api_name": "clib.core.create_session", "line_number": 50, "usage_type": "call"}, {"api_name": "clib.io.create_data", "line_number": 52, "usage_type": "call"}, {"api_name": "clib.io.create_data", "line_number": 61, "usage_type": "call"}, {"api_name": "clib.core.destroy_session", "line_number": 69, "usage_type": "call"}, {"api_name": "clib.core.load_libgmt", "line_number": 75, "usage_type": "call"}, {"api_name": "clib.core.create_session", "line_number": 76, "usage_type": "call"}, {"api_name": "clib.io.create_data", "line_number": 78, "usage_type": "call"}, {"api_name": "clib.core.destroy_session", "line_number": 86, "usage_type": "call"}, {"api_name": "clib.core.load_libgmt", "line_number": 91, "usage_type": "call"}, {"api_name": "clib.core.create_session", "line_number": 92, "usage_type": "call"}, {"api_name": "clib.io.create_data", "line_number": 94, "usage_type": "call"}, {"api_name": "clib.core.destroy_session", "line_number": 104, "usage_type": "call"}, {"api_name": "clib.core.load_libgmt", "line_number": 109, "usage_type": "call"}, {"api_name": "clib.core.create_session", "line_number": 110, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 112, "usage_type": "call"}, {"api_name": "exceptions.GMTCLibError", "line_number": 112, "usage_type": "argument"}, {"api_name": "clib.io.create_data", "line_number": 113, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 124, "usage_type": "call"}, {"api_name": "exceptions.GMTCLibError", "line_number": 124, "usage_type": "argument"}, {"api_name": "clib.io.create_data", "line_number": 125, "usage_type": "call"}, {"api_name": "clib.core.destroy_session", "line_number": 135, "usage_type": "call"}]} +{"seq_id": "562575045", "text": "import torch\r\nimport torchvision\r\nimport torchvision.transforms as transforms\r\n\r\n\r\ndef data_pull_MNIST(Batch_Size=128, Num_Workers=4):\r\n train_dataset = torchvision.datasets.MNIST('/data/', train=True, download=True,\r\n transform=torchvision.transforms.Compose([\r\n torchvision.transforms.ToTensor(),\r\n torchvision.transforms.Normalize((0.1307,), (0.3081,))\r\n ]))\r\n\r\n test_dataset = torchvision.datasets.MNIST('/data/', train=False, download=True,\r\n transform=torchvision.transforms.Compose([\r\n torchvision.transforms.ToTensor(),\r\n torchvision.transforms.Normalize((0.1307,), (0.3081,))\r\n ]))\r\n\r\n SEED = 11\r\n\r\n # CUDA?\r\n cuda = torch.cuda.is_available()\r\n print(\"CUDA Available?\", cuda)\r\n\r\n # For reproducibility\r\n torch.manual_seed(SEED)\r\n\r\n if cuda:\r\n torch.cuda.manual_seed(SEED)\r\n\r\n # dataloader arguments - something you'll fetch these from cmdprmt\r\n dataloader_args = dict(shuffle=True, batch_size=Batch_Size, num_workers=Num_Workers,\r\n pin_memory=True) if cuda else dict(shuffle=True, batch_size=128)\r\n\r\n trainloader = torch.utils.data.DataLoader(train_dataset, **dataloader_args)\r\n\r\n testloader = torch.utils.data.DataLoader(test_dataset, **dataloader_args)\r\n\r\n\r\n return trainloader, testloader", "sub_path": "S9/package/data_loader/MNIST_data_pull.py", "file_name": "MNIST_data_pull.py", "file_ext": "py", "file_size_in_byte": 1655, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "torchvision.datasets.MNIST", "line_number": 7, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 7, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.Compose", "line_number": 8, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 8, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 9, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 10, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 10, "usage_type": "attribute"}, {"api_name": "torchvision.datasets.MNIST", "line_number": 13, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 13, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.Compose", "line_number": 14, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 15, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 15, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 16, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 16, "usage_type": "attribute"}, {"api_name": "torch.cuda.is_available", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 22, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 29, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 35, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 37, "usage_type": "attribute"}]} +{"seq_id": "534862012", "text": "from .AES import *\nfrom hashlib import sha256\nimport json, base64, os, random\nimport urllib.parse, requests\nimport time, base64\nimport logging\nimport tart\n\n\n\nclass Snappy(object):\n\n # encryption key for blob data\n ENCRYPTION_KEY = 'M02cnQ51Ji97vwT4'\n\n # request token generator pattern\n PATTERN = '000111011110111000111101010111101'\\\n '1010001001110011000110001000110'\n\n # current authentication token\n auth_token = None\n\n # initial static auth token used when logging in\n STATIC_TOKEN = 'm198sOkJEn37DjqZ32lpRu76xmw288xSQ9'\n\n # snapchat api host.\n #API_HOST = 'http://localhost/bq' #'https://feelinsonice.appspot.com'\n\n MEDIA_IMAGE = 0\n MEDIA_VIDEO = 1\n MEDIA_VIDEO_NOAUDIO = 2\n MEDIA_FRIEND_REQUEST = 3\n MEDIA_FRIEND_REQUEST_IMAGE = 4\n MEDIA_FRIEND_REQUEST_VIDEO = 5\n MEDIA_FRIEND_REQUEST_VIDEO_NOAUDIO = 6\n\n STATUS_NONE = -1\n STATUS_SENT = 0\n STATUS_DELIVERED = 1\n STATUS_OPENED = 2\n STATUS_SCREENSHOT = 3\n\n FRIEND_CONFIRMED = 0\n FRIEND_UNCONFIRMED = 1\n FRIEND_BLOCKED = 2\n\n PRIVACY_EVERYONE = 0\n PRIVACY_FRIENDS = 1\n\n\n # Secret (used as a Salt for request tokens)\n SECRET = 'iEk21fuwZApXlz93750dmW22pw389dPwOk'\n\n URL = 'https://feelinsonice-hrd.appspot.com/bq'#'http://localhost/bq'\n\n # static headers for each and every request\n _headers = {\n #'Content-Type': 'application/octet-stream',\n 'user-agent': 'Snapchat/6.0.1 CFNetwork/609.1.4 Darwin/13.0.0',\n 'version': '6.0.1'\n }\n\n VERSION = '6.0.1'\n # authenticated.\n authenticated = False\n authToken = \"\"\n # AES instance\n _crypto = None\n\n username = None\n password = None\n\n # constants\n BLOB = 0x1\n JSON = 0x2\n\n def __init__(self, username=None, password=None, authToken=''):\n self.authenticated = 'false'\n self.username = username\n self.password = password\n self.login(username, password)\n\n if (authToken == ''):\n self.login(username, password)\n else:\n self.authToken = authToken\n self.authenticated = 'true'\n\n\n def getTime(self, rounded=True):\n if (rounded):\n return int(round(time.time() * 1000))\n return time.time() * 1000\n\n def testEmpty(self, dictionary, key):\n if key in dictionary:\n return dictionary[key]\n return ''\n\n\n def isMedia(self, blob):\n if (blob[0] == chr(00) and blob[1] == chr(00)):\n return True\n\n elif (blob[0] == chr(0xFF) and blob[1] == chr(0xD8)):\n return True\n\n return False\n\n def _get_crypto(self):\n '''\n Load up a local instance of the aes.py cipher library.\n '''\n\n if self._crypto is None:\n # ECB is required due to a recent change in the Snapchat API\n self._crypto = AES.new(self.ENCRYPTION_KEY, SB_AES_ECB)\n\n return self._crypto\n\n\n def _decrypt(self, data):\n '''\n Decrypt data.\n '''\n crypto = self._get_crypto()\n return crypto.decrypt(self.pkcs5_pad(data))\n\n def _encrypt(self, data):\n '''\n Encrypt data.\n '''\n crypto = self._get_crypto()\n return crypto.encrypt(self.pkcs5_pad(data))\n\n def pkcs5_pad(self, data, blocksize=16):\n pad_count = blocksize - len(data) % blocksize\n return data + (chr(pad_count) * pad_count).encode('utf-8')\n\n def hash(self, first, second):\n '''\n Given an auth_token and a timestamp, generate a snapchat request token\n '''\n\n # salt the authtoken and timestamp\n first = self.SECRET + str(first)\n second = str(second) + self.SECRET\n first = first.encode('utf-8')\n second = second.encode('utf-8')\n # hash the newly salted values.\n hash1 = sha256(first).hexdigest()\n hash2 = sha256(second).hexdigest()\n\n # the snapchat req_token is a blend between the hashed+salted\n # auth_token and the hashed+salted timestamp.\n # Random characters are taken from each (using PATTERN) and blended\n # together to form a \"hash-like\" string\n out = ''\n for i in range(0, len(self.PATTERN)):\n if self.PATTERN[i] == '0':\n out += hash1[i]\n else:\n out += hash2[i]\n return out\n\n\n def sendData(self, endpoint, data, params, files=None):\n\n data['req_token'] = self.hash(params[0], params[1])\n data['version'] = self.VERSION\n url = self.URL + endpoint\n if files != None:\n payload = requests.post(url, data=data, files=files, headers=self._headers)\n else:\n payload = requests.post(url, data=data, headers=self._headers)\n print(payload.status_code)\n if payload.status_code != 200:\n print(\"sending data failed!\")\n raise Exception\n else:\n return payload\n\n def login(self, username, password):\n '''\n Perform a snapchat login.\n '''\n\n timestamp = self.getTime()\n print(username)\n result = self.sendData('/login',\n {'username': username,\n 'password': password,\n 'timestamp': timestamp},\n [self.STATIC_TOKEN,\n timestamp])\n result = result.json()\n print(result)\n if result and result.get('auth_token'):\n print(result['auth_token'])\n # successful login, set the auth token.\n self.authenticated = 'true'\n self.authToken = result['auth_token']\n self.username = username\n self.password = password\n else:\n self.authenticated = 'false'\n\n\n def logout(self):\n if self.authenticated != 'true':\n return False\n\n result = self.sendData('/logout',\n {'timestamp': self.getTime(),\n 'username': self.username},\n [self.authToken,\n timestamp])\n\n return result is None\n\n\n def getUpdates(self, since=0):\n timestamp = self.getTime()\n result = self.sendData('/updates',\n {'timestamp': timestamp,\n 'username': self.username,\n 'update_timestamp': since},\n [self.authToken,\n timestamp])\n print(result.content)\n result = result.json()\n return result\n\n def getSnaps(self, since=0):\n updates = self.getUpdates()\n\n if not updates:\n return False\n\n print(\"Snaps exist!\")\n snaps = []\n for item in updates['snaps']:\n # if 'm' in item:\n # print(\"raw media\", item['m'])\n snap = {\n 'url': item['id'],\n 'media_id': self.testEmpty(item, 'c_id'),\n 'media_type': self.testEmpty(item, 'm'),\n 'countdown': self.testEmpty(item,'t'),\n 'user': self.testEmpty(item, 'sn'),\n 'recipient': self.testEmpty(item, 'rp'),\n 'title': item['st'],\n 'screenshotCount': self.testEmpty(item, 'c'),\n 'sent': item['sts'],\n 'opened': item['ts']\n }\n print(snap, \"\\n\")\n snaps.append(snap)\n\n return snaps\n\n\n def upload(self, mediaType, filename):\n # if not self.authenticated:\n # print(\"ERE\")\n # return False\n\n mediaId = self.username.upper() + str(int(time.time()))\n timestamp = self.getTime()\n origfile = open(os.getcwd() + filename, 'rb')\n encrypteddat = self._encrypt(origfile.read())\n origfile.close()\n encrfile = open(os.getcwd() + filename, 'wb')\n encrfile.write(encrypteddat)\n encrfile.close()\n files = {'data': open(os.getcwd() + filename, 'rb')}\n result = self.sendData('/upload',\n {'media_id': mediaId,\n 'type': mediaType,\n 'timestamp': timestamp,\n 'username': self.username},\n [self.authToken,\n timestamp], files)\n #print(result)\n if result:\n return mediaId\n else:\n return False\n\n def send(self, mediaId, recipients, time=3):\n # if not self.authenticated:\n # return False\n timestamp = self.getTime()\n result = self.sendData('/send',\n {'media_id': mediaId,\n 'recipient': ','.join(recipients),\n 'time': time,\n 'timestamp': timestamp,\n 'username': self.username},\n [self.authToken,\n timestamp])\n #print(result.content)\n\n return result\n\n def getFriends(self, since=0):\n addedFriends = self.getUpdates()\n if not addedFriends:\n return False\n\n return addedFriends['friends']\n\n def addFriends(self, usernames):\n if self.authenticated != 'true':\n return False\n\n friends = [ {'display': '', 'name': username, 'type': self.FRIEND_UNCONFIRMED} for username in usernames ]\n\n timestamp = self.getTime()\n result = self.sendData('/friend',\n {'action': 'multiadddelete',\n 'friend': json.dumps({'friendsToAdd': friends, 'friendsToDelete': []}),\n 'timestamp': timestamp,\n 'username': self.username},\n [self.authToken,\n timestamp])\n return result.content\n\n def clearFeed(self):\n if self.authenticated != 'true':\n return False\n\n timestamp = self.getTime()\n result = self.sendData('/clear',\n {'timestamp': timestamp,\n 'username': self.username},\n [self.authToken,\n timestamp])\n\n return result.content\n\n def getBests(self, friends):\n if self.authenticated != 'true':\n return False\n\n timestamp = self.getTime()\n result = self.sendData('/bests',\n {'friend_usernames': json.dumps(friends),\n 'timestamp': timestamp,\n 'username': self.username},\n [self.authToken,\n timestamp])\n result = result.json()\n #print(result)\n friends = []\n #print(result['teamsnapchat']['best_friends'])\n for item in result:\n friend = {}\n friend[item] = result[item]['best_friends']\n friends.append(friend)\n\n return friends\n\n def getMedia(self, ident):\n '''\n Given a snap id, return the raw decrypted blob content of a\n jpeg image, or mp4 video.\n '''\n timestamp = self.getTime()\n result = self.sendData('/blob',\n {'id': ident,\n 'timestamp': timestamp,\n 'username': self.username},\n [self.authToken,\n timestamp])\n print(result.content)\n if (result.status_code != 200):\n return None\n print(type(result.content))\n if (self.isMedia(str(result.content)) == False):\n print(\"Encrypted!\")\n data = self._decrypt(result.content)\n else:\n data = result.content\n return data\n\n def markRead(self, ident, viewed=1):\n '''\n Given a snap ID, mark it read and return the status_code\n '''\n timestamp = int(time.time())\n snapInfo = {ident: {'t': timestamp, 'sv': viewed + (random.randint(0, 21474836) / 2147483 / 10)}}\n events = [\n {'eventName': 'SNAP_VIEW',\n 'params': {'id': ident},\n 'ts': int(time.time()) - viewed\n },\n {'eventName': 'SNAP_EXPIRED',\n 'params': {'id': ident},\n 'ts': int(time.time())\n }]\n\n self.sendEvents(events, snapInfo)\n\n def sendEvents(self, events, snapInfo):\n # if not self.authenticated:\n # return False\n\n timestamp = self.getTime()\n result = self.sendData('/update_snaps',\n {'events': json.dumps(events),\n 'json': json.dumps(snapInfo),\n 'timestamp': timestamp,\n 'username': self.username},\n [self.authToken,\n timestamp])\n return result\n", "sub_path": "app/snappy.py", "file_name": "snappy.py", "file_ext": "py", "file_size_in_byte": 12086, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "time.time", "line_number": 92, "usage_type": "call"}, {"api_name": "time.time", "line_number": 93, "usage_type": "call"}, {"api_name": "AES.new", "line_number": 117, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 151, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 152, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 173, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 175, "usage_type": "call"}, {"api_name": "time.time", "line_number": 268, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 270, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 273, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 276, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 322, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 348, "usage_type": "call"}, {"api_name": "time.time", "line_number": 391, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 392, "usage_type": "call"}, {"api_name": "time.time", "line_number": 396, "usage_type": "call"}, {"api_name": "time.time", "line_number": 400, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 411, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 412, "usage_type": "call"}]} +{"seq_id": "507315208", "text": "from PIL import Image\n\nafbeelding = Image.open(\"kermit.png\")\nafbeelding.show()\n\nbreedte = str(afbeelding.width)\nhoogte = str(afbeelding.height)\n\nhelft_breedte = afbeelding.width // 2\n\nhelft_hoogte = afbeelding.height // 2\n\nnieuwe_afmeting = (helft_breedte, helft_hoogte)\n\nkleinere_afbeelding = afbeelding.resize(nieuwe_afmeting)\n\nkleinere_afbeelding.save('kermit_klein.png')\n", "sub_path": "03-MemesGifs/bewerk_afbeelding.py", "file_name": "bewerk_afbeelding.py", "file_ext": "py", "file_size_in_byte": 375, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "PIL.Image.open", "line_number": 3, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 3, "usage_type": "name"}]} +{"seq_id": "13737021", "text": "import os\nimport shutil\n\nfrom cement.core.controller import CementBaseController, expose\nfrom cement.core import handler, hook\nfrom wo.core.aptget import WOAptGet\nfrom wo.core.download import WODownload\nfrom wo.core.extract import WOExtract\nfrom wo.core.fileutils import WOFileUtils\nfrom wo.core.logging import Log\nfrom wo.core.services import WOService\nfrom wo.core.shellexec import WOShellExec\nfrom wo.core.variables import WOVariables\nfrom wo.cli.plugins.stack_pref import pre_pref, post_pref\n\n\nclass WOStackUpgradeController(CementBaseController):\n class Meta:\n label = 'upgrade'\n stacked_on = 'stack'\n stacked_type = 'nested'\n exit_on_close = True\n description = ('Upgrade stack safely')\n arguments = [\n (['--all'],\n dict(help='Upgrade all stack', action='store_true')),\n (['--web'],\n dict(help='Upgrade web stack', action='store_true')),\n (['--admin'],\n dict(help='Upgrade admin tools stack', action='store_true')),\n (['--nginx'],\n dict(help='Upgrade Nginx stack', action='store_true')),\n (['--php'],\n dict(help='Upgrade PHP 7.2 stack', action='store_true')),\n (['--php73'],\n dict(help='Upgrade PHP 7.3 stack', action='store_true')),\n (['--mysql'],\n dict(help='Upgrade MySQL stack', action='store_true')),\n (['--wpcli'],\n dict(help='Upgrade WPCLI', action='store_true')),\n (['--redis'],\n dict(help='Upgrade Redis', action='store_true')),\n (['--netdata'],\n dict(help='Upgrade Netdata', action='store_true')),\n (['--dashboard'],\n dict(help='Upgrade WordOps Dashboard', action='store_true')),\n (['--composer'],\n dict(help='Upgrade Composer', action='store_true')),\n (['--phpmyadmin'],\n dict(help='Upgrade phpMyAdmin', action='store_true')),\n (['--no-prompt'],\n dict(help=\"Upgrade Packages without any prompt\",\n action='store_true')),\n (['--force'],\n dict(help=\"Force Packages upgrade without any prompt\",\n action='store_true')),\n ]\n\n @expose(hide=True)\n def default(self):\n # All package update\n apt_packages = []\n packages = []\n nginx_packages = []\n empty_packages = []\n pargs = self.app.pargs\n\n if ((not pargs.web) and (not pargs.nginx) and\n (not pargs.php) and (not pargs.php73) and\n (not pargs.mysql) and\n (not pargs.all) and (not pargs.wpcli) and\n (not pargs.netdata) and (not pargs.composer) and\n (not pargs.phpmyadmin) and (not pargs.dashboard) and\n (not pargs.redis)):\n pargs.web = True\n\n if pargs.all:\n pargs.web = True\n pargs.netdata = True\n pargs.composer = True\n pargs.dashboard = True\n pargs.phpmyadmin = True\n pargs.redis = True\n pargs.wpcli = True\n pargs.php73 = True\n\n if pargs.web:\n if WOAptGet.is_installed(self, 'nginx-custom'):\n pargs.nginx = True\n else:\n Log.info(self, \"Nginx is not already installed\")\n pargs.php = True\n pargs.mysql = True\n pargs.wpcli = True\n\n if pargs.nginx:\n if WOAptGet.is_installed(self, 'nginx-custom'):\n apt_packages = apt_packages + WOVariables.wo_nginx\n nginx_packages = nginx_packages + WOVariables.wo_nginx\n else:\n Log.info(self, \"Nginx Stable is not already installed\")\n\n if pargs.php:\n if WOAptGet.is_installed(self, 'php7.2-fpm'):\n if not WOAptGet.is_installed(self, 'php7.3-fpm'):\n apt_packages = apt_packages + WOVariables.wo_php + \\\n WOVariables.wo_php_extra\n else:\n apt_packages = apt_packages + WOVariables.wo_php\n else:\n Log.info(self, \"PHP 7.2 is not installed\")\n\n if pargs.php73:\n if WOAptGet.is_installed(self, 'php7.3-fpm'):\n if not WOAptGet.is_installed(self, 'php7.2-fpm'):\n apt_packages = apt_packages + WOVariables.wo_php73 + \\\n WOVariables.wo_php_extra\n else:\n apt_packages = apt_packages + WOVariables.wo_php73\n else:\n Log.info(self, \"PHP 7.3 is not installed\")\n\n if pargs.mysql:\n if WOAptGet.is_installed(self, 'mariadb-server'):\n apt_packages = apt_packages + WOVariables.wo_mysql\n else:\n Log.info(self, \"MariaDB is not installed\")\n\n if pargs.redis:\n if WOAptGet.is_installed(self, 'redis-server'):\n apt_packages = apt_packages + WOVariables.wo_redis\n else:\n Log.info(self, \"Redis is not installed\")\n\n if pargs.wpcli:\n if os.path.isfile('/usr/local/bin/wp'):\n packages = packages + [[\"https://github.com/wp-cli/wp-cli/\"\n \"releases/download/v{0}/\"\n \"wp-cli-{0}.phar\"\n \"\".format(WOVariables.wo_wp_cli),\n \"/usr/local/bin/wp\",\n \"WP-CLI\"]]\n else:\n Log.info(self, \"WPCLI is not installed with WordOps\")\n\n if pargs.netdata:\n if os.path.isdir('/opt/netdata'):\n packages = packages + [['https://my-netdata.io/'\n 'kickstart-static64.sh',\n '/var/lib/wo/tmp/kickstart.sh',\n 'Netdata']]\n\n if pargs.dashboard:\n if os.path.isfile('/var/www/22222/htdocs/index.php'):\n packages = packages + \\\n [[\"https://github.com/WordOps/wordops-dashboard/\"\n \"releases/download/v{0}/wordops-dashboard.tar.gz\"\n .format(WOVariables.wo_dashboard),\n \"/var/lib/wo/tmp/wo-dashboard.tar.gz\",\n \"WordOps Dashboard\"]]\n\n if pargs.phpmyadmin:\n if os.path.isdir('/var/www/22222/htdocs/db/pma'):\n packages = packages + \\\n [[\"https://files.phpmyadmin.net\"\n \"/phpMyAdmin/{0}/\"\n \"phpMyAdmin-{0}-\"\n \"all-languages\"\n \".tar.gz\".format(WOVariables.wo_phpmyadmin),\n \"/var/lib/wo/tmp/pma.tar.gz\",\n \"PHPMyAdmin\"]]\n else:\n Log.error(self, \"phpMyAdmin isn't installed\")\n\n if pargs.composer:\n if os.path.isfile('/usr/local/bin/composer'):\n packages = packages + [[\"https://getcomposer.org/installer\",\n \"/var/lib/wo/tmp/composer-install\",\n \"Composer\"]]\n else:\n Log.error(self, \"Composer isn't installed\")\n if len(apt_packages) or len(packages):\n if len(apt_packages):\n Log.info(self, \"Your site may be down for few seconds if \"\n \"you are upgrading Nginx, PHP-FPM, MariaDB or Redis\")\n # Check prompt\n if ((not pargs.no_prompt) and (not pargs.force)):\n start_upgrade = input(\"Do you want to continue:[y/N]\")\n if start_upgrade != \"Y\" and start_upgrade != \"y\":\n Log.error(self, \"Not starting package update\")\n Log.info(self, \"Updating APT packages, please wait...\")\n if set(WOVariables.wo_nginx).issubset(set(apt_packages)):\n pre_pref(self, [\"nginx-custom\", \"nginx-wo\"])\n # apt-get update\n WOAptGet.update(self)\n if set(WOVariables.wo_php).issubset(set(apt_packages)):\n WOAptGet.remove(self, ['php7.2-fpm'],\n auto=False, purge=True)\n if set(WOVariables.wo_php73).issubset(set(apt_packages)):\n WOAptGet.remove(self, ['php7.3-fpm'],\n auto=False, purge=True)\n # Update packages\n WOAptGet.install(self, apt_packages)\n post_pref(self, apt_packages, empty_packages, True)\n # Post Actions after package updates\n\n if len(packages):\n if pargs.wpcli:\n WOFileUtils.rm(self, '/usr/local/bin/wp')\n\n if pargs.netdata:\n WOFileUtils.rm(self, '/var/lib/wo/tmp/kickstart.sh')\n\n if pargs.dashboard:\n WOFileUtils.rm(self, '/var/www/22222/htdocs/index.php')\n\n Log.debug(self, \"Downloading following: {0}\".format(packages))\n WODownload.download(self, packages)\n\n if pargs.wpcli:\n WOFileUtils.chmod(self, \"/usr/local/bin/wp\", 0o775)\n\n if pargs.netdata:\n Log.info(self, \"Upgrading Netdata, please wait...\")\n WOShellExec.cmd_exec(self, \"/bin/bash /var/lib/wo/tmp/\"\n \"kickstart.sh \"\n \"--dont-wait\")\n\n if pargs.dashboard:\n Log.debug(self, \"Extracting wo-dashboard.tar.gz \"\n \"to location {0}22222/htdocs/\"\n .format(WOVariables.wo_webroot))\n WOExtract.extract(self, '/var/lib/wo/tmp/'\n 'wo-dashboard.tar.gz',\n '{0}22222/htdocs'\n .format(WOVariables.wo_webroot))\n WOFileUtils.chown(self, \"{0}22222/htdocs\"\n .format(WOVariables.wo_webroot),\n WOVariables.wo_php_user,\n WOVariables.wo_php_user, recursive=True)\n\n if pargs.composer:\n Log.info(self, \"Upgrading Composer, please wait...\")\n WOShellExec.cmd_exec(self, \"php -q /var/lib/wo\"\n \"/tmp/composer-install \"\n \"--install-dir=/var/lib/wo/tmp/\")\n shutil.copyfile('/var/lib/wo/tmp/composer.phar',\n '/usr/local/bin/composer')\n WOFileUtils.chmod(self, \"/usr/local/bin/composer\", 0o775)\n\n if pargs.phpmyadmin:\n Log.info(self, \"Upgrading phpMyAdmin, please wait...\")\n WOExtract.extract(self, '/var/lib/wo/tmp/pma.tar.gz',\n '/var/lib/wo/tmp/')\n shutil.copyfile(('{0}22222/htdocs/db/pma'\n '/config.inc.php'\n .format(WOVariables.wo_webroot)),\n ('/var/lib/wo/tmp/phpMyAdmin-{0}'\n '-all-languages/config.inc.php'\n .format(WOVariables.wo_phpmyadmin))\n )\n WOFileUtils.rm(self, '{0}22222/htdocs/db/pma'\n .format(WOVariables.wo_webroot))\n shutil.move('/var/lib/wo/tmp/phpMyAdmin-{0}'\n '-all-languages/'\n .format(WOVariables.wo_phpmyadmin),\n '{0}22222/htdocs/db/pma/'\n .format(WOVariables.wo_webroot))\n WOFileUtils.chown(self, \"{0}22222/htdocs\"\n .format(WOVariables.wo_webroot),\n WOVariables.wo_php_user,\n WOVariables.wo_php_user, recursive=True)\n\n Log.info(self, \"Successfully updated packages\")\n else:\n self.app.args.print_help()\n", "sub_path": "wo/cli/plugins/stack_upgrade.py", "file_name": "stack_upgrade.py", "file_ext": "py", "file_size_in_byte": 12456, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "cement.core.controller.CementBaseController", "line_number": 17, "usage_type": "name"}, {"api_name": "wo.core.aptget.WOAptGet.is_installed", "line_number": 88, "usage_type": "call"}, {"api_name": "wo.core.aptget.WOAptGet", "line_number": 88, "usage_type": "name"}, {"api_name": "wo.core.logging.Log.info", "line_number": 91, "usage_type": "call"}, {"api_name": "wo.core.logging.Log", "line_number": 91, "usage_type": "name"}, {"api_name": "wo.core.aptget.WOAptGet.is_installed", "line_number": 97, "usage_type": "call"}, {"api_name": "wo.core.aptget.WOAptGet", "line_number": 97, "usage_type": "name"}, {"api_name": "wo.core.variables.WOVariables.wo_nginx", "line_number": 98, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables", "line_number": 98, "usage_type": "name"}, {"api_name": "wo.core.variables.WOVariables.wo_nginx", "line_number": 99, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables", "line_number": 99, "usage_type": "name"}, {"api_name": "wo.core.logging.Log.info", "line_number": 101, "usage_type": "call"}, {"api_name": "wo.core.logging.Log", "line_number": 101, "usage_type": "name"}, {"api_name": "wo.core.aptget.WOAptGet.is_installed", "line_number": 104, "usage_type": "call"}, {"api_name": "wo.core.aptget.WOAptGet", "line_number": 104, "usage_type": "name"}, {"api_name": "wo.core.aptget.WOAptGet.is_installed", "line_number": 105, "usage_type": "call"}, {"api_name": "wo.core.aptget.WOAptGet", "line_number": 105, "usage_type": "name"}, {"api_name": "wo.core.variables.WOVariables.wo_php", "line_number": 106, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables", "line_number": 106, "usage_type": "name"}, {"api_name": "wo.core.variables.WOVariables.wo_php_extra", "line_number": 107, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables", "line_number": 107, "usage_type": "name"}, {"api_name": "wo.core.variables.WOVariables.wo_php", "line_number": 109, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables", "line_number": 109, "usage_type": "name"}, {"api_name": "wo.core.logging.Log.info", "line_number": 111, "usage_type": "call"}, {"api_name": "wo.core.logging.Log", "line_number": 111, "usage_type": "name"}, {"api_name": "wo.core.aptget.WOAptGet.is_installed", "line_number": 114, "usage_type": "call"}, {"api_name": "wo.core.aptget.WOAptGet", "line_number": 114, "usage_type": "name"}, {"api_name": "wo.core.aptget.WOAptGet.is_installed", "line_number": 115, "usage_type": "call"}, {"api_name": "wo.core.aptget.WOAptGet", "line_number": 115, "usage_type": "name"}, {"api_name": "wo.core.variables.WOVariables.wo_php73", "line_number": 116, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables", "line_number": 116, "usage_type": "name"}, {"api_name": "wo.core.variables.WOVariables.wo_php_extra", "line_number": 117, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables", "line_number": 117, "usage_type": "name"}, {"api_name": "wo.core.variables.WOVariables.wo_php73", "line_number": 119, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables", "line_number": 119, "usage_type": "name"}, {"api_name": "wo.core.logging.Log.info", "line_number": 121, "usage_type": "call"}, {"api_name": "wo.core.logging.Log", "line_number": 121, "usage_type": "name"}, {"api_name": "wo.core.aptget.WOAptGet.is_installed", "line_number": 124, "usage_type": "call"}, {"api_name": "wo.core.aptget.WOAptGet", "line_number": 124, "usage_type": "name"}, {"api_name": "wo.core.variables.WOVariables.wo_mysql", "line_number": 125, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables", "line_number": 125, "usage_type": "name"}, {"api_name": "wo.core.logging.Log.info", "line_number": 127, "usage_type": "call"}, {"api_name": "wo.core.logging.Log", "line_number": 127, "usage_type": "name"}, {"api_name": "wo.core.aptget.WOAptGet.is_installed", "line_number": 130, "usage_type": "call"}, {"api_name": "wo.core.aptget.WOAptGet", "line_number": 130, "usage_type": "name"}, {"api_name": "wo.core.variables.WOVariables.wo_redis", "line_number": 131, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables", "line_number": 131, "usage_type": "name"}, {"api_name": "wo.core.logging.Log.info", "line_number": 133, "usage_type": "call"}, {"api_name": "wo.core.logging.Log", "line_number": 133, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path", "line_number": 136, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables.wo_wp_cli", "line_number": 140, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables", "line_number": 140, "usage_type": "name"}, {"api_name": "wo.core.logging.Log.info", "line_number": 144, "usage_type": "call"}, {"api_name": "wo.core.logging.Log", "line_number": 144, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 147, "usage_type": "call"}, {"api_name": "os.path", "line_number": 147, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 154, "usage_type": "call"}, {"api_name": "os.path", "line_number": 154, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables.wo_dashboard", "line_number": 158, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables", "line_number": 158, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path", "line_number": 163, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables.wo_phpmyadmin", "line_number": 169, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables", "line_number": 169, "usage_type": "name"}, {"api_name": "wo.core.logging.Log.error", "line_number": 173, "usage_type": "call"}, {"api_name": "wo.core.logging.Log", "line_number": 173, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 176, "usage_type": "call"}, {"api_name": "os.path", "line_number": 176, "usage_type": "attribute"}, {"api_name": "wo.core.logging.Log.error", "line_number": 181, "usage_type": "call"}, {"api_name": "wo.core.logging.Log", "line_number": 181, "usage_type": "name"}, {"api_name": "wo.core.logging.Log.info", "line_number": 184, "usage_type": "call"}, {"api_name": "wo.core.logging.Log", "line_number": 184, "usage_type": "name"}, {"api_name": "wo.core.logging.Log.error", "line_number": 190, "usage_type": "call"}, {"api_name": "wo.core.logging.Log", "line_number": 190, "usage_type": "name"}, {"api_name": "wo.core.logging.Log.info", "line_number": 191, "usage_type": "call"}, {"api_name": "wo.core.logging.Log", "line_number": 191, "usage_type": "name"}, {"api_name": "wo.core.variables.WOVariables.wo_nginx", "line_number": 192, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables", "line_number": 192, "usage_type": "name"}, {"api_name": "wo.cli.plugins.stack_pref.pre_pref", "line_number": 193, "usage_type": "call"}, {"api_name": "wo.core.aptget.WOAptGet.update", "line_number": 195, "usage_type": "call"}, {"api_name": "wo.core.aptget.WOAptGet", "line_number": 195, "usage_type": "name"}, {"api_name": "wo.core.variables.WOVariables.wo_php", "line_number": 196, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables", "line_number": 196, "usage_type": "name"}, {"api_name": "wo.core.aptget.WOAptGet.remove", "line_number": 197, "usage_type": "call"}, {"api_name": "wo.core.aptget.WOAptGet", "line_number": 197, "usage_type": "name"}, {"api_name": "wo.core.variables.WOVariables.wo_php73", "line_number": 199, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables", "line_number": 199, "usage_type": "name"}, {"api_name": "wo.core.aptget.WOAptGet.remove", "line_number": 200, "usage_type": "call"}, {"api_name": "wo.core.aptget.WOAptGet", "line_number": 200, "usage_type": "name"}, {"api_name": "wo.core.aptget.WOAptGet.install", "line_number": 203, "usage_type": "call"}, {"api_name": "wo.core.aptget.WOAptGet", "line_number": 203, "usage_type": "name"}, {"api_name": "wo.cli.plugins.stack_pref.post_pref", "line_number": 204, "usage_type": "call"}, {"api_name": "wo.core.fileutils.WOFileUtils.rm", "line_number": 209, "usage_type": "call"}, {"api_name": "wo.core.fileutils.WOFileUtils", "line_number": 209, "usage_type": "name"}, {"api_name": "wo.core.fileutils.WOFileUtils.rm", "line_number": 212, "usage_type": "call"}, {"api_name": "wo.core.fileutils.WOFileUtils", "line_number": 212, "usage_type": "name"}, {"api_name": "wo.core.fileutils.WOFileUtils.rm", "line_number": 215, "usage_type": "call"}, {"api_name": "wo.core.fileutils.WOFileUtils", "line_number": 215, "usage_type": "name"}, {"api_name": "wo.core.logging.Log.debug", "line_number": 217, "usage_type": "call"}, {"api_name": "wo.core.logging.Log", "line_number": 217, "usage_type": "name"}, {"api_name": "wo.core.download.WODownload.download", "line_number": 218, "usage_type": "call"}, {"api_name": "wo.core.download.WODownload", "line_number": 218, "usage_type": "name"}, {"api_name": "wo.core.fileutils.WOFileUtils.chmod", "line_number": 221, "usage_type": "call"}, {"api_name": "wo.core.fileutils.WOFileUtils", "line_number": 221, "usage_type": "name"}, {"api_name": "wo.core.logging.Log.info", "line_number": 224, "usage_type": "call"}, {"api_name": "wo.core.logging.Log", "line_number": 224, "usage_type": "name"}, {"api_name": "wo.core.shellexec.WOShellExec.cmd_exec", "line_number": 225, "usage_type": "call"}, {"api_name": "wo.core.shellexec.WOShellExec", "line_number": 225, "usage_type": "name"}, {"api_name": "wo.core.logging.Log.debug", "line_number": 230, "usage_type": "call"}, {"api_name": "wo.core.logging.Log", "line_number": 230, "usage_type": "name"}, {"api_name": "wo.core.variables.WOVariables.wo_webroot", "line_number": 232, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables", "line_number": 232, "usage_type": "name"}, {"api_name": "wo.core.extract.WOExtract.extract", "line_number": 233, "usage_type": "call"}, {"api_name": "wo.core.extract.WOExtract", "line_number": 233, "usage_type": "name"}, {"api_name": "wo.core.variables.WOVariables.wo_webroot", "line_number": 236, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables", "line_number": 236, "usage_type": "name"}, {"api_name": "wo.core.fileutils.WOFileUtils.chown", "line_number": 237, "usage_type": "call"}, {"api_name": "wo.core.fileutils.WOFileUtils", "line_number": 237, "usage_type": "name"}, {"api_name": "wo.core.variables.WOVariables.wo_webroot", "line_number": 238, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables", "line_number": 238, "usage_type": "name"}, {"api_name": "wo.core.variables.WOVariables.wo_php_user", "line_number": 239, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables", "line_number": 239, "usage_type": "name"}, {"api_name": "wo.core.variables.WOVariables.wo_php_user", "line_number": 240, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables", "line_number": 240, "usage_type": "name"}, {"api_name": "wo.core.logging.Log.info", "line_number": 243, "usage_type": "call"}, {"api_name": "wo.core.logging.Log", "line_number": 243, "usage_type": "name"}, {"api_name": "wo.core.shellexec.WOShellExec.cmd_exec", "line_number": 244, "usage_type": "call"}, {"api_name": "wo.core.shellexec.WOShellExec", "line_number": 244, "usage_type": "name"}, {"api_name": "shutil.copyfile", "line_number": 247, "usage_type": "call"}, {"api_name": "wo.core.fileutils.WOFileUtils.chmod", "line_number": 249, "usage_type": "call"}, {"api_name": "wo.core.fileutils.WOFileUtils", "line_number": 249, "usage_type": "name"}, {"api_name": "wo.core.logging.Log.info", "line_number": 252, "usage_type": "call"}, {"api_name": "wo.core.logging.Log", "line_number": 252, "usage_type": "name"}, {"api_name": "wo.core.extract.WOExtract.extract", "line_number": 253, "usage_type": "call"}, {"api_name": "wo.core.extract.WOExtract", "line_number": 253, "usage_type": "name"}, {"api_name": "shutil.copyfile", "line_number": 255, "usage_type": "call"}, {"api_name": "wo.core.variables.WOVariables.wo_webroot", "line_number": 257, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables", "line_number": 257, "usage_type": "name"}, {"api_name": "wo.core.variables.WOVariables.wo_phpmyadmin", "line_number": 260, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables", "line_number": 260, "usage_type": "name"}, {"api_name": "wo.core.fileutils.WOFileUtils.rm", "line_number": 262, "usage_type": "call"}, {"api_name": "wo.core.fileutils.WOFileUtils", "line_number": 262, "usage_type": "name"}, {"api_name": "wo.core.variables.WOVariables.wo_webroot", "line_number": 263, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables", "line_number": 263, "usage_type": "name"}, {"api_name": "shutil.move", "line_number": 264, "usage_type": "call"}, {"api_name": "wo.core.variables.WOVariables.wo_phpmyadmin", "line_number": 266, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables", "line_number": 266, "usage_type": "name"}, {"api_name": "wo.core.variables.WOVariables.wo_webroot", "line_number": 268, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables", "line_number": 268, "usage_type": "name"}, {"api_name": "wo.core.fileutils.WOFileUtils.chown", "line_number": 269, "usage_type": "call"}, {"api_name": "wo.core.fileutils.WOFileUtils", "line_number": 269, "usage_type": "name"}, {"api_name": "wo.core.variables.WOVariables.wo_webroot", "line_number": 270, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables", "line_number": 270, "usage_type": "name"}, {"api_name": "wo.core.variables.WOVariables.wo_php_user", "line_number": 271, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables", "line_number": 271, "usage_type": "name"}, {"api_name": "wo.core.variables.WOVariables.wo_php_user", "line_number": 272, "usage_type": "attribute"}, {"api_name": "wo.core.variables.WOVariables", "line_number": 272, "usage_type": "name"}, {"api_name": "wo.core.logging.Log.info", "line_number": 274, "usage_type": "call"}, {"api_name": "wo.core.logging.Log", "line_number": 274, "usage_type": "name"}, {"api_name": "cement.core.controller.expose", "line_number": 59, "usage_type": "call"}]} +{"seq_id": "406131624", "text": "\"\"\"Test the select_crawler module.\"\"\"\nimport pytest\n\nfrom sqlfluff.core.linter.linter import Linter\nfrom sqlfluff.utils.analysis.select_crawler import SelectCrawler\n\n\ndef _parse_and_crawl_outer(sql):\n \"\"\"Helper function for select crawlers.\n\n Given a SQL statement this crawls the SQL and instantiates\n a SelectCrawler on the outer relevant segment.\n \"\"\"\n linter = Linter(dialect=\"ansi\")\n parsed = linter.parse_string(sql)\n # Create a crawler from the root segment.\n crawler = SelectCrawler.from_root(parsed.tree, linter.dialect)\n # Analyse the segment.\n return crawler, linter\n\n\n@pytest.mark.parametrize(\n \"sql, expected_json\",\n [\n (\n # Test trivial query.\n \"select 1\",\n {\"selectables\": [\"select 1\"]},\n ),\n (\n # Test set expression.\n \"select 1 union select 2\",\n {\"selectables\": [\"select 1\", \"select 2\"]},\n ),\n (\n # Test multiple CTEs.\n \"with cte1 as (select 1 as x), cte2 as (select 2 as y) \"\n \"select * from cte1 join cte2 using (x)\",\n {\n \"ctes\": {\n \"CTE1\": {\"selectables\": [\"select 1 as x\"]},\n \"CTE2\": {\"selectables\": [\"select 2 as y\"]},\n },\n \"query_type\": \"WithCompound\",\n \"selectables\": [\"select * from cte1 join cte2 using (x)\"],\n },\n ),\n (\n # Nested CTEs (from AM04 test suite)\n \"\"\"\n with a as (\n with b as (select 1 from c)\n select * from b\n )\n select * from a\n \"\"\",\n {\n \"ctes\": {\n \"A\": {\n \"ctes\": {\"B\": {\"selectables\": [\"select 1 from c\"]}},\n \"query_type\": \"WithCompound\",\n \"selectables\": [\"select * from b\"],\n }\n },\n \"query_type\": \"WithCompound\",\n \"selectables\": [\"select * from a\"],\n },\n ),\n (\n # Nested CTEs (from AM04 test suite)\n \"\"\"\n with b as (select 1 from c)\n select * from (\n with a as (select * from b)\n select * from a\n )\n \"\"\",\n {\n \"ctes\": {\"B\": {\"selectables\": [\"select 1 from c\"]}},\n \"query_type\": \"WithCompound\",\n \"selectables\": [\n \"select * from (\\n\"\n \" with a as (select * from b)\\n\"\n \" select * from a\\n\"\n \" )\"\n ],\n },\n ),\n (\n # Test that subquery in \"from\" not included.\n \"select a.x from (select z from b)\",\n {\"selectables\": [\"select a.x from (select z from b)\"]},\n ),\n (\n # Test that subquery in \"from\" / \"join\" not included.\n \"select a.x from a join (select z from b) as b on (a.x = b.x)\",\n {\n \"selectables\": [\n \"select a.x from a join (select z from b) as b on (a.x = b.x)\"\n ]\n },\n ),\n (\n # In CTE main query, test that subquery in \"from\" not included.\n \"with prep as (select 1) select a.x from (select z from b)\",\n {\n \"ctes\": {\"PREP\": {\"selectables\": [\"select 1\"]}},\n \"query_type\": \"WithCompound\",\n \"selectables\": [\"select a.x from (select z from b)\"],\n },\n ),\n (\n # In CTE main query, test that subquery in \"from\" / \"join\" not included.\n \"with prep as (select 1) \"\n \"select a.x from a join (select z from b) as b on (a.x = b.x)\",\n {\n \"ctes\": {\"PREP\": {\"selectables\": [\"select 1\"]}},\n \"query_type\": \"WithCompound\",\n \"selectables\": [\n \"select a.x from a join (select z from b) as b on (a.x = \" \"b.x)\"\n ],\n },\n ),\n (\n \"\"\"with prep_1 as (\n with d as (\n select x, z from b\n )\n select * from d\n)\nselect\n a.x, a.y, b.z\nfrom a\njoin prep_1 using (x)\n\"\"\",\n {\n \"ctes\": {\n \"PREP_1\": {\n \"ctes\": {\n \"D\": {\"selectables\": [\"select x, z from b\"]},\n },\n \"query_type\": \"WithCompound\",\n \"selectables\": [\"select * from d\"],\n }\n },\n \"query_type\": \"WithCompound\",\n \"selectables\": [\n \"select\\n a.x, a.y, b.z\\nfrom a\\njoin prep_1 using (x)\"\n ],\n },\n ),\n ],\n)\ndef test_select_crawler_constructor(sql, expected_json):\n \"\"\"Test SelectCrawler when created using constructor.\"\"\"\n crawler, _ = _parse_and_crawl_outer(sql)\n assert all(\n cte.cte_definition_segment is not None\n for cte in crawler.query_tree.ctes.values()\n )\n json_query_tree = crawler.query_tree.as_json()\n assert expected_json == json_query_tree\n\n\ndef test_select_crawler_nested():\n \"\"\"Test invoking with an outer from_expression_segment.\"\"\"\n sql = \"\"\"\nselect\n a.x, a.y, b.z\nfrom a\njoin (\n with d as (\n select x, z from b\n )\n select * from d\n) using (x)\n \"\"\"\n crawler, linter = _parse_and_crawl_outer(sql)\n sc = SelectCrawler(\n crawler.query_tree.selectables[0]\n .select_info.table_aliases[1]\n .from_expression_element,\n linter.dialect,\n )\n assert sc.query_tree.as_json() == {\n \"selectables\": [\n \"select * from d\",\n ],\n \"ctes\": {\"D\": {\"selectables\": [\"select x, z from b\"]}},\n \"query_type\": \"WithCompound\",\n }\n", "sub_path": "test/utils/analysis/test_select_crawler.py", "file_name": "test_select_crawler.py", "file_ext": "py", "file_size_in_byte": 5894, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "sqlfluff.core.linter.linter.Linter", "line_number": 14, "usage_type": "call"}, {"api_name": "sqlfluff.utils.analysis.select_crawler.SelectCrawler.from_root", "line_number": 17, "usage_type": "call"}, {"api_name": "sqlfluff.utils.analysis.select_crawler.SelectCrawler", "line_number": 17, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 22, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 22, "usage_type": "attribute"}, {"api_name": "sqlfluff.utils.analysis.select_crawler.SelectCrawler", "line_number": 179, "usage_type": "call"}]} +{"seq_id": "152914730", "text": "#--------------------------------------------------------------------------\n# File and Version Information:\n# $Id$\n#\n# Description:\n# Module Connection...\n#\n#------------------------------------------------------------------------\n\n\"\"\"Standard connection class for irods clients.\n\nThis software was developed for the LCLS project. If you use all or \npart of it, please give an appropriate acknowledgment.\n\n@version $Id$\n\n@author Andy Salnikov\n\"\"\"\n\n\n#------------------------------\n# Module's version from CVS --\n#------------------------------\n__version__ = \"$Revision$\"\n# $Source$\n\n#--------------------------------\n# Imports of standard modules --\n#--------------------------------\nimport sys\n\n#---------------------------------\n# Imports of base class module --\n#---------------------------------\n\n#-----------------------------\n# Imports for other modules --\n#-----------------------------\nimport irods\n\n#----------------------------------\n# Local non-exported definitions --\n#----------------------------------\n\n#------------------------\n# Exported definitions --\n#------------------------\n\n#---------------------\n# Class definition --\n#---------------------\nclass Connection( object ) :\n\n def __init__( self ):\n\n self._conn = None\n\n def __del__(self):\n if self._conn : self._conn.disconnect() \n\n def connection(self):\n \n if self._conn : return self._conn\n\n # some parameters come from the configuration\n myEnv, status = irods.getRodsEnv()\n if status < 0:\n return self._conn\n \n host = myEnv.getRodsHost()\n port = myEnv.getRodsPort()\n user = myEnv.getRodsUserName()\n zone = myEnv.getRodsZone()\n \n conn, errMsg = irods.rcConnect(host, port, user, zone)\n if conn :\n status = irods.clientLogin(conn)\n if status != 0:\n conn.disconnect()\n conn = None\n self._conn = conn\n\n return self._conn\n\n#\n# In case someone decides to run this module\n#\nif __name__ == \"__main__\" :\n\n # In principle we can try to run test suite for this module,\n # have to think about it later. Right now just abort.\n sys.exit ( \"Module is not supposed to be run as main module\" )\n", "sub_path": "iRODSAccess/tags/V00-00-13/src/Connection.py", "file_name": "Connection.py", "file_ext": "py", "file_size_in_byte": 2257, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "irods.getRodsEnv", "line_number": 66, "usage_type": "call"}, {"api_name": "irods.rcConnect", "line_number": 75, "usage_type": "call"}, {"api_name": "irods.clientLogin", "line_number": 77, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 92, "usage_type": "call"}]} +{"seq_id": "184321076", "text": "# -*- coding: cp1252 -*-\nimport os\nimport re\nimport sys\nimport pandas\nimport random\nimport numpy as np\nimport sklearn.utils\n\n\n### The tld related imports were meant to be used to extract primary-domain\n# but is commented out because it returned too many urls as not urls, however it was based on a compiled list of domains and is not removed in the script in case it can be repurposed in the future for benchmarking, supplimenting etc\n\n'''\n#Get most up-to-date list for domain level extraction\nfrom tld.utils import update_tld_names\nfrom tld import get_tld\nupdate_tld_names()\nfull_data['site']=full_data['site'].apply(lambda x: get_tld(x, as_object=True).domain )\n'''\n\n\n#Load the data into a dataframe splitting into columns and naming the columns\nfull_data=pandas.read_csv('input/dga-dataset_clean.txt',names=['site','source','type'])\nprint(full_data)\n\n#Standardize the text to be all lowercase and convert any missing data to a string (which is the data type of all the non-missing data)\nfor name in ['site','source','type']:\n\tfull_data[name]=full_data[name].apply(lambda x: x.lower() if type(x)!=type(float()) else ' ')\n\t\n#Correct misspellings, based on this sample simply determining legit or not legit by the first letter is good enough (so no need for word matching by determining type of misspelling)\nfull_data['type']=full_data['type'].apply(lambda x: 'dga' if x[0]=='d' else ('legit' if x[0]=='l' else x))\n\n\n#printing the basic set of the two columns to make sure changes until now ave created the proper elements (basically make sure nothing unexpected shows up in the data)\nprint(set(full_data['type']))\nprint('\\n\\n')\nprint(set(full_data['source']))\nprint('\\n\\n')\n\n\n\n# Store sites with no label for testing later\nmissing_data=full_data[full_data['type']==' '].copy()\nprint('Number of missing types: {}'.format(missing_data.shape[0]))\nmissing_data.to_csv('output/dga_dataset_missing.csv')\nmissing_data=None\n\n\n\n\n#Remove unlabeled sites\n\nfull_data=full_data[full_data['type']!=' ']\nfull_data['type']=full_data['type'].apply(lambda x: 1.0 if x[0]=='d' else 0.0)\n\n\n\n#### Extract Primary Domian\n\n\ndef domain_extract(site):\n\t#Remove unneeded url segments that are likely top-level domains\n\tsite_segments={len(site_segment):site_segment for site_segment in re.sub(r'(^www[.]|^www1[.]|^https://|^http://|^[a-z][.]|[.][a-z]{1,3}$)','',site).split('.')}\n\t#Taking the longest segment since it may consume too much time to find or create a more dynamic domain extractor to ensure we aren't getting sub-domains\n\treturn site_segments[max(site_segments.keys())]\n\n\nfull_data['site']=full_data['site'].apply(lambda site: domain_extract(site))\n\n\n\n#Split Data for training and validation\nn_rows=full_data.shape[0]\nsplit=int(round(0.75*n_rows))\nfull_data=full_data.copy()\n\n# Shuffling the data before split in case there is some unknown sequential effect (maybe more DGA sites were collected at first)\nfull_data = sklearn.utils.shuffle(full_data)\ntrain_data=full_data[:split]\ntest_data=full_data[split:]\ntrain_data.to_csv('output/dga-dataset_train.csv')\ntest_data.to_csv('output/dga-dataset_test.csv')\n", "sub_path": "Example of Clean Shareable Code/ex01_DGA_part01.py", "file_name": "ex01_DGA_part01.py", "file_ext": "py", "file_size_in_byte": 3092, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "pandas.read_csv", "line_number": 24, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 64, "usage_type": "call"}, {"api_name": "sklearn.utils.utils.shuffle", "line_number": 79, "usage_type": "call"}, {"api_name": "sklearn.utils.utils", "line_number": 79, "usage_type": "attribute"}, {"api_name": "sklearn.utils", "line_number": 79, "usage_type": "name"}]} +{"seq_id": "132858629", "text": "\"\"\"\nDefinition of urls for Website.\n\"\"\"\n\nfrom django.urls import path, include\n\n# Uncomment the next two lines to enable the admin:\n# from django.contrib import admin\n# admin.autodiscover()\n\nurlpatterns = [\n\tpath('RandomHeroPicker/', include('RandomHeroPicker.urls')),\n \n]\n", "sub_path": "Website/Website/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 275, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "552235803", "text": "\"\"\"Tango Card SDK - Store Front Base.\"\"\"\n#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n# tangocard_store_example.py\n#\n# Copyright (c) 2013 Tango Card, Inc\n# All rights reserved.\n# \n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions: \n# \n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software. \n# \n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n# THE SOFTWARE.\n#\n# Python 3.3\n#\n# category TangoCard\n# package SDK\n# version Id: failure_response.py 2013-03-25 \n# copyright Copyright (c) 2013, Tango Card (http://www.tangocard.com)\n#\n\nimport sys\nimport configparser\nimport os.path\n\n# Path hack.\nsys.path.insert(0, os.path.abspath('..'))\nimport tangocardsdk\n\n# \n# Store Front example using Tango Card Python SDK.\n#\n\nclass TangoCardSdkExamplesBase:\n \"\"\"Tango Card SDK example Store Front Base class.\"\"\"\n \n CONFIG_INI_FILENAME = \"config/examples_config.ini\"\n \n __dict = None\n \n # \n # Constructor that prevents a default instance of this class from being created.\n #\n def __init__(self):\n try:\n filename = os.path.split(__file__)[0]\n filepath = os.path.join(filename, TangoCardSdkExamplesBase.CONFIG_INI_FILENAME)\n \n if not os.path.exists(filepath):\n raise ValueError(\"Example Configuration INI file invalid.\")\n \n config = configparser.ConfigParser()\n config.read(filepath)\n \n app_tango_card_service_api = config['TANGOCARD']['app_tango_card_service_api']\n \n self.__dict = {\n \n 'app_username' : str(config['TANGOCARD']['app_username']),\n 'app_password' : str(config['TANGOCARD']['app_password']),\n 'app_card_sku' : str(config['TANGOCARD']['app_card_sku']),\n 'app_card_value' : int(config['TANGOCARD']['app_card_value']),\n 'app_recipient_email' : str(config['TANGOCARD']['app_recipient_email']),\n \n 'app_tango_card_service_api' : app_tango_card_service_api, \n 'enum_tango_card_service_api' : tangocardsdk.enums.TangoCardServiceApiEnum.to_enum(app_tango_card_service_api)\n }\n\n except Exception as exc:\n sys.stderr.write(\"Exception ({})\".format(exc))\n raise\n \n @property\n def config(self):\n return self.__dict", "sub_path": "examples/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 3386, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "sys.path.insert", "line_number": 41, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.path.path.abspath", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 41, "usage_type": "name"}, {"api_name": "os.path.path.split", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 60, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 61, "usage_type": "name"}, {"api_name": "os.path.path.exists", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 63, "usage_type": "name"}, {"api_name": "configparser.ConfigParser", "line_number": 66, "usage_type": "call"}, {"api_name": "tangocardsdk.enums.TangoCardServiceApiEnum.to_enum", "line_number": 80, "usage_type": "call"}, {"api_name": "tangocardsdk.enums", "line_number": 80, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 84, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 84, "usage_type": "attribute"}]} +{"seq_id": "406028032", "text": "from flask import Flask, request, render_template, make_response, url_for\nfrom pexels import Pexels\nfrom random import randint\n\napp = Flask(__name__)\nparams = {\n\t\"per_page\": 30,\n\t\"page\": 1,\n}\ncurated_data = Pexels('curated')\n\napp.config.from_object('config.DevelopmentConfig')\n\ndef get_random_image():\n\treturn url_for('static', filename=f'img/cover-{randint(1,1)}.jpg')\n\n@app.route('/')\ndef index():\n\tbg_image_url = get_random_image()\n\t# image_url = \"https://images.pexels.com/photos/3597096/pexels-photo-3597096.jpeg?auto=compress&crop=focalpoint&cs=tinysrgb&fit=crop&fp-y=0.8&h=850.0&w=2600\"\n\t# image_url = curated_data.get_random_image()['src']['landscape']\n\treturn render_template('index.html', title='trending', bg_image_url=bg_image_url)\n\n\n@app.route('/curated')\ndef curated():\n\tpage = request.args.get('page', 1)\n\tresponse = curated_data.request_images(page)\n\treturn make_response({'photos': response['photos']})\n\n@app.route('/search/')\ndef search(query):\n\tprint(query)\n\tpage = request.args.get('page', 1)\n\tresponse = Pexels('search', query).request_images(page)\n\treturn make_response({'photos': response['photos']})\n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1138, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "flask.Flask", "line_number": 5, "usage_type": "call"}, {"api_name": "pexels.Pexels", "line_number": 10, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 15, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 34, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 34, "usage_type": "name"}, {"api_name": "pexels.Pexels", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "270810477", "text": "from pyautogui import *\nimport pyautogui\nimport time\nimport keyboard\nimport random\nimport win32api, win32con\nimport math\n\n#Top Left Corner of Paint.NET\n#X: 901 Y: 279 RGB: (237, 218, 255)\ntx = 901\nty = 279\nx = 1300\ny = 657\nR = 36\ndistance = 50\nmult = 3.14\noffsetx = 0\noffsety = 0\n\n#colors\nrx = 1824\nry = 338\n\nbx = 1763\nby = 273\n\ngx = 1825\ngy = 210\n\npx = 1884\npy = 268\n\ndef changeColor(x,y):\n win32api.SetCursorPos((x,y))\n win32api.mouse_event(win32con.MOUSEEVENTF_LEFTDOWN, x, y, 0, 0)\n time.sleep(0.005)\n win32api.mouse_event(win32con.MOUSEEVENTF_LEFTUP, x, y, 0, 0)\n\ndef drawInterior():\n global distance,mult\n while distance > 0 and keyboard.is_pressed('q') == False:\n pyautogui.drag(distance * mult, 0, duration=0.01)#right\n distance -= 2\n pyautogui.drag(0,distance* mult, duration=0.05)#down\n pyautogui.drag(-distance* mult, 0, duration=0.01)#left\n distance -= 2\n pyautogui.drag(0, -distance* mult, duration=0.01)#up\n\n\ndef loop():\n global distance,offsetx,offsety\n while offsetx < (156 * 4):\n if not distance > 0:\n win32api.mouse_event(win32con.MOUSEEVENTF_LEFTUP, x, y, 0, 0)\n offsetx += 156\n win32api.SetCursorPos((tx+offsetx,ty+offsety))\n distance = 50\n drawInterior()\n offsetx = 0\n offsety = 156\n win32api.mouse_event(win32con.MOUSEEVENTF_LEFTUP, x, y, 0, 0)\n win32api.SetCursorPos((tx,ty+offsety))\n win32api.mouse_event(win32con.MOUSEEVENTF_LEFTDOWN, x, y, 0, 0)\n print(ty-offsety)\n drawInterior()\n loop()\n \n \ntime.sleep(2)\nchangeColor(rx,ry)\nwin32api.SetCursorPos((tx,ty))\nwin32api.mouse_event(win32con.MOUSEEVENTF_LEFTDOWN, tx, ty, 0, 0)\n\ndrawInterior()\nloop()\n\n\n\n \n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1740, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "win32api.SetCursorPos", "line_number": 35, "usage_type": "call"}, {"api_name": "win32api.mouse_event", "line_number": 36, "usage_type": "call"}, {"api_name": "win32con.MOUSEEVENTF_LEFTDOWN", "line_number": 36, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 37, "usage_type": "call"}, {"api_name": "win32api.mouse_event", "line_number": 38, "usage_type": "call"}, {"api_name": "win32con.MOUSEEVENTF_LEFTUP", "line_number": 38, "usage_type": "attribute"}, {"api_name": "keyboard.is_pressed", "line_number": 42, "usage_type": "call"}, {"api_name": "pyautogui.drag", "line_number": 43, "usage_type": "call"}, {"api_name": "pyautogui.drag", "line_number": 45, "usage_type": "call"}, {"api_name": "pyautogui.drag", "line_number": 46, "usage_type": "call"}, {"api_name": "pyautogui.drag", "line_number": 48, "usage_type": "call"}, {"api_name": "win32api.mouse_event", "line_number": 55, "usage_type": "call"}, {"api_name": "win32con.MOUSEEVENTF_LEFTUP", "line_number": 55, "usage_type": "attribute"}, {"api_name": "win32api.SetCursorPos", "line_number": 57, "usage_type": "call"}, {"api_name": "win32api.mouse_event", "line_number": 62, "usage_type": "call"}, {"api_name": "win32con.MOUSEEVENTF_LEFTUP", "line_number": 62, "usage_type": "attribute"}, {"api_name": "win32api.SetCursorPos", "line_number": 63, "usage_type": "call"}, {"api_name": "win32api.mouse_event", "line_number": 64, "usage_type": "call"}, {"api_name": "win32con.MOUSEEVENTF_LEFTDOWN", "line_number": 64, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 70, "usage_type": "call"}, {"api_name": "win32api.SetCursorPos", "line_number": 72, "usage_type": "call"}, {"api_name": "win32api.mouse_event", "line_number": 73, "usage_type": "call"}, {"api_name": "win32con.MOUSEEVENTF_LEFTDOWN", "line_number": 73, "usage_type": "attribute"}]} +{"seq_id": "257266310", "text": "#!/usr/bin/env python\n\n# regex part inspired by the commit hook script:\n# https://github.com/pbetkier/add-issue-id-hook\n# needs jira-python https://github.com/pycontribs/jira\n# install via `pip install jira`\n# https://jira.readthedocs.io/en/master/api.html#issue\n\nimport subprocess\nimport re\nfrom jira import JIRA, JIRAError\nfrom datetime import datetime\n\n# point to your jira installation\njira_server = 'https://jira.yourdomain.com'\n# configure authentication to your needs, see jira module docs for more auth modes\njira = JIRA(server=(jira_server), auth=('changelogbot', 'cryp71cp455w0rd'))\n# configure your jira project or just leave it to find all\nproject_format = '[A-Z][A-Z\\d]+'\n# define jira projects to create version\nprojects = ['CORE', 'PAS']\n\n# configure possible issue types\nbugTypes = ['Bug', 'InstaBug']\nfeatureTypes = ['Story, Task']\nrefactoringTypes = ['Refactoring']\nignoredTypes = ['Sub-task']\n\n# if you building different types (alpha,beta,production) and \n# want to differ in the changelog, specify default here and/or\n# pass it as first argument\nbuildType = \"Release\"\nif len(sys.argv) > 1:\n buildType = sys.argv[1]\n\nchangelogFilename = \"CHANGELOG.md\"\n\n# generate markdown with hyperlinks\nrender_link = False\n\n# git log to find all changes since last tag (use on master only, only uses commit messages)\ngit_cmd = 'git log $(git describe --abbrev=0 --tag)..HEAD --format=\"%s\"'\n# if you want to print branch infos too use lightly different output\n# git_cmd = 'git log $(git describe --abbrev=0 --tag)..HEAD --oneline --decorate'\n\n# parse version this example uses a gradle property file\n# load_properties taken from:\n# https://stackoverflow.com/questions/3595363/properties-file-in-python-similar-to-java-properties#8220790\ndef load_properties(filepath, sep='=', comment_char='#'):\n \"\"\"\n Read the file passed as parameter as a properties file and return as dict\n \"\"\"\n props = {}\n with open(filepath, \"rt\") as f:\n for line in f:\n l = line.strip()\n if l and not l.startswith(comment_char):\n key_value = l.split(sep)\n key = key_value[0].strip()\n value = sep.join(key_value[1:]).strip().strip('\"')\n props[key] = value\n return props\n\ndef set_fixVersions(issue, version):\n fixVersions = []\n for existing_version in issue.fields.fixVersions:\n fixVersions.append({'name': existing_version.name})\n fixVersions.append({'name': version.name})\n try:\n issue.update(fields={'fixVersions': fixVersions})\n except JIRAError as e:\n print(e.status_code, e.text, issue.key)\n\ndef scan_for_tickets():\n issue_pattern = '{}-[\\d]+'.format(project_format)\n try:\n result = subprocess.check_output(git_cmd, shell=True)\n except subprocess.CalledProcessError as e:\n print(\"Calledprocerr\")\n for line in result.splitlines():\n issue_id_match = re.search(issue_pattern, line)\n if issue_id_match:\n found_issue_id = issue_id_match.group()\n issues.append(found_issue_id)\n return list(set(issues))\n\ndef render(issue):\n if(render_link):\n issue_url = jira_server + \"/browse/\" + issue.key\n issue_line = \" * [\" + issue.key + \"](\" + issue_url + \") \" + issue.fields.summary + \"\\n\"\n else:\n issue_line = \" * \" + issue.key + \" \" + issue.fields.summary + \"\\n\"\n return issue_line\n\nprops = load_properties('gradle.properties')\nrelease_version = props['versionMajor'] + '.' + props['versionMinor'] + '.' + props['versionPatch']\n\nfor project in projects:\n version_exists = False\n versions = jira.project_versions(project)\n for version in versions:\n if version.name == release_version:\n version_exists = True\n break\n\n if(version_exists):\n print('version ' + release_version + ' in project ' + project + ' exists - dont create one\\n')\n else:\n print('version ' + release_version + ' in project ' + project + ' not found - creating it!\\n')\n version = jira.create_version(release_version, project_version)\n\nissues = []\nadded = []\nbugs = []\n\nissues = scan_for_tickets()\nfor issueCode in issues:\n try:\n issue = jira.issue(issueCode)\n except JIRAError as e:\n print(issueCode + \"not found\")\n set_fixVersions(issue, version)\n if issue.fields.issuetype.name in bugTypes:\n bugs.append(issue)\n elif issue.fields.issuetype.name in ignoredTypes:\n # This issue is of a type that we want to ignore; continue with the next one.\n continue\n elif issue.fields.issuetype.name in featureTypes:\n added.append(issue)\n else:\n added.append(issue)\n\nchangelogHeading = \"## [\" + release_version + \"] \" + buildType + \" \" + props['buildNumber'] + \" - \" + datetime.today().strftime(\"%Y-%m-%d\") + \"\\n\"\nchangelog = \"\"\nif added:\n changelog += \"### Added\\n\"\n for issue in added:\n changelog += render(issue)\n changelog += \"\\n\"\nif bugs:\n changelog += \"### Fixed\\n\"\n for issue in bugs:\n changelog += render(issue)\n\nchangelog = changelog.encode('utf8', 'replace')\nprint(changelog)\n\nf = open(\"CHANGES.md\", \"w+\")\nf.write(changelog)\nf.close()\n\nchangelog += \"\\n\"\nf = open(changelogFilename, \"r\")\ncontents = f.readlines()\nf.close()\ncontents.insert(8, changelog)\ncontents.insert(8, changelogHeading)\nf = open(changelogFilename, \"w+\")\nf.writelines(contents)\nf.close()\n", "sub_path": "generate-changelog.py", "file_name": "generate-changelog.py", "file_ext": "py", "file_size_in_byte": 5391, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "jira.JIRA", "line_number": 17, "usage_type": "call"}, {"api_name": "jira.JIRAError", "line_number": 71, "usage_type": "name"}, {"api_name": "subprocess.check_output", "line_number": 77, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 78, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 81, "usage_type": "call"}, {"api_name": "jira.project_versions", "line_number": 100, "usage_type": "call"}, {"api_name": "jira.create_version", "line_number": 110, "usage_type": "call"}, {"api_name": "jira.issue", "line_number": 119, "usage_type": "call"}, {"api_name": "jira.JIRAError", "line_number": 120, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 133, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 133, "usage_type": "name"}]} +{"seq_id": "130681750", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport sys\nfrom setuptools import setup\n\nwith open('README.rst') as readme_file:\n readme = readme_file.read()\n\nwith open('HISTORY.rst') as history_file:\n history = history_file.read()\n\nrequirements = [\n 'Click>=6.0',\n 'passlib',\n 'six',\n 'argon2-cffi',\n]\n\nif sys.version_info[0]==2:\n requirements.append('cffi')\n\ntest_requirements = [\n 'pytest'\n]\n\nsetup(\n name='lunch_puppy',\n version='0.1.0',\n description=\"Lunch Puppy is yet another password vault.\",\n long_description=readme + '\\n\\n' + history,\n author=\"Terrel Shumway\",\n author_email='moan-o-storm-beware-spoke@shumway.us',\n url='https://github.com/bbiw/lunch_puppy',\n packages=[\n 'lunch_puppy',\n ],\n package_dir={'lunch_puppy':\n 'lunch_puppy'},\n entry_points={\n 'console_scripts': [\n 'lpup=lunch_puppy.cli:main'\n ]\n },\n include_package_data=True,\n install_requires=requirements,\n license=\"Apache Software License 2.0\",\n zip_safe=False,\n keywords='lunch_puppy',\n classifiers=[\n 'Development Status :: 2 - Pre-Alpha',\n 'Intended Audience :: Developers',\n 'License :: OSI Approved :: Apache Software License',\n 'Natural Language :: English',\n \"Programming Language :: Python :: 2\",\n 'Programming Language :: Python :: 2.7',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.3',\n 'Programming Language :: Python :: 3.4',\n 'Programming Language :: Python :: 3.5',\n ],\n test_suite='tests',\n tests_require=test_requirements\n)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1653, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "sys.version_info", "line_number": 19, "usage_type": "attribute"}, {"api_name": "setuptools.setup", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "306561666", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# SmallestService.py\n#\n# A sample demonstrating the smallest possible service written in Python.\n\"\"\"\n安装服务　　　　python PythonService.py install \n自动启动　　　　python PythonService.py --startup auto install \n启动服务　　　　python PythonService.py start \n重启服务　　　　python PythonService.py restart\n停止服务　　　　python PythonService.py stop\n删除/卸载服务　 python PythonService.py remove\n\"\"\"\nimport win32serviceutil\nimport win32service\nimport win32event\n\nclass SmallestPythonService(win32serviceutil.ServiceFramework):\n _svc_name_ = \"PythonServiceDemo1\"\n _svc_display_name_ = \"PythonServiceDemo1\"\n _svc_description_ = \"Python service demo1.\"\n\n def __init__(self, args):\n win32serviceutil.ServiceFramework.__init__(self, args)\n # Create an event which we will use to wait on.\n # The \"service stop\" request will set this event.\n self.hWaitStop = win32event.CreateEvent(None, 0, 0, None)\n self.logger = self._getLogger()\n self.isAlive = True\n\n def _getLogger(self):\n import logging\n import os\n import inspect\n import datetime\n\n logger = logging.getLogger('[PythonService1]')\n \n this_file = inspect.getfile(inspect.currentframe())\n dirpath = os.path.abspath(os.path.dirname(this_file))\n handler = logging.FileHandler(os.path.join(dirpath, \"log/%s-1.log\" % datetime.date.today().strftime('%Y%m%d')))\n \n formatter = logging.Formatter('%(asctime)s %(name)-12s %(levelname)-8s %(message)s')\n handler.setFormatter(formatter)\n\n logger.addHandler(handler)\n logger.setLevel(logging.INFO)\n\n return logger\n\n def SvcStop(self):\n \t# 先告诉SCM停止这个过程 \n self.logger.error(\"svc1 do stop....\")\n # Before we do anything, tell the SCM we are starting the stop process.\n self.ReportServiceStatus(win32service.SERVICE_STOP_PENDING)\n # And set my event.\n win32event.SetEvent(self.hWaitStop)\n self.isAlive = False\n\n def SvcDoRun(self):\n import time\n from eve import Eve \n self.logger.info(\"svc1 do run....\") \n while self.isAlive:\n self.logger.info(\"I am alive. demo1\")\n c1(self.logger)\n time.sleep(10)\n # We do nothing other than wait to be stopped!\n #win32event.WaitForSingleObject(self.hWaitStop, win32event.INFINITE)\n\nclass c1:\n def __init__(self, _logger):\n _logger.info(\"c1...\")\n\nif __name__=='__main__':\n win32serviceutil.HandleCommandLine(SmallestPythonService)", "sub_path": "py-winservice-1.py", "file_name": "py-winservice-1.py", "file_ext": "py", "file_size_in_byte": 2663, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "win32serviceutil.ServiceFramework", "line_number": 18, "usage_type": "attribute"}, {"api_name": "win32serviceutil.ServiceFramework.__init__", "line_number": 24, "usage_type": "call"}, {"api_name": "win32serviceutil.ServiceFramework", "line_number": 24, "usage_type": "attribute"}, {"api_name": "win32event.CreateEvent", "line_number": 27, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 37, "usage_type": "call"}, {"api_name": "inspect.getfile", "line_number": 39, "usage_type": "call"}, {"api_name": "inspect.currentframe", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 40, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "datetime.date.today", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 41, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 43, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 47, "usage_type": "attribute"}, {"api_name": "win32service.SERVICE_STOP_PENDING", "line_number": 55, "usage_type": "attribute"}, {"api_name": "win32event.SetEvent", "line_number": 57, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 67, "usage_type": "call"}, {"api_name": "win32serviceutil.HandleCommandLine", "line_number": 76, "usage_type": "call"}]} +{"seq_id": "598934724", "text": "import scrapy\nfrom ..static_data import get_provincies\nimport scrapy\nfrom ..items import RealEstate\nfrom scrapy.http.request import Request\nfrom scrapy.spiders import Spider\nfrom scrapy.item import Item\nfrom scrapy.selector import Selector\nfrom scrapy.loader import ItemLoader\n\nclass GetareasSpider(scrapy.Spider):\n name = 'getRealEstate'\n allowed_domains = ['https://www.idealista.com/']\n start_urls = get_provincies([\"venta-trasteros\", \"venta-garajes\"])\n\n\n def parse(self, response, region=\"\"):\n if (region == \"\"):\n tab = response.url.split(\"/\")\n region = tab[len(tab) - 2]\n selector = Selector(response)\n elements = selector.css(\"#sublocations li a\")\n sublocations = elements.xpath(\"@href\").extract()\n sublocation_names = elements.xpath(\"text()\").extract()\n if (len(sublocations) > 0):\n for i, sublocation in enumerate(sublocations):\n yield Request(url=(self.base_url + sublocation), callback=self.parse, cb_kwargs={'region':(region + \">\" + sublocation_names[i])}, meta={'proxy':'81.0.27.254:19999'})\n else:\n product_links = selector.css(\"a.item-link\").xpath(\"@href\").getall()\n for product_link in product_links:\n yield Request(url=(self.base_url + product_link),callback=self.parse_area_page,cb_kwargs={'region':region}, meta={'proxy':'81.0.27.254:19999'})\n next_button = selector.css(\"li.next > a\").xpath(\"@href\").get()\n if next_button:\n yield Request(url=(self.base_url + next_button), callback=self.parse_area_page, cb_kwargs={'region':region}, meta={'proxy':'81.0.27.254:19999'})\n \n def parse_area_page(self, response, region):\n selector = Selector(response)\n product_links = selector.css(\"a.item-link\").xpath(\"@href\").getall()\n for product_link in product_links:\n yield Request(url=(self.base_url + product_link), callback=self.parse_real_estate, cb_kwargs={'region':region}, meta={'proxy':'81.0.27.254:19999'})\n next_button = selector.css(\"li.next > a\").xpath(\"@href\").get()\n if next_button:\n yield Request(url=(self.base_url + next_button), callback=self.parse_area_page, cb_kwargs={'region':region}, meta={'proxy':'81.0.27.254:19999'})\n def parse_real_estate(self, response, region):\n selector = Selector(response)\n price = selector.css(\"info-data-price span.txt-bold\").xpath(\"text()\").get()\n size = selector.css(\"div.info-features>span\").xpath(\"text()\").get()\n floor = selector.css(\"div.info-features>span + span + span\").xpath(\"text()\").get()\n item = ItemLoader(RealEstate(), selector)\n item.add_value(\"uid\", get_uid_from_link(response.url))\n item.add_value(\"price\", price)\n item.add_value(\"size\", size)\n item.add_value(\"floor\", floor)\n item.add_value(\"region\", region)\n yield item.load_item()\n\ndef get_uid_from_link(url):\n tab = url.split(\"/\")\n return (tab[len(tab) - 2])\n", "sub_path": "CrawlIdealista/spiders/getRealEstate.py", "file_name": "getRealEstate.py", "file_ext": "py", "file_size_in_byte": 3027, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "scrapy.Spider", "line_number": 11, "usage_type": "attribute"}, {"api_name": "static_data.get_provincies", "line_number": 14, "usage_type": "call"}, {"api_name": "scrapy.selector.Selector", "line_number": 21, "usage_type": "call"}, {"api_name": "scrapy.http.request.Request", "line_number": 27, "usage_type": "call"}, {"api_name": "scrapy.http.request.Request", "line_number": 31, "usage_type": "call"}, {"api_name": "scrapy.http.request.Request", "line_number": 34, "usage_type": "call"}, {"api_name": "scrapy.selector.Selector", "line_number": 37, "usage_type": "call"}, {"api_name": "scrapy.http.request.Request", "line_number": 40, "usage_type": "call"}, {"api_name": "scrapy.http.request.Request", "line_number": 43, "usage_type": "call"}, {"api_name": "scrapy.selector.Selector", "line_number": 45, "usage_type": "call"}, {"api_name": "scrapy.loader.ItemLoader", "line_number": 49, "usage_type": "call"}, {"api_name": "items.RealEstate", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "478505495", "text": "import datetime\nfrom resnet18 import ResNet18\nfrom utils import *\nsetup_seed(6666)\n\n\ntrain_loader = load_data()\nval_loader = load_data(train=False, n_items=512)\nepoch_val_loader = load_data(train=False)\n\nnet = ResNet18(if_bn=False).to(device)\nprint(net)\n\ncriterion = torch.nn.CrossEntropyLoss()\noptimizer = torch.optim.SGD(net.parameters(), lr=0.1, momentum=0.9, weight_decay=0.0005)\nscheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=8, min_lr=1e-4)\n\nloss_list = []\ntrain_err = []\nval_err = []\n\n# max_val_accuracy = 0\n# PATH = f'./my-resnet-.pth'\nstart_time = datetime.datetime.now()\nfor epoch in range(33):\n acc = train_model(epoch, (train_loader, val_loader, epoch_val_loader), (loss_list, train_err, val_err), (net, criterion, optimizer))\n scheduler.step(acc)\nend_time = datetime.datetime.now()\n# torch.save(model.state_dict(), PATH)\nprint('Training time:%d' % (end_time - start_time).seconds)\n\ndraw_loss(loss_list)\ndraw_acc(train_err, val_err)\n\nILV, EFR = get_ILV_and_EFR(loss_list, train_err, val_err)\nprint(*ILV, sep='\\t')\nprint(*EFR, sep='\\t')\n", "sub_path": "output-no-BN/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1082, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "resnet18.ResNet18", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 24, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 28, "usage_type": "attribute"}]} +{"seq_id": "235799669", "text": "from django.urls import path\nfrom django.conf.urls.static import static\nfrom django.conf import settings\n\nfrom . import views\n\nurlpatterns = [\n path('', views.index, name='index'),\n path('categories_all/', views.categories_all, name='categories_all'),\n path('categories_one//', views.categories_one, name='categories_one'),\n path('skills_all/', views.skills_all, name='skills_all'),\n path('socials_all/', views.socials_all, name='socials_all'),\n path('projects_all/', views.projects_all, name='projects_all'),\n path('projects_one//', views.projects_one, name='projects_one'),\n path('blog_posts_all/', views.blog_posts_all, name='blog_posts_all'),\n path('blog_posts_one//', views.blog_posts_one, name='blog_posts_one'),\n path('blog_posts_by_tag//', views.blog_posts_by_tag, name='blog_posts_by_tag'),\n path('email/', views.email, name='email'),\n] + static(settings.STATIC_URL, document_root=settings.STATIC_ROOT) + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT) \n", "sub_path": "portfolio/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1042, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.conf.urls.static.static", "line_number": 19, "usage_type": "call"}, {"api_name": "django.conf.settings.STATIC_URL", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 19, "usage_type": "name"}, {"api_name": "django.conf.settings.STATIC_ROOT", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django.conf.settings.MEDIA_URL", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 19, "usage_type": "attribute"}]} +{"seq_id": "239995631", "text": "#!/usr/bin/env python\n\nimport logging\nlogging.basicConfig(level=logging.WARNING)\nimport sys\nimport argparse\nimport os\nimport yaml\nfrom klogger import klogger, klog_levels\n\ndef main():\n \"\"\"Rebuilds maps on broken regions.\"\"\"\n parser = argparse.ArgumentParser(description='Prepares downloaded regions for building.')\n parser.add_argument('--name', required=True, type=str, help='name of region')\n parser.add_argument('--disable-opencl', action='store_false', dest='doOCL', default=True, help='disable OpenCL code')\n parser.add_argument('--single', action='store_true', help='enable single-threaded mode for debugging or profiling')\n parser.add_argument(\"-v\", \"--verbosity\", action=\"count\", \\\n help=\"increase output verbosity\")\n parser.add_argument(\"-q\", \"--quiet\", action=\"store_true\", \\\n help=\"suppress informational output\")\n args = parser.parse_args()\n\n # set up logging\n log_level = klog_levels.LOG_INFO\n if args.quiet:\n log_level = klog_levels.LOG_ERROR\n if args.verbosity:\n # v=1 is DEBUG 1, v=2 is DEBUG 2, and so on\n log_level += args.verbosity\n log = klogger(log_level)\n\n log.log_info(\"Preparing region %s...\" % args.name)\n yamlfile = file(os.path.join('Regions', args.name, 'Region.yaml'))\n myRegion = yaml.load(yamlfile)\n yamlfile.close()\n\n myRegion.log = log\n myRegion.buildmap(args.doOCL, args.single)\n\nif __name__ == '__main__':\n sys.exit(main())\n", "sub_path": "PrepRegion.py", "file_name": "PrepRegion.py", "file_ext": "py", "file_size_in_byte": 1486, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.basicConfig", "line_number": 4, "usage_type": "call"}, {"api_name": "logging.WARNING", "line_number": 4, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 13, "usage_type": "call"}, {"api_name": "klogger.klog_levels.LOG_INFO", "line_number": 24, "usage_type": "attribute"}, {"api_name": "klogger.klog_levels", "line_number": 24, "usage_type": "name"}, {"api_name": "klogger.klog_levels.LOG_ERROR", "line_number": 26, "usage_type": "attribute"}, {"api_name": "klogger.klog_levels", "line_number": 26, "usage_type": "name"}, {"api_name": "klogger.klogger", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "yaml.load", "line_number": 34, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "34081452", "text": "# -*- coding: utf-8 -*-\n\nimport pyaudio as pa\nimport pickle as pl\nfrom copy import *\n\nclass RoxGlobal(object):\n def __init__(self):\n self.settings = {};\n self.trackTypes = {}\n self.projects = [];\n self.steps = [];\n self.audio = pa.PyAudio();\n self.devnum = self.audio.get_device_count();\n self.settings[\"core.outdev\"] = self.audio.get_default_output_device_info();\n self.settings[\"core.samprate\"] = self.settings[\"core.outdev\"][\"defaultSampleRate\"];\n self.settings[\"core.latency\"] = 0.1;\n self.settings[\"core.noteReso\"] = 960;\n self.settings[\"core.prelength\"] = 960 * 16;\n self.primaryEngine = \"core.ruce\";\n self.engines = {};\n self.conmodes = {};\n self.project_class = None;\n self.playLock = 0;\n self.playing = 0;\n self.playStream = 0;\n \n def dump(self, fn, proj):\n proj.glob = None;\n f = open(fn, \"wb\");\n pl.dump(proj, f);\n proj.fn = fn;\n proj.glob = self;\n \n def load(self, fn):\n f = open(fn, \"rb\");\n p = pl.load(f);\n self.projects.append(p);\n self.steps.append(([], []));\n p.fn = fn;\n p.glob = self;\n p.rebuildUnpackable();\n return p;\n \n def createStep(self, proj):\n i = self.projects.index(proj);\n self.steps[i][1][:] = [];\n self.steps[i][0].append(deepcopy(proj));\n \n def undoStep(self, proj):\n i = self.projects.index(proj);\n if(self.steps[i][0]):\n self.steps[i][1].append(proj);\n op = self.steps[i][0][-1];\n self.steps[i][0].pop();\n self.projects[i] = op;\n return op;\n \n def redoStep(self, proj):\n i = self.projects.index(proj);\n if(self.steps[i][1]):\n self.steps[i][0].append(proj);\n np = self.steps[i][1][-1];\n self.steps[i][1].pop();\n self.projects[i] = op;\n return op;\n \n def openStream(self, proj):\n stream = self.audio.open(format=pa.paFloat32,\n channels = 1,\n rate = int(self.settings[\"core.samprate\"]),\n output = True,\n stream_callback = proj.getData);\n self.playStream = stream;\n \n def closeStream(self):\n self.playStream.stop_stream();\n self.playStream.close();\n self.playStream = 0;\n \n def setProjectClass(self, class_type):\n self.project_class = class_type;\n \n def getDevices(self):\n self.devnum = self.audio.get_device_count();\n for x in range(self.devnum):\n yield self.audio.get_device_info_by_index(x);\n \n def newProject(self, name):\n if(self.project_class):\n p = self.project_class(name, self.settings[\"core.samprate\"], self.audio, self);\n self.projects.append(p);\n self.steps.append(([], []));\n return p;\n else:\n print(\"Project class is not specifed.\");\n \n def registerMode(self, name, func_type):\n if(name in self.conmodes): raise NameError('ControlPoint Mode %s is already exists!' % (name));\n self.conmodes[name] = func_type;\n \n def unregisterMode(self, name):\n del self.conmodes[name];\n \n def registerEngine(self, name, class_type):\n if(name in self.engines): raise NameError('EngineType %s is already exists!' % (name));\n self.engines[name] = class_type;\n \n def unregisterEngine(self, name):\n del self.engines[name];\n \n def removeProject(self, name):\n for i in range(len(self.projects)):\n if(self.projects[i].name == name):\n del self.steps[i];\n del self.projects[i];\n return;\n \n def registerTrack(self, name, class_type):\n if(name in self.trackTypes): raise NameError('TrackType %s is already exists!' % (name));\n self.trackTypes[name] = class_type;\n \n def unregisterTrack(self, name):\n del self.trackTypes[Name];\n \nglobal G;\nG = RoxGlobal();\n", "sub_path": "core/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4107, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pyaudio.PyAudio", "line_number": 13, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 31, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 37, "usage_type": "call"}, {"api_name": "pyaudio.paFloat32", "line_number": 69, "usage_type": "attribute"}]} +{"seq_id": "369022029", "text": "import random\n\nimport scramble # @UnusedImport\n\nfrom OpenGL import GL\n\nfrom cgev.ui.QtKit import GLWidget\nfrom cgev.ui.Qt import QtCore\n\nfrom cgev.sbra.ui import GraphLayout, TreeConfig\n\n\nclass Node(object):\n def __init__(self, idx, pos, sz):\n self.idx = idx\n self.pos = pos\n self.sz = sz\n self.parents = list()\n self.linked = list()\n\n @property\n def center(self):\n center = [0, 0]\n center[0] = self.pos[0] + self.sz[0] / 2.0\n center[1] = self.pos[1] + self.sz[1] / 2.0\n return center\n\n def drawShape(self, margin=0.0):\n t = [self.pos[0] + margin,\n self.pos[1] + margin,\n self.pos[0] + self.sz[0] - margin,\n self.pos[1] + self.sz[1] - margin]\n\n GL.glVertex3f(t[0], t[1], 0)\n GL.glVertex3f(t[2], t[1], 0)\n GL.glVertex3f(t[2], t[3], 0)\n GL.glVertex3f(t[0], t[3], 0)\n\n def drawLinks(self):\n sc = self.center\n\n for node in self.linked:\n nc = node.center\n GL.glVertex3f(sc[0], sc[1], 0)\n GL.glVertex3f(nc[0], nc[1], 0)\n\n def childTree(self):\n if not self.linked:\n return list()\n\n children = self.linked\n\n for node in self.linked:\n children.extend(node.childTree())\n\n return children\n\n\nclass GLGraph(GLWidget):\n def __init__(self, num_nodes, size, ortho=False, parent=None):\n self.nodes = list()\n self.byidx = dict()\n\n super(GLGraph, self).__init__(size, ortho=ortho, parent=parent)\n\n self.generateNodes(num_nodes)\n\n def generateNodes(self, num_nodes):\n sz = [0.2, 0.1]\n idx = 0\n\n while num_nodes > 0:\n num_nodes -= 1\n idx += 1\n x = random.random() * 2.0 - 1.0\n y = random.random() * 2.0 - 1.0\n\n node = Node(idx, [x, y], sz)\n\n self.nodes.append(node)\n self.byidx[node.idx] = node\n\n remain = list(self.nodes)\n\n while len(remain) > 1:\n src = remain.pop(random.randint(0, len(remain) - 1))\n\n if not random.randint(0, 42) > 2:\n continue\n\n done = list()\n num_links = random.randint(1, min(len(remain), 3))\n while num_links > 0:\n dst = remain[random.randint(0, len(remain) - 1)]\n if src in dst.childTree() or dst in done:\n continue\n\n num_links -= 1\n done.append(dst)\n src.linked.append(dst)\n dst.parents.append(src)\n\n def draw(self):\n GL.glBegin(GL.GL_QUADS)\n self.qglColor(QtCore.Qt.blue)\n for node in self.nodes:\n node.drawShape()\n self.qglColor(QtCore.Qt.yellow)\n for node in self.nodes:\n node.drawShape(0.1)\n GL.glEnd()\n\n self.qglColor(QtCore.Qt.white)\n\n GL.glBegin(GL.GL_LINES)\n for node in self.nodes:\n node.drawLinks()\n GL.glEnd()\n\n def applyPositions(self, positions):\n for idx, pos in positions.iteritems():\n node = self.byidx[idx]\n node.pos = [-pos[1], pos[0]]\n\n self.update()\n\n def updateLayout(self):\n cfg = TreeConfig()\n cfg.parent_attr = \"parents\"\n cfg.child_attr = \"linked\"\n cfg.id_attr = \"idx\"\n\n gl = GraphLayout(self.nodes, cfg)\n for layout in gl.layouts:\n self.applyPositions(gl.fetchPositions(layout))\n", "sub_path": "test/gl/GLGraph.py", "file_name": "GLGraph.py", "file_ext": "py", "file_size_in_byte": 3485, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "OpenGL.GL.glVertex3f", "line_number": 34, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 34, "usage_type": "name"}, {"api_name": "OpenGL.GL.glVertex3f", "line_number": 35, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 35, "usage_type": "name"}, {"api_name": "OpenGL.GL.glVertex3f", "line_number": 36, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 36, "usage_type": "name"}, {"api_name": "OpenGL.GL.glVertex3f", "line_number": 37, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 37, "usage_type": "name"}, {"api_name": "OpenGL.GL.glVertex3f", "line_number": 44, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 44, "usage_type": "name"}, {"api_name": "OpenGL.GL.glVertex3f", "line_number": 45, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 45, "usage_type": "name"}, {"api_name": "cgev.ui.QtKit.GLWidget", "line_number": 59, "usage_type": "name"}, {"api_name": "random.random", "line_number": 75, "usage_type": "call"}, {"api_name": "random.random", "line_number": 76, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 86, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 88, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 92, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 94, "usage_type": "call"}, {"api_name": "OpenGL.GL.glBegin", "line_number": 104, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 104, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_QUADS", "line_number": 104, "usage_type": "attribute"}, {"api_name": "cgev.ui.Qt.QtCore.Qt", "line_number": 105, "usage_type": "attribute"}, {"api_name": "cgev.ui.Qt.QtCore", "line_number": 105, "usage_type": "name"}, {"api_name": "cgev.ui.Qt.QtCore.Qt", "line_number": 108, "usage_type": "attribute"}, {"api_name": "cgev.ui.Qt.QtCore", "line_number": 108, "usage_type": "name"}, {"api_name": "OpenGL.GL.glEnd", "line_number": 111, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 111, "usage_type": "name"}, {"api_name": "cgev.ui.Qt.QtCore.Qt", "line_number": 113, "usage_type": "attribute"}, {"api_name": "cgev.ui.Qt.QtCore", "line_number": 113, "usage_type": "name"}, {"api_name": "OpenGL.GL.glBegin", "line_number": 115, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 115, "usage_type": "name"}, {"api_name": "OpenGL.GL.GL_LINES", "line_number": 115, "usage_type": "attribute"}, {"api_name": "OpenGL.GL.glEnd", "line_number": 118, "usage_type": "call"}, {"api_name": "OpenGL.GL", "line_number": 118, "usage_type": "name"}, {"api_name": "cgev.sbra.ui.TreeConfig", "line_number": 128, "usage_type": "call"}, {"api_name": "cgev.sbra.ui.GraphLayout", "line_number": 133, "usage_type": "call"}]} +{"seq_id": "444954236", "text": "import logging\r\nimport hashlib\r\nimport hmac\r\nimport os\r\nimport azure.functions as func\r\nimport json\r\nimport re\r\nimport base64\r\nimport requests\r\nimport datetime\r\n\r\nTheHiveBearerToken = os.environ['TheHiveBearerToken']\r\ncustomer_id = os.environ['WorkspaceID']\r\nshared_key = os.environ['WorkspaceKey']\r\nlogAnalyticsUri = os.environ.get('logAnalyticsUri')\r\nlog_type = 'TheHive'\r\n\r\nif ((logAnalyticsUri in (None, '') or str(logAnalyticsUri).isspace())):\r\n logAnalyticsUri = 'https://' + customer_id + '.ods.opinsights.azure.com'\r\npattern = r'https:\\/\\/([\\w\\-]+)\\.ods\\.opinsights\\.azure.([a-zA-Z\\.]+)$'\r\nmatch = re.match(pattern,str(logAnalyticsUri))\r\nif(not match):\r\n raise Exception(\"TheHive Data Connector: Invalid Log Analytics Uri.\")\r\n\r\ndef build_signature(customer_id, shared_key, date, content_length, method, content_type, resource):\r\n x_headers = 'x-ms-date:' + date\r\n string_to_hash = method + \"\\n\" + str(content_length) + \"\\n\" + content_type + \"\\n\" + x_headers + \"\\n\" + resource\r\n bytes_to_hash = bytes(string_to_hash, encoding=\"utf-8\")\r\n decoded_key = base64.b64decode(shared_key)\r\n encoded_hash = base64.b64encode(hmac.new(decoded_key, bytes_to_hash, digestmod=hashlib.sha256).digest()).decode()\r\n authorization = \"SharedKey {}:{}\".format(customer_id,encoded_hash)\r\n return authorization\r\n\r\ndef post_data_to_sentinel(body):\r\n method = 'POST'\r\n content_type = 'application/json'\r\n resource = '/api/logs'\r\n rfc1123date = datetime.datetime.utcnow().strftime('%a, %d %b %Y %H:%M:%S GMT')\r\n content_length = len(body)\r\n signature = build_signature(customer_id, shared_key, rfc1123date, content_length, method, content_type, resource)\r\n uri = logAnalyticsUri + resource + '?api-version=2016-04-01'\r\n headers = {\r\n 'content-type': content_type,\r\n 'Authorization': signature,\r\n 'Log-Type': log_type,\r\n 'x-ms-date': rfc1123date\r\n }\r\n response = requests.post(uri,data=body, headers=headers)\r\n if (response.status_code >= 200 and response.status_code <= 299):\r\n logging.info(\"TheHive event successfully processed to the Azure Sentinel.\")\r\n return response.status_code\r\n else:\r\n logging.warn(\"Event is not processed into Azure. Response code: {}\".format(response.status_code))\r\n return None\r\n\r\ndef main(req: func.HttpRequest) -> func.HttpResponse:\r\n logging.info('Python HTTP trigger function processed a request. Start of processing.')\r\n method = req.method\r\n params = req.params\r\n if method == 'POST':\r\n if 'Authorization' in req.headers:\r\n bearer_string = re.match(\"^Bearer\\s+(.*)\", req.headers['Authorization'])\r\n if bearer_string:\r\n bearer_token = bearer_string.group(1)\r\n if bearer_token == TheHiveBearerToken:\r\n post_req_data = req.get_body()\r\n message = post_req_data.decode('utf-8')\r\n logging.info(\"200 OK HTTPS\")\r\n try:\r\n message = json.loads(message)\r\n post_data_to_sentinel(json.dumps(message))\r\n except Exception as err:\r\n logging.error(f\"Is not valid Message format. Error: {err}. Message: {message}.\")\r\n return func.HttpResponse(\"200 OK HTTPS\", status_code=200)\r\n else:\r\n logging.error(\"Unauthorized, Bearer token is not right. Error code: 401.\")\r\n return func.HttpResponse(\"Unauthorized, Bearer token is not right.\", status_code=401)\r\n else:\r\n logging.error(\"Unauthorized, Bearer token is not present in the request. Error code: 401.\")\r\n return func.HttpResponse(\"Unauthorized, Bearer token is not present in the request.\", status_code=401)\r\n else:\r\n logging.error(\"Unauthorized, Bearer token is not right. Error code: 401.\")\r\n return func.HttpResponse(\"Unauthorized, Bearer token is not present in the request.\", status_code=401)\r\n logging.error(\"HTTP method not supported. Error code: 405.\")\r\n return func.HttpResponse(\"HTTP method not supported\", status_code=405)", "sub_path": "Solutions/TheHive/Data Connectors/TheHiveWebhooksTrigger/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 4180, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.environ", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 15, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 15, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 21, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 29, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 30, "usage_type": "call"}, {"api_name": "hmac.new", "line_number": 30, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 30, "usage_type": "attribute"}, {"api_name": "datetime.datetime.utcnow", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 38, "usage_type": "attribute"}, {"api_name": "requests.post", "line_number": 48, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 50, "usage_type": "call"}, {"api_name": "logging.warn", "line_number": 53, "usage_type": "call"}, {"api_name": "azure.functions.HttpRequest", "line_number": 56, "usage_type": "attribute"}, {"api_name": "azure.functions", "line_number": 56, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 57, "usage_type": "call"}, {"api_name": "re.match", "line_number": 62, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 68, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 70, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 71, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 73, "usage_type": "call"}, {"api_name": "azure.functions.HttpResponse", "line_number": 74, "usage_type": "call"}, {"api_name": "azure.functions", "line_number": 74, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 76, "usage_type": "call"}, {"api_name": "azure.functions.HttpResponse", "line_number": 77, "usage_type": "call"}, {"api_name": "azure.functions", "line_number": 77, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 79, "usage_type": "call"}, {"api_name": "azure.functions.HttpResponse", "line_number": 80, "usage_type": "call"}, {"api_name": "azure.functions", "line_number": 80, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 82, "usage_type": "call"}, {"api_name": "azure.functions.HttpResponse", "line_number": 83, "usage_type": "call"}, {"api_name": "azure.functions", "line_number": 83, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 84, "usage_type": "call"}, {"api_name": "azure.functions.HttpResponse", "line_number": 85, "usage_type": "call"}, {"api_name": "azure.functions", "line_number": 85, "usage_type": "name"}, {"api_name": "azure.functions.HttpResponse", "line_number": 56, "usage_type": "attribute"}]} +{"seq_id": "124243351", "text": "import pymongo\r\nimport requests\r\nimport re\r\nimport math\r\nimport time\r\nimport redis\r\nfrom bs4 import BeautifulSoup\r\nfrom lxml import etree\r\nfrom selenium import webdriver\r\n\r\n\r\ndef get_db_client():\r\n client = pymongo.MongoClient(\r\n 'mongodb://MZsa:MZdba@192.168.1.121:27000,192.168.1.222:27000,192.168.1.230:27000/?readPreference=secondary')\r\n return client\r\n\r\n\r\ndef get_redis():\r\n return redis.Redis(host='192.168.1.121', port=6379, db=9, decode_responses=True)\r\n\r\n\r\nsession = requests.session()\r\n\r\ncache = get_redis()\r\nAjaxHost = 'http://www.zyfgj.org/spf/scspf.aspx'\r\nsize = 15\r\nDB_Name = \"LouPan\"\r\nCity = 'YanTai'\r\nTable_Basic_Name = f\"{City}_Project_Basic\" # 项目表名\r\nTable_Detail_Name = f\"{City}_Project_Detail\" # 项目信息\r\nTable_House_Name = f\"{City}_House\"\r\nTable_House_Basic = f\"{City}_House_Baisc\"\r\nTable_Build_Name = f\"{City}_Build_Baisc\"\r\nCache_HouseId = f\"{City}_HouseId\"\r\nCache_HouseUrl = f\"{City}_HouseUrl\"\r\nDB = get_db_client()[DB_Name]\r\ntimeout = 10\r\nTotal_Page = 1\r\nheaders = {\r\n 'content-type': \"application/x-www-form-urlencoded\",\r\n 'cache-control': \"no-cache\",\r\n 'postman-token': \"4c1044de-299b-c516-ec7f-354bcd9d155b\"\r\n}\r\nsetting = {'need_page': True}\r\nneed_page = True\r\nProjectList_Url = 'http://www.ytfcjy.com/public/project/ProjectList.aspx'\r\nInfo = {}\r\nInfo['txtPrjName'] = ''\r\nInfo['txtYsxkz'] = ''\r\nInfo['txtKfsName'] = ''\r\nInfo['txtPrjAdress'] = ''\r\nInfo['PageNavigator1$txtNewPageIndex'] = ''\r\nInfo['__EVENTTARGET'] = 'PageNavigator1$LnkBtnNext'\r\nInfo['__EVENTARGUMENT'] = ''\r\n\r\nXKZ_info = {\"sgxkzInfo\": 'http://www.ytfcjy.com/public/project/sgxkzInfo.aspx?code=',#施工许可证\r\n 'tdzInfo': \"http://www.ytfcjy.com/public/project/tdzInfo.aspx?code=\",#土地证\r\n 'presellCertInfo': 'http://www.ytfcjy.com/public/project/presellCertInfo.aspx?code=',#预售许可证\r\n 'jsydghxkzInfo':\"http://www.ytfcjy.com/public/project/ydghxkzInfo.aspx?code=\",#用地规划许可证\r\n 'ghxkzInfo':'http://www.ytfcjy.com/public/project/jsgcghxkzInfo.aspx?code=',#建设工程规划许可证\r\n }\r\n\r\n\r\ndef get_project_at_page(page):\r\n \"\"\"\r\n 获取项目列表\r\n \"\"\"\r\n if page == 1:\r\n r = requests.get(ProjectList_Url)\r\n r.encoding = 'gb2312'\r\n html = r.text\r\n bs = BeautifulSoup(html, 'lxml')\r\n if setting['need_page']:\r\n global Total_Page\r\n Total_Page = parse_page_count(bs)\r\n parse_page(bs, page)\r\n else:\r\n r = requests.post(ProjectList_Url, data=Info)\r\n r.encoding = 'gb2312'\r\n html = r.text\r\n bs = BeautifulSoup(html, 'lxml')\r\n parse_page(bs, page)\r\n pass\r\n\r\n\r\ndef parse_current_page(bs):\r\n page = bs.select_one('#PageNavigator1_LblPageIndex')\r\n return int(page.text)\r\n\r\n\r\ndef parse_page_count(bs):\r\n total = bs.select_one('#PageNavigator1_LblPageCount')\r\n return int(total.text)\r\n\r\n\r\ndef parse_page(bs, page):\r\n parse_info(bs)\r\n current = parse_current_page(bs)\r\n if current == page:\r\n print('=====')\r\n else:\r\n exit()\r\n thead = bs.select_one('tr.ListTop')\r\n if thead:\r\n keys = [t.text.strip() for t in thead.select('td')[1:]]\r\n tbody = bs.select('tr.TR_BG_list')\r\n for tr in tbody:\r\n tds = tr.select('td')[1:]\r\n values = [t.text.strip() for t in tds]\r\n href = tds[0].select_one('a')['href']\r\n pid = href.split('=')[1]\r\n data = dict(zip(keys, values))\r\n data['id'] = pid\r\n data['href'] = href\r\n save(data, Table_Basic_Name)\r\n print(f'page {page} done')\r\n\r\n\r\ndef parse_info(bs):\r\n __EVENTVALIDATION = bs.select_one('#__EVENTVALIDATION')\r\n if __EVENTVALIDATION:\r\n Info['__EVENTVALIDATION'] = __EVENTVALIDATION['value']\r\n\r\n __VIEWSTATE = bs.select_one('#__VIEWSTATE')\r\n if __VIEWSTATE:\r\n Info['__VIEWSTATE'] = __VIEWSTATE['value']\r\n\r\n pass\r\n\r\n\r\ndef save(data, table):\r\n t = int(time.time() * 1000)\r\n data[\"time\"] = t\r\n query = {\"id\": data[\"id\"]}\r\n if DB[table].count(query) > 0:\r\n DB[table].replace_one(query, data)\r\n else:\r\n DB[table].insert_one(data)\r\n return True\r\n\r\n\r\ndef get_all_project():\r\n '''\r\n 遍历获取\r\n '''\r\n start = 1\r\n while start <= Total_Page:\r\n get_project_at_page(start)\r\n start = start + 1\r\n print(f\"total page {Total_Page} done\")\r\n\r\n\r\ndef get_project_info(pid):\r\n \"\"\"\r\n 获取项目信息\r\n \"\"\"\r\n params = {\"code\": pid}\r\n url = \"http://www.ytfcjy.com/public/project/ProjectInfo.aspx\"\r\n r = requests.get(url, params=params)\r\n r.encoding = 'gb2312'\r\n html = r.text\r\n bs = BeautifulSoup(html, 'lxml')\r\n parse_info(bs)\r\n jbxx = bs.select_one('#infotable_jbxx') # 基本信息\r\n if jbxx:\r\n table = jbxx.find('table', attrs={'cellspacing': '1'})\r\n if table:\r\n titles = table.select('td.Infotitle')\r\n infoxxs = table.select('td.Infoxx')\r\n keys = [td.text.strip() for td in titles]\r\n values = [td.text.strip() for td in infoxxs]\r\n data = dict(zip(keys, values))\r\n data['id'] = pid\r\n\r\n infotable_build = bs.select_one('#infotable_build')\r\n if infotable_build:\r\n tables = infotable_build.select('table')\r\n if len(tables) == 2:\r\n table = tables[1]\r\n arr = []\r\n trs = table.select('tr')\r\n lltr = len(trs)\r\n if lltr>0:\r\n name = ''\r\n for i in range(lltr):\r\n tds = trs[i].select('td.Infotitle')\r\n if tds:#name\r\n name = trs[i].text.strip()\r\n else:\r\n tds = trs[i].select('td')\r\n for td in tds:\r\n buildname = td.text.strip()\r\n buildid = td.select_one('input')['bid']\r\n d = {'buildId':buildid,'buildName':buildname,'presell':name}\r\n arr.append(d)\r\n data['buildInfo'] = arr\r\n else:\r\n data['build'] = 0\r\n PROJECT_KFQY_CODE = bs.find(\r\n 'input', attrs={'id': 'PROJECT_KFQY_CODE', 'value': True})\r\n if PROJECT_KFQY_CODE:\r\n data['PROJECT_KFQY_CODE'] = PROJECT_KFQY_CODE[\"value\"]\r\n # 预售许可证\r\n presellInfo = bs.find(\r\n 'input', attrs={'id': 'presellInfo', 'value': True})\r\n if presellInfo:\r\n data['presellInfo'] = presellInfo[\"value\"]\r\n # 幢信息\r\n buildInfo = bs.find(\r\n 'input', attrs={'id': 'buildInfo', 'value': True})\r\n if buildInfo:\r\n data['buildInfo'] = buildInfo[\"value\"]\r\n # 土地证\r\n tdzInfo = bs.find('input', attrs={'id': 'tdzInfo', 'value': True})\r\n if tdzInfo:\r\n data['tdzInfo'] = tdzInfo[\"value\"]\r\n # 施工许可证\r\n sgxkzInfo = bs.find(\r\n 'input', attrs={'id': 'sgxkzInfo', 'value': True})\r\n if sgxkzInfo:\r\n data['sgxkzInfo'] = sgxkzInfo[\"value\"]\r\n # 用地规划许可证\r\n jsydghxkzInfo = bs.find(\r\n 'input', attrs={'id': 'jsydghxkzInfo', 'value': True})\r\n if jsydghxkzInfo:\r\n data['jsydghxkzInfo'] = jsydghxkzInfo[\"value\"]\r\n # 工程规划许可证\r\n ghxkzInfo = bs.find(\r\n 'input', attrs={'id': 'ghxkzInfo', 'value': True})\r\n if ghxkzInfo:\r\n data['ghxkzInfo'] = ghxkzInfo[\"value\"]\r\n save(data, Table_Detail_Name)\r\n set_done(Table_Basic_Name, pid, 'detail', 1)\r\n\r\ndef get_build_info(pid):\r\n \"\"\"\r\n 获取项目信息\r\n \"\"\"\r\n params = {\"code\": pid}\r\n url = \"http://www.ytfcjy.com/public/project/ProjectInfo.aspx\"\r\n r = requests.get(url, params=params)\r\n r.encoding = 'gb2312'\r\n html = r.text\r\n bs = BeautifulSoup(html, 'lxml')\r\n infotable_build = bs.select_one('#infotable_build')\r\n if infotable_build:\r\n tables = infotable_build.select('table')\r\n if len(tables) == 2:\r\n table = tables[1]\r\n data = []\r\n trs = table.select('tr')\r\n lltr = len(trs)\r\n if lltr>0:\r\n name = ''\r\n for i in range(lltr):\r\n tds = trs[i].select('td.Infotitle')\r\n if tds:#name\r\n name = trs[i].text.strip()\r\n else:\r\n tds = trs[i].select('td')\r\n for td in tds:\r\n buildname = td.text.strip()\r\n buildid = td.select_one('input')['bid']\r\n d = {'buildId':buildid,'buildName':buildname,'presell':name}\r\n data.append(d)\r\n DB[Table_Detail_Name].update_one({\"id\": pid}, {\"$set\": {'buildInfoData': data}})\r\n else:\r\n DB[Table_Detail_Name].update_one({\"id\": pid}, {\"$set\": {'build':0}})\r\n\r\n\r\ndef get_house_info(pid,bid,bname,presell):\r\n payload = f\"\\r\\n\\r\\n{bid}\\r\\n1\\r\\n1\\r\\n80\\r\\n720\\r\\ng_oBuildTable\\r\\n 1=1\\r\\n\\r\\n\"\r\n querystring = {\"req\":int(time.time()*1000)}\r\n r = requests.post('http://www.ytfcjy.com/Common/Agents/ExeFunCommon.aspx',data=payload,params = querystring)\r\n r.encoding = 'gb2312'\r\n bs = BeautifulSoup(r.text,'lxml')\r\n table = bs.select_one('#buildTable'+str(bid))\r\n if table:\r\n us = table.find_all('u',attrs={'style':'cursor:pointer'})\r\n for u in us:\r\n title = u['title'].replace(' 平方米','').replace(' 元','')\r\n #g_oBuildTable.clickRoom('679663')\r\n onclick = u['onclick']\r\n houseId = re.search(r'(\\d+)',onclick).group(1)\r\n infos = title.split()\r\n name = u.text.strip()\r\n data = {\"id\":houseId,'name':name,'projId':pid,'buildId':bid,'buildName':bname,'presell':presell}\r\n for info in infos:\r\n arr = info.split('：')\r\n if len(arr)==2:\r\n k,v = arr[0],arr[1]\r\n data[k.strip()] = v.strip()\r\n save(data,Table_House_Basic)\r\n pass\r\n\r\ndef get_house_by_buildInfo(pid,buildInfo):\r\n for build in buildInfo:\r\n get_house_info(pid,build['buildId'],build['buildName'],build['presell'])\r\n\r\ndef get_all_house():\r\n while True:\r\n ids = list(DB[Table_Detail_Name].find({'buildInfoData': {\"$exists\": True}, 'build': {\"$exists\": False}}, {\r\n \"id\": True, 'buildInfoData': True}).limit(100))\r\n if len(ids) > 0:\r\n for obj in ids:\r\n pid = obj['id']\r\n print(pid)\r\n get_house_by_buildInfo(pid,obj['buildInfoData'])\r\n DB[Table_Detail_Name].update_one({\"id\": pid}, {\"$set\": {'build': 1}})\r\n time.sleep(0.1)\r\n else:\r\n print(\"empty\")\r\n break\r\n\r\ndef get_xkz_info(pid,xkz,xkzInfo,kdata):\r\n \"\"\"\r\n 获取许可证\r\n :param pid: 项目id\r\n :param xkz: 许可证类型\r\n :param xkzInfo: 8481,,370603201711290101\r\n :param kdata: key name\r\n \"\"\"\r\n aCertInfo = xkzInfo.split(';;')\r\n data = []\r\n for info in aCertInfo:\r\n aCertItem = info.split(\",,\")\r\n if len(aCertItem) == 2:\r\n code = aCertItem[0]\r\n name = aCertItem[1]\r\n d = get_xkz_data(xkz,code, name)\r\n data.append(d)\r\n DB[Table_Detail_Name].update_one({\"id\": pid}, {\"$set\": {kdata: data}})\r\n\r\n pass\r\n\r\ndef get_xkz_data(xkz, code, name):\r\n r = requests.get(XKZ_info[xkz]+code)\r\n r.encoding = 'gb2312'\r\n html = r.text\r\n bs = BeautifulSoup(html, 'lxml')\r\n titles = bs.select('td.Infotitle')\r\n infoxxs = bs.select('td.Infoxx')\r\n keys = [td.text.strip() for td in titles]\r\n values = [td.text.strip() for td in infoxxs]\r\n data = dict(zip(keys, values))\r\n data['code'] = code\r\n data['name'] = name\r\n return data\r\n\r\n\r\ndef set_all_xkz_info(xkz):\r\n k = xkz\r\n kdata = k+\"Data\"\r\n while True:\r\n ids = list(DB[Table_Detail_Name].find({k: {\"$exists\": True}, kdata: {\"$exists\": False}}, {\r\n \"id\": True, k: True}).limit(100))\r\n if len(ids) > 0:\r\n for obj in ids:\r\n print(obj)\r\n get_xkz_info(obj[\"id\"], xkz, obj[k],kdata)\r\n time.sleep(0.1)\r\n else:\r\n print(\"empty\")\r\n break\r\n\r\ndef set_detail_done(hid):\r\n DB[Table_Basic_Name].update_one({\"id\": hid}, {\"$set\": {\"detail\": 1}})\r\n\r\n\r\ndef set_done(table, hid, key, value):\r\n DB[table].update_one({\"id\": hid}, {\"$set\": {key: value}})\r\n\r\n\r\ndef get_all_project_info():\r\n while True:\r\n ids = list(DB[Table_Basic_Name].find({\"detail\": {\"$exists\": False}}, {\r\n \"id\": True}).limit(100))\r\n if len(ids) > 0:\r\n for obj in ids:\r\n print(obj)\r\n get_project_info(obj[\"id\"])\r\n time.sleep(0.1)\r\n else:\r\n print(\"empty\")\r\n break\r\n\r\ndef get_all_build_info():\r\n while True:\r\n ids = list(DB[Table_Detail_Name].find({\"build\": {\"$exists\": False},\"buildInfoData\": {\"$exists\": False}}, {\r\n \"id\": True}).limit(100))\r\n if len(ids) > 0:\r\n for obj in ids:\r\n print(obj)\r\n get_build_info(obj['id'])\r\n time.sleep(0.1)\r\n else:\r\n print(\"empty\")\r\n break\r\n\r\n\r\nimport sys\r\nimport getopt\r\nimport argparse\r\nif __name__ == '__main__':\r\n parser = argparse.ArgumentParser(description=\"获取宁波房地产数据\")\r\n h = '''\r\n 命令 (default:help)\r\n --project: 项目基本信息;\r\n --detail: 项目详细信息;\r\n --build: 项目楼盘情况;\r\n --presell: 预售许可证信息;\r\n --tdz: 土地证信息;\r\n --sgxkz: 施工许可证信息;\r\n --ghxkz: 建设工程规划许可证;\r\n --jsydghxkz: 用地规划许可证信息;\r\n '''\r\n parser.add_argument('-cmd', '--cmd', type=str, default='help', help=h)\r\n parser.add_argument('-p', '--proxy', type=bool,\r\n default=False, help=\"是否启用代理,可选 (default:False)\")\r\n args = parser.parse_args()\r\n cmd = args.cmd\r\n proxy = args.proxy\r\n print(cmd, proxy)\r\n if cmd == \"project\":\r\n get_all_project()\r\n elif cmd == \"detail\":\r\n get_all_project_info()\r\n elif cmd == \"presell\":\r\n set_all_xkz_info('presellInfo')\r\n elif cmd == 'tdz':\r\n set_all_xkz_info('tdzInfo')\r\n elif cmd == 'sgxkz':\r\n set_all_xkz_info('sgxkzInfo')\r\n elif cmd == 'ghxkz':\r\n set_all_xkz_info('ghxkzInfo')\r\n elif cmd == 'jsydghxkz':\r\n set_all_xkz_info('jsydghxkzInfo')\r\n elif cmd == 'build':\r\n get_all_build_info()\r\n elif cmd == 'house':\r\n get_all_house()\r\n elif cmd == 'test':\r\n pass\r\n else:\r\n parser.print_help()\r\n", "sub_path": "company_spider/爬虫/loupan/yt.py", "file_name": "yt.py", "file_ext": "py", "file_size_in_byte": 15546, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pymongo.MongoClient", "line_number": 13, "usage_type": "call"}, {"api_name": "redis.Redis", "line_number": 19, "usage_type": "call"}, {"api_name": "requests.session", "line_number": 22, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 69, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 72, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 78, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 81, "usage_type": "call"}, {"api_name": "time.time", "line_number": 132, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 159, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 162, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 241, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 244, "usage_type": "call"}, {"api_name": "time.time", "line_number": 273, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 274, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 276, "usage_type": "call"}, {"api_name": "re.search", "line_number": 284, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 310, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 337, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 340, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 361, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 382, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 395, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 405, "usage_type": "call"}]} +{"seq_id": "221282119", "text": "# uncompyle6 version 3.7.4\n# Python bytecode 2.5 (62131)\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/gsteditor/element.py\n# Compiled at: 2009-05-03 07:26:43\nimport gst, gtk, gtk.glade, goocanvas, logging, paramwin\nfrom pad import Pad\n\nclass Element(goocanvas.Group):\n \"\"\"represents GStreamer element\"\"\"\n width = 100\n height = 66\n xPadding = 20\n yPadding = 20\n\n def __init__(self, element):\n goocanvas.Group.__init__(self)\n self.model = element\n self.model.set_data('widget', self)\n self.dragHandler = None\n self.box = goocanvas.Rect(parent=self, width=self.width, x=-self.width / 2, line_width=3, stroke_color='black', fill_color='grey', radius_y=5, radius_x=5)\n if element.get_factory():\n name = element.get_factory().get_longname()\n else:\n name = element.get_name()\n self.logger = logging.getLogger(name)\n self.text = goocanvas.Text(parent=self, text=name, width=self.width - 2 * self.xPadding, anchor=gtk.ANCHOR_NORTH, font='Sans 9')\n self.model.connect('pad_added', self.onPadAdded)\n self.buttonPressHandler = self.connect('button_press_event', self.onButtonPress)\n self.buttonReleaseHandler = self.connect('button_release_event', self.onButtonRelease)\n self.updateLayout()\n return\n\n def logBounds(self):\n b = self.get_bounds()\n self.logger.debug('bounds before adding to table: (%d,%d) - (%d,%d)' % (b.x1, b.y1, b.x2, b.y2))\n\n def onPadAdded(self, model, padModel):\n \"\"\" Handler for pad_added signal \"\"\"\n assert model == self.model\n self.logger.debug('pad_added: ' + str(padModel) + ' to ' + str(model))\n pad = Pad(padModel)\n self.add_child(pad)\n handler = padModel.get_data('link-handler')\n if handler:\n handler()\n self.updateLayout()\n\n def updateLayout(self):\n srcPads = [ pad.get_data('widget') for pad in self.model.src_pads() ]\n sinkPads = [ pad.get_data('widget') for pad in self.model.sink_pads() ]\n self.setSize(width=None, height=max(self.height, self.box.props.height, max(len(srcPads), len(sinkPads)) * Pad.radius * 2))\n\n def updatePadsLayout(pads, x):\n for (i, pad) in enumerate(pads):\n if pad:\n pad.setPosition(x, self.box.props.height / (len(pads) + 1) * (i + 1) - self.box.props.height / 2)\n\n updatePadsLayout(srcPads, self.box.props.width / 2 - self.xPadding / 2)\n updatePadsLayout(sinkPads, -self.box.props.width / 2 + self.xPadding / 2)\n return\n\n def setPlaying(self):\n \"\"\"sets the element to playing\"\"\"\n self.model.set_state(gst.STATE_PLAYING)\n\n def setPaused(self):\n \"\"\"sets the element to paused\"\"\"\n self.model.set_state(gst.STATE_PAUSED)\n\n def setPlayMode(self, state):\n \"\"\"sets an explicit state for the element\"\"\"\n self.model.set_state(state)\n\n def getPlayMode(self):\n \"\"\"returns the current state of the element\"\"\"\n (rtrn, current, pending) = self.model.get_state(gst.CLOCK_TIME_NONE)\n return current\n\n def onButtonPress(self, item, target, event):\n \"\"\"handle button clicks\"\"\"\n if event.type == gtk.gdk.BUTTON_PRESS:\n self.raise_(None)\n if event.button == 1:\n logging.debug('start moving element')\n handler = item.connect('motion_notify_event', self.onMotion, event.x, event.y)\n self.dragHandler = (item, handler)\n return True\n elif event.button == 3:\n popup = gtk.Menu()\n configItem = gtk.ImageMenuItem('Configure Element')\n configImg = gtk.image_new_from_stock(gtk.STOCK_INDEX, gtk.ICON_SIZE_MENU)\n configItem.set_image(configImg)\n configItem.connect('activate', self._configure)\n configItem.show()\n popup.attach(configItem, 0, 1, 0, 1)\n deleteItem = gtk.ImageMenuItem('Delete Element')\n deleteImg = gtk.image_new_from_stock(gtk.STOCK_DELETE, gtk.ICON_SIZE_MENU)\n deleteItem.set_image(deleteImg)\n deleteItem.connect('activate', self._delete)\n deleteItem.show()\n popup.attach(deleteItem, 0, 1, 1, 2)\n popup.popup(None, None, None, event.button, event.time)\n return True\n if event.type == gtk.gdk._2BUTTON_PRESS:\n self._configure()\n return True\n return\n\n def onMotion(self, item, target, event, startX, startY):\n if event.state & gtk.gdk.BUTTON1_MASK:\n (x, y, scale, rotate) = self.get_simple_transform()\n self.setPosition(x + int(event.x - startX), y + int(event.y - startY))\n return True\n\n def onButtonRelease(self, view, target, event):\n \"\"\"finish dragging\"\"\"\n if self.dragHandler:\n logging.debug('end moving element')\n (item, handler) = self.dragHandler\n item.disconnect(handler)\n self.dragHandler = None\n return True\n\n def _delete(self, event):\n \"\"\"un-draws the element and cleans up for deletion\"\"\"\n dialog = gtk.Dialog('Delete Element', self.get_canvas().get_toplevel(), gtk.DIALOG_MODAL | gtk.DIALOG_DESTROY_WITH_PARENT, (\n gtk.STOCK_DELETE, gtk.RESPONSE_OK, gtk.STOCK_CANCEL, gtk.RESPONSE_CLOSE))\n dialog.vbox.pack_start(gtk.Label('Are you sure?'))\n dialog.set_position(gtk.WIN_POS_CENTER_ON_PARENT)\n dialog.show_all()\n rtn = dialog.run()\n if rtn != gtk.RESPONSE_OK:\n pass\n else:\n if hasattr(self, 'paramWin'):\n del self.paramWin\n self.get_parent().model.remove(self.model)\n dialog.destroy()\n\n def _configure(self, event=None):\n \"\"\"opens up the config dialog to set element parameters\"\"\"\n if not hasattr(self, 'paramWin'):\n self.paramWin = paramwin.ParamWin(self.model)\n self.paramWin.show_all()\n return True\n\n def remove(self):\n self.model.get_parent().remove(self.model)\n\n def pads(self):\n for pad in self.model.pads():\n widget = pad.get_data('widget')\n yield widget\n if widget.get_child(1):\n yield widget.get_child(1)\n\n def updateLinks(self):\n for pad in self.pads():\n pad.updateLink()\n\n def setPosition(self, x, y):\n self.set_simple_transform(x, y, 1, 0)\n for pad in self.pads():\n if not pad.link:\n continue\n (pad1, pad2) = pad.link.get_data('pads')\n if pad1 == pad:\n point = 0\n elif pad2 == pad:\n point = 1\n else:\n assert not 'link has invalid pads data'\n coords = pad.link.props.points.coords\n coords[point] = pad.link.get_parent().coordsFromChild(pad)\n self.logger.debug('moving link point from ' + str(pad.link.props.points.coords[point]) + ' to ' + str(coords[point]))\n pad.link.props.points = goocanvas.Points(coords)\n\n def setSize(self, width, height):\n if width:\n self.box.props.width = width\n self.box.props.x = -width / 2\n self.text.props.width = width - self.xPadding * 2\n if height:\n self.box.props.height = height\n self.box.props.y = -height / 2\n self.text.props.y = -height / 2 + 5\n\n def coordsFromChild(self, child):\n x = 0\n y = 0\n while child != self:\n (childX, childY, scale, rotate) = child.get_simple_transform()\n x += childX\n y += childY\n child = child.get_parent()\n\n return (x, y)\n\n def getDownstream(self):\n for pad in self.model.src_pads():\n if pad.get_peer():\n widget = pad.get_peer().get_data('widget')\n if widget and widget.get_parent() and widget.get_parent().__class__ != Pad:\n yield widget.get_parent()", "sub_path": "pycfiles/GstEditor-0.1.0-py2.5/element.py", "file_name": "element.py", "file_ext": "py", "file_size_in_byte": 8195, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "goocanvas.Group", "line_number": 10, "usage_type": "attribute"}, {"api_name": "goocanvas.Group.__init__", "line_number": 18, "usage_type": "call"}, {"api_name": "goocanvas.Group", "line_number": 18, "usage_type": "attribute"}, {"api_name": "goocanvas.Rect", "line_number": 22, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 27, "usage_type": "call"}, {"api_name": "goocanvas.Text", "line_number": 28, "usage_type": "call"}, {"api_name": "gtk.ANCHOR_NORTH", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pad.Pad", "line_number": 43, "usage_type": "call"}, {"api_name": "pad.get_data", "line_number": 51, "usage_type": "call"}, {"api_name": "pad.get_data", "line_number": 52, "usage_type": "call"}, {"api_name": "pad.Pad.radius", "line_number": 53, "usage_type": "attribute"}, {"api_name": "pad.Pad", "line_number": 53, "usage_type": "name"}, {"api_name": "pad.setPosition", "line_number": 58, "usage_type": "call"}, {"api_name": "gst.STATE_PLAYING", "line_number": 66, "usage_type": "attribute"}, {"api_name": "gst.STATE_PAUSED", "line_number": 70, "usage_type": "attribute"}, {"api_name": "gst.CLOCK_TIME_NONE", "line_number": 78, "usage_type": "attribute"}, {"api_name": "gtk.gdk", "line_number": 83, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 86, "usage_type": "call"}, {"api_name": "gtk.Menu", "line_number": 91, "usage_type": "call"}, {"api_name": "gtk.ImageMenuItem", "line_number": 92, "usage_type": "call"}, {"api_name": "gtk.image_new_from_stock", "line_number": 93, "usage_type": "call"}, {"api_name": "gtk.STOCK_INDEX", "line_number": 93, "usage_type": "attribute"}, {"api_name": "gtk.ICON_SIZE_MENU", "line_number": 93, "usage_type": "attribute"}, {"api_name": "gtk.ImageMenuItem", "line_number": 98, "usage_type": "call"}, {"api_name": "gtk.image_new_from_stock", "line_number": 99, "usage_type": "call"}, {"api_name": "gtk.STOCK_DELETE", "line_number": 99, "usage_type": "attribute"}, {"api_name": "gtk.ICON_SIZE_MENU", "line_number": 99, "usage_type": "attribute"}, {"api_name": "gtk.gdk", "line_number": 106, "usage_type": "attribute"}, {"api_name": "gtk.gdk", "line_number": 112, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 120, "usage_type": "call"}, {"api_name": "gtk.Dialog", "line_number": 128, "usage_type": "call"}, {"api_name": "gtk.DIALOG_MODAL", "line_number": 128, "usage_type": "attribute"}, {"api_name": "gtk.DIALOG_DESTROY_WITH_PARENT", "line_number": 128, "usage_type": "attribute"}, {"api_name": "gtk.STOCK_DELETE", "line_number": 129, "usage_type": "attribute"}, {"api_name": "gtk.RESPONSE_OK", "line_number": 129, "usage_type": "attribute"}, {"api_name": "gtk.STOCK_CANCEL", "line_number": 129, "usage_type": "attribute"}, {"api_name": "gtk.RESPONSE_CLOSE", "line_number": 129, "usage_type": "attribute"}, {"api_name": "gtk.Label", "line_number": 130, "usage_type": "call"}, {"api_name": "gtk.WIN_POS_CENTER_ON_PARENT", "line_number": 131, "usage_type": "attribute"}, {"api_name": "gtk.RESPONSE_OK", "line_number": 134, "usage_type": "attribute"}, {"api_name": "paramwin.ParamWin", "line_number": 145, "usage_type": "call"}, {"api_name": "pad.get_data", "line_number": 154, "usage_type": "call"}, {"api_name": "pad.updateLink", "line_number": 161, "usage_type": "call"}, {"api_name": "pad.link", "line_number": 166, "usage_type": "attribute"}, {"api_name": "pad.link.get_data", "line_number": 168, "usage_type": "call"}, {"api_name": "pad.link", "line_number": 168, "usage_type": "attribute"}, {"api_name": "pad.link", "line_number": 175, "usage_type": "attribute"}, {"api_name": "pad.link.get_parent", "line_number": 176, "usage_type": "call"}, {"api_name": "pad.link", "line_number": 176, "usage_type": "attribute"}, {"api_name": "pad.link", "line_number": 177, "usage_type": "attribute"}, {"api_name": "pad.link", "line_number": 178, "usage_type": "attribute"}, {"api_name": "goocanvas.Points", "line_number": 178, "usage_type": "call"}, {"api_name": "pad.get_peer", "line_number": 203, "usage_type": "call"}, {"api_name": "pad.get_peer", "line_number": 204, "usage_type": "call"}, {"api_name": "pad.Pad", "line_number": 205, "usage_type": "name"}]} +{"seq_id": "467276716", "text": "from os.path import join\nimport librosa\nimport librosa.display\nimport numpy as np\nimport scipy.signal\nimport scipy.io.wavfile\nimport matplotlib.pyplot as plt\nfrom sklearn.preprocessing import scale\nimport math\n\n\ndef round_up_to_even(x):\n return int(math.ceil(x / 2.) * 2)\n \ndef next_pow2(x):\n return 2**(x-1).bit_length()\n \ndef apply_window(frame):\n\n\tM = frame.size\n\twindow = scipy.signal.hann(M)\n\t\n\treturn(frame*window)\n\ndef overlapp_add_reconstruction( frame1_windowed, frame2_windowed ):\n\t\n\tM = frame2_windowed.size\n\tR = M/2\n\toutput = np.zeros(frame1_windowed.size + R)\n\toutput[:frame1_windowed.size] = frame1_windowed\n\toutput[(frame1_windowed.size-R):] = output[(frame1_windowed.size-R):] + frame2_windowed\n\t\n\treturn(output)\n\ndef load_audio_files( audio_folder_path, chapter_names, noise_names ):\n\t\n\tchapters = {}\n\tfor chapter_name in chapter_names:\n\t\tfile_path = join(audio_folder_path, chapter_name + \".wav\")\n\t\tfs, audio_time_series = scipy.io.wavfile.read(file_path)\n\t\tchapters[ chapter_name ] = (audio_time_series, fs)\n\t\n\tnoise = {}\n\tfor noise_name in noise_names:\n\t\tfile_path = join(audio_folder_path, noise_name + \".wav\")\n\t\tfs, audio_time_series = scipy.io.wavfile.read(file_path)\n\t\tnoise[ noise_name ] = (audio_time_series, fs)\n\t\n\treturn chapters, noise\n\ndef load_audio( audio_folder_path, audio_filename ):\n\tfile_path = audio_folder_path + audio_filename\n\tfs, audio_time_series = scipy.io.wavfile.read(file_path)\n\treturn audio_time_series, fs\n\t\ndef concatenate_audio( names, dict ):\n\n\tarrays_to_concatenate = []\n\tfs = dict[names[0]][1]\n\tfor name in names:\n\t\tarrays_to_concatenate.append(dict[name][0])\n\n\treturn(np.concatenate(arrays_to_concatenate) , fs)\n\t\ndef combine_clean_and_noise(audio_time_series_train, audio_time_series_noise, snr_db):\n\tif( audio_time_series_train.size <= audio_time_series_noise.size ):\n\t\taudio_time_series_noise = audio_time_series_noise[0:audio_time_series_train.size] \n\telse:\n\t\taudio_time_series_train = audio_time_series_train[0:audio_time_series_noise.size]\n\t\n\taudio_time_series_train = audio_time_series_train.astype('float')\n\taudio_time_series_noise = audio_time_series_noise.astype('float')\n\t\n\tA_train_2 = np.mean(np.power(np.absolute(audio_time_series_train),2))\n\tA_noise_2 = np.mean(np.power(np.absolute(audio_time_series_noise),2))\n\tA_noise_targ_2 = A_train_2 / (10**(snr_db/10.))\n\t\n\tscaling_coeff = np.sqrt(A_noise_targ_2) / np.sqrt(A_noise_2)\n\t\t\n\tcombined_result = audio_time_series_train + (scaling_coeff * audio_time_series_noise)\n\t\n\treturn(combined_result)\n\ndef downsample( audio, orig_sr, targ_sr):\n\taudio = audio.astype('float')\n\taudio_downsampled = librosa.resample(audio, orig_sr, targ_sr)\n\taudio_downsampled = audio_downsampled.astype('int16')\n\treturn audio_downsampled, targ_sr\n\ndef generate_frames( audio_time_series_train, fs, frame_time, lag = 0.5 ):\n\tframe_length = round_up_to_even( frame_time * fs ) \n\ttotal_time_steps = int((audio_time_series_train.size / (frame_length * lag)) - 1)\n\t\n\tx_train = np.zeros( shape = (frame_length, total_time_steps) )\n\n\tfor i in range(0, (total_time_steps - 1)):\n\t\tx_train[:, i] = apply_window( audio_time_series_train[ i*(frame_length / 2) : (i*(frame_length / 2) + frame_length) ] )\n\t\t\n\treturn(x_train)\n\t\ndef generate_train_features( x_train ):\n\tn = next_pow2(x_train.shape[0])\n\tx_train_features = np.zeros( shape = (n, x_train.shape[1]))\n\tfor i in range(0, x_train.shape[1]):\n\t\tx_train_features[:, i] = np.abs(np.fft.fft( a = x_train[:, i], n = n ))\n\treturn(x_train_features)\n\t\ndef scale_features( x_train_features, mu, std ):\n\t\n\tx_train_features = (x_train_features - mu) / float(std)\n\t\t\n\treturn(x_train_features.T)\n\t\ndef rebuild_audio_from_indices( predicted_time_series_indices, x_train ):\n\toutput = x_train[:, predicted_time_series_indices[0]]\n\t\n\tfor i in predicted_time_series_indices[1:]:\n\t\toutput = overlapp_add_reconstruction(output, x_train[:, i])\n\t\t\n\treturn(output.astype('int16'))\n\t\ndef rebuild_audio( x_test ):\n\toutput = x_test[0, :]\n\tfor i in xrange(1, x_test.shape[0]-1):\n\t\toutput = overlapp_add_reconstruction(output, x_test[i, :])\n\t\t\n\treturn(output.astype('int16'))\n\ndef sdr_computation( target_speech, distorted_speech ):\n\tif( target_speech.size <= distorted_speech.size ):\n\t\tdistorted_speech = distorted_speech[0:target_speech.size] \n\telse:\n\t\ttarget_speech = target_speech[0:distorted_speech.size]\n\t\n\ttarget_speech = target_speech.astype('float')\n\tdistorted_speech = distorted_speech.astype('float')\n\n\tA_target_2 = np.mean(np.power(np.absolute(target_speech),2))\n\tA_noisedistortion_2 = np.mean(np.power(np.absolute(distorted_speech),2))\n\t\n\tsdr = 10*np.log10(A_target_2 / A_noisedistortion_2)\n\t\n\treturn(sdr)\n\t\ndef generate_input( audio_time_series, fs, frame_time, train_mu, train_std ):\n\t\n\tx_frames = generate_frames( audio_time_series, fs, frame_time, )\n\tx_frames_scaled = scale_features( x_frames, train_mu, train_std )\n\tx_frames_scaled_input = np.reshape(x_frames_scaled, (x_frames_scaled.shape[0], x_frames_scaled.shape[1], 1))\n\t\n\treturn(x_frames_scaled_input)", "sub_path": "Scripts/audio_preprocessing.py", "file_name": "audio_preprocessing.py", "file_ext": "py", "file_size_in_byte": 4998, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "math.ceil", "line_number": 13, "usage_type": "call"}, {"api_name": "scipy.signal.signal.hann", "line_number": 21, "usage_type": "call"}, {"api_name": "scipy.signal.signal", "line_number": 21, "usage_type": "attribute"}, {"api_name": "scipy.signal", "line_number": 21, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "scipy.signal.io.wavfile.read", "line_number": 40, "usage_type": "call"}, {"api_name": "scipy.signal.io", "line_number": 40, "usage_type": "attribute"}, {"api_name": "scipy.signal", "line_number": 40, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}, {"api_name": "scipy.signal.io.wavfile.read", "line_number": 46, "usage_type": "call"}, {"api_name": "scipy.signal.io", "line_number": 46, "usage_type": "attribute"}, {"api_name": "scipy.signal", "line_number": 46, "usage_type": "name"}, {"api_name": "scipy.signal.io.wavfile.read", "line_number": 53, "usage_type": "call"}, {"api_name": "scipy.signal.io", "line_number": 53, "usage_type": "attribute"}, {"api_name": "scipy.signal", "line_number": 53, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 78, "usage_type": "call"}, {"api_name": "librosa.resample", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.fft.fft", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 105, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 149, "usage_type": "call"}]} +{"seq_id": "374045897", "text": "import time\nfrom openapi_server.models.stats import Stats\nfrom openapi_server.models.stats_version import StatsVersion\nfrom openapi_server.models.stats_tool import StatsTool\nfrom openapi_server.models.stats_tags_source import StatsTagsSource\nfrom openapi_server.models.stats_note import StatsNote\nfrom openapi_server.models.search_result import SearchResult\nfrom openapi_server.models.search_result_by_currency \\\n import SearchResultByCurrency\nfrom gsrest.service.stats_service import get_currency_statistics\nfrom gsrest.service.txs_service import list_matching_txs\nfrom gsrest.service.tags_service import list_labels\nfrom gsrest.service.addresses_service import list_matching_addresses\nfrom gsrest.db import get_connection\n\nimport yaml\n\nnote1 = ('Please **note** that the clustering dataset is built with'\n ' multi input address clustering to avoid false clustering '\n 'results due to coinjoins (see TITANIUM glossary '\n 'http://titanium-project.eu/glossary/#coinjoin), we exclude'\n ' coinjoins prior to clustering. This does not eliminate '\n 'the risk of false results, since coinjoin detection is also'\n ' heuristic in nature, but it should decrease the potential '\n 'for wrong cluster merges.')\n\n\nnote2 = ('Our tags are all manually crawled or from credible sources,'\n ' we do not use tags that where automatically extracted '\n 'without human interaction. Origins of the tags have been '\n 'saved for reproducibility please contact the GraphSense '\n 'team (contact@graphsense.info) for more insight.')\n\n\ndef get_statistics():\n \"\"\"\n Returns summary statistics on all available currencies\n \"\"\"\n with open('./openapi_server/openapi/openapi.yaml', 'r') as input_file:\n input = yaml.safe_load(input_file)\n version = input['info']['version']\n title = input['info']['title']\n currency_stats = list()\n db = get_connection()\n for currency in db.get_supported_currencies():\n currency_stats.append(get_currency_statistics(currency, version))\n return Stats(\n currencies=currency_stats,\n version=StatsVersion(\n nr=version,\n hash=None,\n timestamp=time.strftime(\n \"%Y-%m-%d %H:%M:%S\", time.gmtime()),\n file=version),\n tools=[StatsTool(\n visible_name=title,\n version=version,\n id='ait:graphsense',\n titanium_replayable=False,\n responsible_for=[]\n )],\n tags_source=StatsTagsSource(\n visible_name=\"GraphSense attribution tags\",\n version=version,\n id=\"graphsense_tags\",\n report_uuid=\"graphsense_tags\"),\n notes=[StatsNote(note=note1),\n StatsNote(note=note2)])\n\n\ndef search(q, currency=None, limit=None):\n db = get_connection()\n currencies = db.get_supported_currencies()\n\n q = q.strip()\n result = SearchResult(currencies=[], labels=[])\n\n for curr in currencies:\n if currency is not None and currency.lower() != curr.lower():\n continue\n element = SearchResultByCurrency(\n currency=curr,\n addresses=[],\n txs=[]\n )\n\n # Look for addresses and transactions\n txs = list_matching_txs(curr, q)\n element.txs = txs[:limit]\n\n addresses = list_matching_addresses(curr, q)\n element.addresses = addresses[:limit]\n\n result.currencies.append(element)\n\n labels = list_labels(curr, q)[:limit]\n if labels:\n result.labels += labels\n\n return result\n", "sub_path": "gsrest/service/general_service.py", "file_name": "general_service.py", "file_ext": "py", "file_size_in_byte": 3846, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "yaml.safe_load", "line_number": 40, "usage_type": "call"}, {"api_name": "gsrest.db.get_connection", "line_number": 44, "usage_type": "call"}, {"api_name": "gsrest.service.stats_service.get_currency_statistics", "line_number": 46, "usage_type": "call"}, {"api_name": "openapi_server.models.stats.Stats", "line_number": 47, "usage_type": "call"}, {"api_name": "openapi_server.models.stats_version.StatsVersion", "line_number": 49, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 52, "usage_type": "call"}, {"api_name": "time.gmtime", "line_number": 53, "usage_type": "call"}, {"api_name": "openapi_server.models.stats_tool.StatsTool", "line_number": 55, "usage_type": "call"}, {"api_name": "openapi_server.models.stats_tags_source.StatsTagsSource", "line_number": 62, "usage_type": "call"}, {"api_name": "openapi_server.models.stats_note.StatsNote", "line_number": 67, "usage_type": "call"}, {"api_name": "openapi_server.models.stats_note.StatsNote", "line_number": 68, "usage_type": "call"}, {"api_name": "gsrest.db.get_connection", "line_number": 72, "usage_type": "call"}, {"api_name": "openapi_server.models.search_result.SearchResult", "line_number": 76, "usage_type": "call"}, {"api_name": "openapi_server.models.search_result_by_currency.SearchResultByCurrency", "line_number": 81, "usage_type": "call"}, {"api_name": "gsrest.service.txs_service.list_matching_txs", "line_number": 88, "usage_type": "call"}, {"api_name": "gsrest.service.addresses_service.list_matching_addresses", "line_number": 91, "usage_type": "call"}, {"api_name": "gsrest.service.tags_service.list_labels", "line_number": 96, "usage_type": "call"}]} +{"seq_id": "103983197", "text": "#!/usr/bin/python3\n#Main endpoint caller\nimport cfg.maincfgs\nimport os\nimport cgi\nimport cgitb\nimport json\n\nfrom core.wrapper_lib import wrapperManager\nfrom core.comm_lib import commManager\nfrom core.utility_lib import utilityLibrary\n\n#Main variables\ncgitb.enable()\nwm = wrapperManager()\ncomm = commManager()\nutl = utilityLibrary()\nhtmlStr = \"\"\nhtmlContentTemplate = \"main_menu\"\ndataToWrap = \"\"\nwm.headerInit()\n\n\ndef endpointLoader():\n global htmlStr, htmlContentTemplate, dataToWrap\n\n #Getting the content\n reqData = utl.formLoader(cgi.FieldStorage())\n dataToWrap = \"Welcome to bagdag {}\"\n\n if utl.getMethod() in utl.getValidMethods() and len(reqData) > 0:\n dataToWrap = comm.postData(cfg.maincfgs.xbarcfg[\"xbar_endpoint\"], reqData[\"request_data\"], \"json\")\n htmlContentTemplate = \"default_content\"\n #Call the endpoint and request the result\n dataToWrap = json.loads(dataToWrap)\n dataToWrap = dataToWrap[\"routers\"][\"response_data\"][\"html\"]\n else:\n #No data goes to the main\n reqData = {}\n\n\n #Building the data to wrap\n #wm.headerInit()\n htmlStr += wm.htmlTemplateWrapper(dataToWrap, htmlContentTemplate) \n print(htmlStr)\n \n\n\nif __name__ == '__main__':\n endpointLoader()", "sub_path": "endpoint.py", "file_name": "endpoint.py", "file_ext": "py", "file_size_in_byte": 1333, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "cgitb.enable", "line_number": 14, "usage_type": "call"}, {"api_name": "core.wrapper_lib.wrapperManager", "line_number": 15, "usage_type": "call"}, {"api_name": "core.comm_lib.commManager", "line_number": 16, "usage_type": "call"}, {"api_name": "core.utility_lib.utilityLibrary", "line_number": 17, "usage_type": "call"}, {"api_name": "cgi.FieldStorage", "line_number": 28, "usage_type": "call"}, {"api_name": "cfg.maincfgs.maincfgs", "line_number": 32, "usage_type": "attribute"}, {"api_name": "cfg.maincfgs", "line_number": 32, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "511794152", "text": "# IMPORTS\nimport copy\nfrom flask import Blueprint, render_template, request, flash\nfrom flask_login import login_required, current_user\nfrom app import db, requires_roles\nfrom models import Draw\nfrom models import User\n\n# CONFIG\nlottery_blueprint = Blueprint('lottery', __name__, template_folder='templates')\n\n\n# VIEWS\n# view lottery page\n@lottery_blueprint.route('/lottery')\n@login_required\n@requires_roles('user')\ndef lottery():\n\n return render_template('lottery.html')\n\n\n@lottery_blueprint.route('/add_draw', methods=['POST'])\n@login_required\n@requires_roles('user')\ndef add_draw():\n submitted_draw = ''\n for i in range(6):\n submitted_draw += request.form.get('no' + str(i + 1)) + ' '\n submitted_draw.strip()\n\n # create a new draw with the form data.\n new_draw = Draw(user_id=current_user.id, draw=submitted_draw, win=False, round=0, draw_key=current_user.draw_key)\n\n # add the new draw to the database\n db.session.add(new_draw)\n db.session.commit()\n\n # re-render lottery.page\n flash('Draw %s submitted.' % submitted_draw)\n return lottery()\n\n\n# view all draws that have not been played\n@lottery_blueprint.route('/view_draws', methods=['POST'])\n@login_required\n@requires_roles('user')\ndef view_draws():\n # get all draws that have not been played [played=0]\n playable_draws = Draw.query.filter_by(user_id=current_user.id).all()\n draw_copies = list(map(lambda x: copy.deepcopy(x), playable_draws))\n decrypted_draws = []\n for p in draw_copies:\n user = User.query.filter_by(email=current_user.email).first()\n p.view_draw(user.draw_key)\n decrypted_draws.append(p)\n # if playable draws exist\n if len(playable_draws) != 0:\n # re-render lottery page with playable draws\n return render_template('lottery.html', playable_draws=decrypted_draws)\n else:\n flash('No playable draws.')\n return lottery()\n\n\n# view lottery results\n@lottery_blueprint.route('/check_draws', methods=['POST'])\n@login_required\n@requires_roles('user')\ndef check_draws():\n # get played draws\n played_draws = Draw.query.filter_by(user_id=current_user.id).all()\n draw_copies = list(map(lambda x: copy.deepcopy(x), played_draws))\n decrypted_draws = []\n for p in draw_copies:\n user = User.query.filter_by(email=current_user.email).first()\n p.view_draw(user.draw_key)\n decrypted_draws.append(p)\n # if played draws exist\n if len(played_draws) != 0:\n return render_template('lottery.html', results=decrypted_draws, played=True)\n\n # if no played draws exist [all draw entries have been played therefore wait for next lottery round]\n else:\n flash(\"Next round of lottery yet to play. Check you have playable draws.\")\n return lottery()\n\n\n# delete all played draws\n@lottery_blueprint.route('/play_again', methods=['POST'])\n@login_required\n@requires_roles('user')\ndef play_again():\n\n # deleting only the draws of the current user\n delete_played = Draw.__table__.delete().where(Draw.user_id == current_user.id)\n db.session.execute(delete_played)\n db.session.commit()\n\n flash(\"All played draws deleted.\")\n return lottery()\n\n\n", "sub_path": "lottery/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3174, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "flask.Blueprint", "line_number": 10, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 20, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 16, "usage_type": "name"}, {"api_name": "app.requires_roles", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 29, "usage_type": "name"}, {"api_name": "models.Draw", "line_number": 33, "usage_type": "call"}, {"api_name": "flask_login.current_user.id", "line_number": 33, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 33, "usage_type": "name"}, {"api_name": "flask_login.current_user.draw_key", "line_number": 33, "usage_type": "attribute"}, {"api_name": "app.db.session.add", "line_number": 36, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 36, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 36, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 37, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 37, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 37, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 40, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 24, "usage_type": "name"}, {"api_name": "app.requires_roles", "line_number": 25, "usage_type": "call"}, {"api_name": "models.Draw.query.filter_by", "line_number": 50, "usage_type": "call"}, {"api_name": "models.Draw.query", "line_number": 50, "usage_type": "attribute"}, {"api_name": "models.Draw", "line_number": 50, "usage_type": "name"}, {"api_name": "flask_login.current_user.id", "line_number": 50, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 50, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 51, "usage_type": "call"}, {"api_name": "models.User.query.filter_by", "line_number": 54, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 54, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 54, "usage_type": "name"}, {"api_name": "flask_login.current_user.email", "line_number": 54, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 60, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 62, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 46, "usage_type": "name"}, {"api_name": "app.requires_roles", "line_number": 47, "usage_type": "call"}, {"api_name": "models.Draw.query.filter_by", "line_number": 72, "usage_type": "call"}, {"api_name": "models.Draw.query", "line_number": 72, "usage_type": "attribute"}, {"api_name": "models.Draw", "line_number": 72, "usage_type": "name"}, {"api_name": "flask_login.current_user.id", "line_number": 72, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 72, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 73, "usage_type": "call"}, {"api_name": "models.User.query.filter_by", "line_number": 76, "usage_type": "call"}, {"api_name": "models.User.query", "line_number": 76, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 76, "usage_type": "name"}, {"api_name": "flask_login.current_user.email", "line_number": 76, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 76, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 81, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 85, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 68, "usage_type": "name"}, {"api_name": "app.requires_roles", "line_number": 69, "usage_type": "call"}, {"api_name": "models.Draw.__table__.delete", "line_number": 96, "usage_type": "call"}, {"api_name": "models.Draw.__table__", "line_number": 96, "usage_type": "attribute"}, {"api_name": "models.Draw", "line_number": 96, "usage_type": "name"}, {"api_name": "models.Draw.user_id", "line_number": 96, "usage_type": "attribute"}, {"api_name": "flask_login.current_user.id", "line_number": 96, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 96, "usage_type": "name"}, {"api_name": "app.db.session.execute", "line_number": 97, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 97, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 97, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 98, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 98, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 98, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 100, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 91, "usage_type": "name"}, {"api_name": "app.requires_roles", "line_number": 92, "usage_type": "call"}]} +{"seq_id": "29903380", "text": "# coding: utf8\r\n__author__ = 'Lev'\r\n\r\nimport requests\r\nimport json\r\n\r\nfrom domain.request import Request\r\nfrom domain.request_factory import build_error_frequency_request, build_error_duplicate_request\r\nfrom settings import VK_LISTENING_TOKEN\r\nfrom util import log\r\nfrom config import user_attempts, user_spam_warnings, answers_queue\r\n\r\n\r\ndef get_long_poll_server():\r\n log('getting long poll server...')\r\n method_url = 'https://api.vk.com/method/messages.getLongPollServer?'\r\n params = dict(access_token=VK_LISTENING_TOKEN, use_ssl=1)\r\n response = requests.get(method_url, params=params)\r\n result = json.loads(response.text)\r\n if 'error' in result:\r\n raise Exception('error in getting long poll server...')\r\n result = result['response']\r\n log('got long poll server...')\r\n return result['server'], result['key'], result['ts']\r\n\r\n\r\ndef long_polling(server, key, ts):\r\n log('starting long polling...')\r\n method_url = 'https://' + server\r\n params = dict(act='a_check', key=key, ts=ts, wait=25, mode=0, version=1)\r\n response = requests.get(method_url, params=params)\r\n return json.loads(response.text)\r\n\r\n\r\ndef process_long_polling_results(updates):\r\n log('found {} update(s)...'.format(len(updates)))\r\n messages = []\r\n for update in updates:\r\n # fuck magic numbers\r\n if update[0] == 4 and not (update[2] & 2) and update[3] < 2000000000:\r\n user_id = update[3]\r\n text = update[6]\r\n messages.append(update)\r\n if user_id not in user_attempts:\r\n user_attempts[user_id] = []\r\n user_attempts[user_id].append(text)\r\n\r\n if len(messages) == 0:\r\n return messages\r\n\r\n log('found {} message(s)...'.format(len(messages)))\r\n reqs = []\r\n for message in messages:\r\n # fuck magic numbers\r\n user_id = message[3]\r\n dt = message[4]\r\n text = message[6]\r\n attempts = user_attempts[user_id]\r\n if len(attempts) > 3:\r\n if user_id not in user_spam_warnings:\r\n log('detected spam (frequent) from user {}, adding spam warning...'.format(user_id))\r\n user_spam_warnings[user_id] = True\r\n answers_queue.put(build_error_frequency_request(user_id, dt))\r\n else:\r\n if len(attempts) != len(set(attempts)):\r\n if user_id not in user_spam_warnings:\r\n log('detected spam (duplicate) from user {}, adding spam warning...'.format(user_id))\r\n user_spam_warnings[user_id] = True\r\n answers_queue.put(build_error_duplicate_request(user_id, dt))\r\n else:\r\n reqs.append(Request(user_id, dt, text))\r\n return reqs\r\n\r\n\r\n", "sub_path": "vk_api/listening.py", "file_name": "listening.py", "file_ext": "py", "file_size_in_byte": 2741, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "util.log", "line_number": 15, "usage_type": "call"}, {"api_name": "settings.VK_LISTENING_TOKEN", "line_number": 17, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 18, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 19, "usage_type": "call"}, {"api_name": "util.log", "line_number": 23, "usage_type": "call"}, {"api_name": "util.log", "line_number": 28, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 31, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 32, "usage_type": "call"}, {"api_name": "util.log", "line_number": 36, "usage_type": "call"}, {"api_name": "config.user_attempts", "line_number": 44, "usage_type": "name"}, {"api_name": "config.user_attempts", "line_number": 45, "usage_type": "name"}, {"api_name": "config.user_attempts", "line_number": 46, "usage_type": "name"}, {"api_name": "util.log", "line_number": 51, "usage_type": "call"}, {"api_name": "config.user_attempts", "line_number": 58, "usage_type": "name"}, {"api_name": "config.user_spam_warnings", "line_number": 60, "usage_type": "name"}, {"api_name": "util.log", "line_number": 61, "usage_type": "call"}, {"api_name": "config.user_spam_warnings", "line_number": 62, "usage_type": "name"}, {"api_name": "config.answers_queue.put", "line_number": 63, "usage_type": "call"}, {"api_name": "config.answers_queue", "line_number": 63, "usage_type": "name"}, {"api_name": "domain.request_factory.build_error_frequency_request", "line_number": 63, "usage_type": "call"}, {"api_name": "config.user_spam_warnings", "line_number": 66, "usage_type": "name"}, {"api_name": "util.log", "line_number": 67, "usage_type": "call"}, {"api_name": "config.user_spam_warnings", "line_number": 68, "usage_type": "name"}, {"api_name": "config.answers_queue.put", "line_number": 69, "usage_type": "call"}, {"api_name": "config.answers_queue", "line_number": 69, "usage_type": "name"}, {"api_name": "domain.request_factory.build_error_duplicate_request", "line_number": 69, "usage_type": "call"}, {"api_name": "domain.request.Request", "line_number": 71, "usage_type": "call"}]} +{"seq_id": "107877116", "text": "\"\"\" tests base setup.py \"\"\"\n\nfrom os import chdir, environ, path, devnull\nfrom subprocess import call\n\nimport unittest2 as unittest\n\nclass TestSetupPy(unittest.TestCase):\n \"\"\" test base setup.py \"\"\"\n\n def test_sdist(self):\n \"\"\" test sdist makes tarball \"\"\"\n base_dir = path.join(environ[\"PEIS_TOP_LEVEL\"], \"src\", \"base\")\n chdir(base_dir)\n\n call([\"rm\", \"-rf\", \"dist\"])\n # setting the stdout to devnull hides noisy output of sdist\n call([\"python\", \"setup.py\", \"sdist\"], stdout=open(devnull, 'wb'))\n self.assertTrue(path.isfile(\"dist/peisosbase-0.0.tar.gz\"))\n", "sub_path": "test_src/test_base/test_setup.py", "file_name": "test_setup.py", "file_ext": "py", "file_size_in_byte": 611, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "unittest2.TestCase", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 13, "usage_type": "name"}, {"api_name": "os.chdir", "line_number": 14, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 16, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 18, "usage_type": "call"}, {"api_name": "os.devnull", "line_number": 18, "usage_type": "argument"}, {"api_name": "os.path.isfile", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "name"}]} +{"seq_id": "54315609", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Sep 11 16:14:01 2017\r\n\r\n@author: Ekansh\r\n\"\"\"\r\n\r\nimport quandl\r\nimport pandas as pd\r\nimport pickle\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib import style\r\nstyle.use('ggplot')\r\n\r\n\r\napi_key = open('api_key.txt','r').read()\r\ndef fifty_states():\r\n fifty_states = pd.read_html('https://simple.wikipedia.org/wiki/List_of_U.S._states')\r\n return fifty_states[0][1][1:]\r\n\r\n\r\n\r\ndef get_initial_state_data():\r\n states=fifty_states()\r\n main_df = pd.DataFrame()\r\n\r\n for abbv in states:\r\n query = \"FMAC/HPI_\"+str(abbv)\r\n df = quandl.get(query, authtoken=api_key)\r\n df.rename(columns={'Value':str(abbv)} , inplace=True)\r\n df[abbv]=(df[abbv]-df[abbv][0])/df[abbv][0]*100\r\n\r\n if main_df.empty:\r\n main_df = df\r\n else:\r\n main_df = main_df.join(df)\r\n main_df.plot()\r\n save_file=open('fifty_states_pct_change_5.pickle','wb')\r\n pickle.dump(main_df,save_file)\r\n save_file.close()\r\n\r\n#get_initial_state_data()\r\n\r\nHPI_data=pd.read_pickle('fifty_states_pct_change_5.pickle')\r\nHPI_data.plot(legend=None)\r\nplt.show()\r\n", "sub_path": "ad_v5.py", "file_name": "ad_v5.py", "file_ext": "py", "file_size_in_byte": 1133, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "matplotlib.style.use", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.style", "line_number": 13, "usage_type": "name"}, {"api_name": "pandas.read_html", "line_number": 18, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 25, "usage_type": "call"}, {"api_name": "quandl.get", "line_number": 29, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 39, "usage_type": "call"}, {"api_name": "pandas.read_pickle", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}]} +{"seq_id": "639909857", "text": "#!/usr/bin/python\n# -*- coding:utf-8 -*-\nfrom hashlib import sha1\nimport hmac\nimport base64\nimport time\n\n\ndef gen_hmac(data):\n key = \"MONITOR2.0\"\n token = hmac.new(key=key, msg=data, digestmod=sha1).digest()\n token = base64.b64encode(token)\n return token\n\n\ndef gen_server_token(method, now_time, path):\n separator = \"-\"\n data = method + separator + str(now_time) + separator + path\n token = gen_hmac(data)\n return token\n\n\ndef get_auth_header(method, path):\n \"\"\"\n 生成请求头（服务端用不到）\n :param method:\n :param path:\n :return:\n \"\"\"\n separator = \"-\"\n now_time = int(round(time.time() * 1000))\n data = method + separator + str(now_time) + separator + path\n token = gen_hmac(data)\n return {'Date': str(now_time), 'Authorization': token}\n\n\nif __name__ == '__main__':\n gen_hmac('GET-1535959328990-/api/material/crawl/app/list')\n header = get_auth_header('POST', '/api/performad/creative/crawler/upload')\n print(\"Date:%s\" % header['Date'])\n print(\"Authorization:%s\" % header['Authorization'])\n", "sub_path": "monitor_api2/monitor_web/hmac_utils.py", "file_name": "hmac_utils.py", "file_ext": "py", "file_size_in_byte": 1072, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "hmac.new", "line_number": 11, "usage_type": "call"}, {"api_name": "hashlib.sha1", "line_number": 11, "usage_type": "name"}, {"api_name": "base64.b64encode", "line_number": 12, "usage_type": "call"}, {"api_name": "time.time", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "605133324", "text": "from django.core.mail import send_mail\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nfrom django.shortcuts import render, get_object_or_404\nfrom django.views.generic import ListView\n\nfrom .forms import EmailPostForm\nfrom .models import Post\n\n\nclass PostListView(ListView):\n \"\"\"Show All Post with Class-Based View\"\"\"\n queryset = Post.published.all()\n context_object_name = 'posts' # instead of object_list\n paginate_by = 3\n template_name = 'blog/post/list.html'\n\n\ndef post_list(request):\n \"\"\"Show All Post\"\"\"\n object_list = Post.published.all()\n # Instantiate the Paginator class with the number of objects\n # we want to display on each page.\n paginator = Paginator(object_list, 3) # 3 posts in each page\n # indeicate the current page number\n current_page = request.GET.get('page')\n try:\n # Obtain the objects for the desired page\n posts = paginator.page(current_page)\n except PageNotAnInteger:\n # If page is not an integer deliver the first page\n posts = paginator.page(1)\n except EmptyPage:\n # If page is out of range deliver last page of results\n posts = paginator.page(paginator.num_pages)\n\n return render(request,\n 'blog/post/list.html',\n {'page': current_page, # page number\n 'posts': posts}) # retrieved object\n\n\ndef post_detail(request, year, month, day, post):\n \"\"\"Show Desired Post\"\"\"\n post = get_object_or_404(Post,\n slug=post,\n status='published',\n publish__year=year,\n publish__month=month,\n publish__day=day)\n return render(request,\n 'blog/post/detail.html',\n {'post': post})\n\n\ndef post_share(request, post_id):\n # Retrieve post by id\n post = get_object_or_404(Post, id=post_id, status='published')\n sent_by_email = False\n\n if request.method == 'POST':\n # Form was submitted\n form = EmailPostForm(request.POST)\n if form.is_valid():\n # Form fields passed validation\n cd = form.cleaned_data\n print(cd)\n # https://docs.djangoproject.com/en/2.1/ref/request-response/#django.http.HttpRequest.build_absolute_uri\n post_url = request.build_absolute_uri(post.get_absolute_url())\n subject = '{}({})recommends you reading {}'\\\n .format(cd['name'], cd['email'], post.title)\n message = 'Read \"{}\" at {} \\n\\n{}\\'s comments: {}'\\\n .format(post.title,\n post_url,\n cd['name'],\n cd['comments'])\n send_mail(subject, message, 'admin@myblog.com', [cd['to']])\n sent_by_email = True\n else:\n form = EmailPostForm()\n return render(request,\n 'blog/post/share.html',\n {'post': post,\n 'form': form,\n 'sent': sent_by_email})\n", "sub_path": "src/blog/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3079, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.views.generic.ListView", "line_number": 10, "usage_type": "name"}, {"api_name": "models.Post.published.all", "line_number": 12, "usage_type": "call"}, {"api_name": "models.Post.published", "line_number": 12, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 12, "usage_type": "name"}, {"api_name": "models.Post.published.all", "line_number": 20, "usage_type": "call"}, {"api_name": "models.Post.published", "line_number": 20, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 20, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 23, "usage_type": "call"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 29, "usage_type": "name"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 32, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 36, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 44, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 44, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 50, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 57, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 57, "usage_type": "argument"}, {"api_name": "forms.EmailPostForm", "line_number": 62, "usage_type": "call"}, {"api_name": "django.core.mail.send_mail", "line_number": 76, "usage_type": "call"}, {"api_name": "forms.EmailPostForm", "line_number": 79, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 80, "usage_type": "call"}]} +{"seq_id": "80615625", "text": "import mpids.MPInumpy as mpi_np\nimport numpy as np\nfrom mpi4py import MPI\nfrom operations import add, sub, mul, div\n\nif __name__ == '__main__':\n comm = MPI.COMM_WORLD\n rank = comm.Get_rank()\n n_procs = comm.Get_size()\n local_size = 2**16\n size = n_procs * local_size\n iters = 1000\n mpi_np_arr = mpi_np.arange(size, dtype=np.float64)\n\n add_time = add(mpi_np_arr, iters=iters)\n sub_time = sub(mpi_np_arr, iters=iters)\n mul_time = mul(mpi_np_arr, iters=iters)\n div_time = div(mpi_np_arr, iters=iters)\n\n if rank == 0:\n print(\"mpi_np,add,%d,%d,%.9f\" %(n_procs, local_size, add_time))\n print(\"mpi_np,sub,%d,%d,%.9f\" %(n_procs, local_size, sub_time))\n print(\"mpi_np,mul,%d,%d,%.9f\" %(n_procs, local_size, mul_time))\n print(\"mpi_np,div,%d,%d,%.9f\" %(n_procs, local_size, div_time))\n", "sub_path": "MPInumpy/Weak/arithmetic_mpi_np.py", "file_name": "arithmetic_mpi_np.py", "file_ext": "py", "file_size_in_byte": 839, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "mpi4py.MPI.COMM_WORLD", "line_number": 7, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 7, "usage_type": "name"}, {"api_name": "mpids.MPInumpy.arange", "line_number": 13, "usage_type": "call"}, {"api_name": "mpids.MPInumpy", "line_number": 13, "usage_type": "name"}, {"api_name": "numpy.float64", "line_number": 13, "usage_type": "attribute"}, {"api_name": "operations.add", "line_number": 15, "usage_type": "call"}, {"api_name": "operations.sub", "line_number": 16, "usage_type": "call"}, {"api_name": "operations.mul", "line_number": 17, "usage_type": "call"}, {"api_name": "operations.div", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "179618155", "text": "#!/usr/bin/env python\n# coding: utf-8\nimport can\nimport serial\nimport os\nimport time\nimport argparse\n\nHOST = '' # Symbolic name meaning all available interfaces\nPORT = 6666 # Arbitrary non-privileged port\n\nMCM = 0x010\n\nMOVE_LEFT = \"0\"\nMOVE_RIGHT = \"1\"\nMOVE_FORWARD = \"2\"\n\n# Manage arguments used when launching the script\nparser = argparse.ArgumentParser()\nparser.add_argument(\"serial_port\", help=\"serial port of XBee module\")\nargs = parser.parse_args()\n\n# Creates serial link with XBee module\ntry:\n ser = serial.Serial(\n port=args.serial_port,\n baudrate=9600,\n parity=serial.PARITY_NONE,\n stopbits=serial.STOPBITS_ONE,\n bytesize=serial.EIGHTBITS,\n timeout=1\n )\nexcept:\n print(\"Error creating the serial link\")\n\n# Steering angle = 0\nsteer_cmd = 50 | 0x80\n# Stop rear wheels\nmove_cmd = 0 & ~0x80\n\nwhile 1:\n # Setup CAN communication bus\n print('Bring up CAN0....')\n os.system(\"sudo /sbin/ip link set can0 up type can bitrate 400000\")\n time.sleep(0.1)\n\n try:\n bus = can.interface.Bus(channel='can0', bustype='socketcan_native')\n except OSError:\n print('Cannot find PiCAN board.')\n exit()\n\n # Read message received on the XBee\n msg_xbee=ser.readline().strip()\n\n # Convert it to a string\n msg_xbee = msg_xbee.decode(\"utf-8\")\n\n # Turn front wheels to the left and stop rear wheels\n if msg_xbee == MOVE_LEFT:\n steer_cmd = 0 | 0x80\n move_cmd = 0 & ~0x80\n print(\"TURNING LEFT\")\n # Turn front wheels to the right and stop rear wheels\n elif msg_xbee == MOVE_RIGHT:\n steer_cmd = 100 | 0x80\n move_cmd = 0 & ~0x80\n print(\"TURNING RIGHT\")\n # Set front wheels at 0° and move rear wheels forward\n elif msg_xbee == MOVE_FORWARD:\n move_cmd = 50 | 0x80\n steer_cmd = 50 | 0x80\n print(\"GOING FORWARD\")\n elif msg_xbee == \"\":\n pass\n else:\n print(\"Unknown message : {}\".format(msg_xbee))\n\n # Send msg on the CAN bus\n msg = can.Message(arbitration_id=MCM, data=[move_cmd, move_cmd, steer_cmd, 0, 0, 0, 0, 0], extended_id=False)\n bus.send(msg)\n", "sub_path": "raspberry/TestCommandes/XbeeReceive.py", "file_name": "XbeeReceive.py", "file_ext": "py", "file_size_in_byte": 2160, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 19, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 25, "usage_type": "call"}, {"api_name": "serial.PARITY_NONE", "line_number": 28, "usage_type": "attribute"}, {"api_name": "serial.STOPBITS_ONE", "line_number": 29, "usage_type": "attribute"}, {"api_name": "serial.EIGHTBITS", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 44, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 45, "usage_type": "call"}, {"api_name": "can.interface.Bus", "line_number": 48, "usage_type": "call"}, {"api_name": "can.interface", "line_number": 48, "usage_type": "attribute"}, {"api_name": "can.Message", "line_number": 80, "usage_type": "call"}]} +{"seq_id": "280352050", "text": "#Step2, Load background and cargo\n\n# 1.1 - Import library\nimport pygame\nimport random\nimport math\npygame.init()\nwidth, height = 740, 480\nscreen=pygame.display.set_mode((width, height))\nkeep_going = True\n\n#3.1 initial the value of Key and position\nkey_up=key_down=key_left=key_right = False\nplayer_pos=[130,100] #change the position to list\n\nplayer = pygame.image.load(\"images/player.png\")\n\n#2.1 load more images\nbackground = pygame.image.load(\"images/sky.jpg\")\nbackground = pygame.transform.scale(background, (width, height))\ncargo = pygame.image.load(\"images/airballoon.png\")\n\n\nbullets=[]\nbullet = pygame.image.load(\"images/bullet.png\")\n\n#5 initial enemies\nenemyImg = pygame.image.load(\"images/enemy2.png\")\nenemyImg=pygame.transform.scale(enemyImg, (100, 33)).convert_alpha() #use the convert_alpha() method after loading so that the image has per pixel transparency.\nenemys=[[640,100]]\nenemySpeed=-0.3\nenemyMaxnumber=5 #how many enemies in the screen same time\n\n#7 initial load explosion animaton images\nexplosions=[] # store explosion location and img index [(x,y),i,t] \nexplosion_anim=[] #store img for animation\nBLACK = (0, 0, 0)\nexplosion_time=60\nfor i in range(9):\n filename = 'Explosion0{}.png'.format(i)\n img = pygame.image.load(\"images/\"+ filename).convert() # convert will create a copy that will draw more quickly on the screen.\n img.set_colorkey(BLACK)\n img= pygame.transform.scale(img, (75, 75))\n explosion_anim.append(img)\n \n\n\n\n \nwhile keep_going:\n\n screen.fill(0)\n \n #2.2 load the background\n screen.blit(background,(0,0))\n # if you image is small, you need use double loop to fill the background\n #for x in range( int(width/background.get_width())+1):\n # for y in range(int(height/background.get_height())+1):\n # screen.blit(background,(x*100,y*100))\n \n # 2.3 load the cargo\n screen.blit(cargo,(0,30))\n screen.blit(cargo,(0,135))\n screen.blit(cargo,(0,240))\n screen.blit(cargo,(0,345))\n\n \n#3.2 set player position use player_pos\n screen.blit(player, player_pos)\n\n \n#4 - Draw bullet\n \n for bulletPos in bullets:\n enemy_index=0\n bulletPos[0]=bulletPos[0]+2\n screen.blit(bullet,bulletPos)\n\n #remove bullet if out the screen\n if bulletPos[0]<-64 or bulletPos[0]>640 or bulletPos[1]<-64 or bulletPos[1]>480:\n bullets.pop(enemy_index) #remove from list\n enemy_index+=1\n\n\n #5 Draw enemies random time and only keep 5 enemies in screen\n if(random.randint(1,100)<3 and len(enemys)0:\n player_pos[1]-=1\n elif key_down and player_pos[1]0:\n player_pos[0]-=1\n elif key_right and player_pos[0] 30:\n title = title[:27] + '...'\n comments = entry.get_n_comments()\n if comments:\n return '%s (%i)' % (title, comments)\n return title\n get_title_comments.short_description = _('title')\n\n def get_authors(self, entry):\n \"\"\"Return the authors in HTML\"\"\"\n try:\n authors = ['%s' %\n (reverse('zinnia:author_detail',\n args=[author.username]), author.username) \\\n for author in entry.authors.all()]\n except NoReverseMatch:\n authors = [author.username for author in entry.authors.all()]\n return ', '.join(authors)\n get_authors.allow_tags = True\n get_authors.short_description = _('author(s)')\n\n def get_categories(self, entry):\n \"\"\"Return the categories linked in HTML\"\"\"\n try:\n categories = ['%s' %\n (category.get_absolute_url(), category.title)\n for category in entry.categories.all()]\n except NoReverseMatch:\n categories = [category.title \\\n for category in entry.categories.all()]\n return ', '.join(categories)\n get_categories.allow_tags = True\n get_categories.short_description = _('category(s)')\n\n def get_tags(self, entry):\n \"\"\"Return the tags linked in HTML\"\"\"\n try:\n return ', '.join(['%s' %\n (reverse('zinnia:tagged_entry_list', args=[tag]),\n tag) \\\n for tag in entry.tags.replace(',', '').split()])\n except NoReverseMatch:\n return ', '.join(entry.tags.replace(',', '').split())\n get_tags.allow_tags = True\n get_tags.short_description = _('tag(s)')\n\n def get_sites(self, entry):\n \"\"\"Return the sites linked in HTML\"\"\"\n return ', '.join(['' \\\n '%(name)s' %\n site.__dict__ for site in entry.sites.all()])\n get_sites.allow_tags = True\n get_sites.short_description = _('site(s)')\n\n def get_link(self, entry):\n \"\"\"Return a formated link to the entry\"\"\"\n return _('View') \\\n % entry.get_absolute_url()\n get_link.allow_tags = True\n get_link.short_description = _('View on site')\n\n # Custom Methods\n def save_model(self, request, entry, form, change):\n \"\"\"Save the authors, update time, make an excerpt\"\"\"\n if not form.cleaned_data.get('excerpt'):\n entry.excerpt = truncate_words(strip_tags(entry.content), 50)\n\n if not request.user.has_perm('zinnia.can_change_author'):\n form.cleaned_data['authors'] = entry.authors.all()\n\n if not form.cleaned_data.get('authors'):\n form.cleaned_data['authors'].append(request.user)\n\n entry.last_update = datetime.now()\n entry.save()\n\n if entry.is_visible() and SAVE_PING_DIRECTORIES:\n self.ping_directories(request, [entry])\n\n def queryset(self, request):\n \"\"\"Make special filtering by user permissions\"\"\"\n queryset = super(EntryAdmin, self).queryset(request)\n if request.user.has_perm('zinnia.can_view_all'):\n return queryset\n return request.user.entry_set.all()\n\n def formfield_for_manytomany(self, db_field, request, **kwargs):\n \"\"\"Filters the disposable authors\"\"\"\n if db_field.name == 'authors' and \\\n not request.user.has_perm('zinnia.can_change_author'):\n kwargs['queryset'] = User.objects.filter(pk=request.user.pk)\n return db_field.formfield(**kwargs)\n return super(EntryAdmin, self).formfield_for_manytomany(db_field,\n request, **kwargs)\n\n def get_actions(self, request):\n \"\"\"Define user actions by permissions\"\"\"\n actions = super(EntryAdmin, self).get_actions(request)\n if not request.user.has_perm('zinnia.can_change_author') \\\n or not request.user.has_perm('zinnia.can_view_all'):\n del actions['make_mine']\n if not PING_DIRECTORIES:\n del actions['ping_directories']\n return actions\n\n # Custom Actions\n def make_mine(self, request, queryset):\n \"\"\"Set the entries to the user\"\"\"\n for entry in queryset:\n if request.user not in entry.authors.all():\n entry.authors.add(request.user)\n make_mine.short_description = _('Set the entries to the user')\n\n def make_published(self, request, queryset):\n \"\"\"Set entries selected as published\"\"\"\n queryset.update(status=PUBLISHED)\n make_published.short_description = _('Set entries selected as published')\n\n def make_hidden(self, request, queryset):\n \"\"\"Set entries selected as hidden\"\"\"\n queryset.update(status='hidden')\n make_hidden.short_description = _('Set entries selected as hidden')\n\n def close_comments(self, request, queryset):\n \"\"\"Close the comments for selected entries\"\"\"\n queryset.update(comment_enabled=False)\n close_comments.short_description = _('Close the comments' \\\n ' for selected entries')\n\n def ping_directories(self, request, queryset):\n \"\"\"Ping Directories for selected entries\"\"\"\n success = 0\n for directory in PING_DIRECTORIES:\n pinger = DirectoryPinger(directory)\n for entry in queryset:\n response = pinger.ping(entry)\n if not response.get('flerror', True):\n success += 1\n self.message_user(request, _('%i directories succesfully pinged.') \\\n % success)\n ping_directories.short_description = _('Ping Directories for selected' \\\n ' entries')\n\nadmin.site.register(Category, CategoryAdmin)\nadmin.site.register(Entry, EntryAdmin)\n", "sub_path": "admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 8418, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "django.contrib.admin.ModelAdmin", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 19, "usage_type": "name"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 27, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 27, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 29, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 30, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 36, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 60, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 66, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.NoReverseMatch", "line_number": 69, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 73, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.NoReverseMatch", "line_number": 81, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 86, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 92, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.NoReverseMatch", "line_number": 95, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 98, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 106, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 110, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 113, "usage_type": "call"}, {"api_name": "django.utils.text.truncate_words", "line_number": 119, "usage_type": "call"}, {"api_name": "django.utils.html.strip_tags", "line_number": 119, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 127, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 127, "usage_type": "name"}, {"api_name": "zinnia.settings.SAVE_PING_DIRECTORIES", "line_number": 130, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 144, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 144, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 144, "usage_type": "name"}, {"api_name": "zinnia.settings.PING_DIRECTORIES", "line_number": 155, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 165, "usage_type": "call"}, {"api_name": "zinnia.managers.PUBLISHED", "line_number": 169, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 170, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 175, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 180, "usage_type": "call"}, {"api_name": "zinnia.settings.PING_DIRECTORIES", "line_number": 186, "usage_type": "name"}, {"api_name": "zinnia.ping.DirectoryPinger", "line_number": 187, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 192, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 194, "usage_type": "call"}, {"api_name": "django.contrib.admin.site.register", "line_number": 197, "usage_type": "call"}, {"api_name": "zinnia.models.Category", "line_number": 197, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 197, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 197, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 198, "usage_type": "call"}, {"api_name": "zinnia.models.Entry", "line_number": 198, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 198, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 198, "usage_type": "name"}]} +{"seq_id": "611471783", "text": "# coding:utf-8\nimport socket\nimport os\nimport json\nimport struct\nimport hashlib\nsock = socket.socket()\nsock.connect(('127.0.0.1', 9999))\n\n\nwhile True:\n cmd = input(\"请输入命令:\")\n if cmd == 'exit':\n break\n\n action, filename = cmd.strip().split(\" \")\n filesize = os.path.getsize(filename)\n if action == 'put':\n file_info = {\n \"action\": action, # put\n \"filename\": filename, # 文件名\n \"filesize\": filesize, # 文件大小\n }\n file_info_json = json.dumps(file_info).encode('utf8')\n ret = struct.pack('i', len(file_info_json))\n print(len(ret))\n\n # 发送file_info_json的打包长度\n sock.send(ret)\n # 发送file_info_json字节串\n sock.send(file_info_json)\n\n md5 = hashlib.md5()\n", "sub_path": "day30/TFP_WORK/client/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 812, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "socket.socket", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path.getsize", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 24, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 25, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "119625097", "text": "# import the necessary packages\r\nimport cv2\r\nimport glob\r\nfrom collections import defaultdict\r\nfrom os.path import basename\r\nfrom Utils import *\r\n\r\n# initialize the list of reference points and boolean indicating\r\n# whether cropping is being performed or not\r\nrefPt = []\r\ncropping = False\r\ncount = 0\r\npoints = []\r\n\r\n\r\ndef click_mark(event, x, y, flags, param):\r\n # grab references to the global variables\r\n global refPt, cropping, count, points\r\n\r\n # if the left mouse button was clicked, record the starting\r\n # (x, y) coordinates and indicate that cropping is being\r\n # performed\r\n if event == cv2.EVENT_LBUTTONDOWN:\r\n points.append((x, y))\r\n count += 1\r\n\r\n\r\ndirPath = r\"D:\\Users\\Hadas\\Desktop\\CompoundEmotion\"\r\npaths = glob.glob(dirPath + r\"\\05*\")\r\ndict = defaultdict(list)\r\nfor p in paths:\r\n # load the image, clone it, and setup the mouse callback function\r\n print(p)\r\n image = cv2.imread(p)\r\n clone = image.copy()\r\n cv2.namedWindow(\"image\")\r\n cv2.setMouseCallback(\"image\", click_mark)\r\n count = 0\r\n # keep looping until the 'q' key is pressed\r\n while True:\r\n # display the image and wait for a keypress\r\n cv2.imshow(\"image\", image)\r\n key = cv2.waitKey(1) & 0xFF\r\n\r\n # if the 'r' key is pressed, reset the cropping region\r\n if key == ord(\"r\"):\r\n image = clone.copy()\r\n\r\n # if the 'c' key is pressed, break from the loop\r\n elif key == ord(\"c\"):\r\n break\r\n\r\n if (count == 1):\r\n print(points)\r\n dict[basename(p)[:6]] = points\r\n points=[]\r\n break\r\n\r\nSavePickle(\"foreheads_5\", dict)\r\n # if there are two reference points, then crop the region of interest\r\n # from teh image and display it\r\n\r\n# if len(refPt) == 2:\r\n# roi = clone[refPt[0][1]:refPt[1][1], refPt[0][0]:refPt[1][0]]\r\n# cv2.imshow(\"ROI\", roi)\r\n# cv2.waitKey(0)\r\n\r\n# close all open windows\r\ncv2.destroyAllWindows()", "sub_path": "ColorInEmotion/GetForeheads.py", "file_name": "GetForeheads.py", "file_ext": "py", "file_size_in_byte": 1974, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "cv2.EVENT_LBUTTONDOWN", "line_number": 23, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 29, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.setMouseCallback", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 69, "usage_type": "call"}]} +{"seq_id": "4913685", "text": "_author_ = 'Miguel Veliz'\n\n#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\n fibonacci_web_service.py\n ~~~~~~~~~~~~~~\n\n RESTful web service that accepts a positive number, \n as input and returns the first n Fibonacci numbers\n\n\"\"\"\n\nfrom flask import Flask\nfrom flask import request\n\napp = Flask(__name__)\n\nfibonacci_number_list = []\n\n#: Function to Calculate Fibonacci numbers\n\n\ndef calculate_fibonacci(n):\n \"\"\"This Function takes a number and calculate its\n fibonacci, it number is 0 then returns 0, if number\n is 1 then returns 1, if number is greater than 1 then\n Function(n-1)+Function(n-2)\"\"\"\n\n if n == 0:\n return 0\n elif n == 1:\n return 1\n else:\n return calculate_fibonacci(n-1)+calculate_fibonacci(n-2)\n\n#: create the / end point\n\n\n@app.route('/', methods=['GET'])\n#: define main function for endpoint\ndef return_fibonacci():\n \"\"\"This Function calls the calculate_fibonacci function\n builds a list with each result, and prints it out in a\n formatted way \"\"\"\n \n#: 127.0.0.1:5000/?n='int'\n number = int(request.args.get('n'))\n \"\"\"The format of the web address is 127.0.0.1:5000/?n='int'\n where int is a integer for example: 127.0.0.1:5000/?n=10\"\"\"\n #: check for negative number\n if number < 0:\n return \"Invalid Number, please enter a positive number\"\n else:\n #: clear list for multiple calls\n fibonacci_number_list.clear()\n\n #: Calculates the fibonacci number and adds to a list\n for x in range(number+1):\n fibonacci_number_list.append(calculate_fibonacci(x))\n\n #: returns the list of fibonacci number formated.\n return str(fibonacci_number_list).strip('[]')\n\n\n#: runs the application and defines the port number\napp.run(host='0.0.0.0', port=5000)\n\n", "sub_path": "fibonacci_web_service.py", "file_name": "fibonacci_web_service.py", "file_ext": "py", "file_size_in_byte": 1798, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "flask.Flask", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 48, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 48, "usage_type": "name"}]} +{"seq_id": "567918662", "text": "# -*- coding: utf-8 -*-\n\n# Copyright (c) 2016 - 2019 Detlev Offenbach \n#\n\n\"\"\"\nModule implementing the TLD Extractor.\n\"\"\"\n\n#\n# This is a Python port of the TLDExtractor of Qupzilla\n# Copyright (C) 2014 Razi Alavizadeh \n#\n\n\nimport collections\n\nfrom PyQt5.QtCore import QObject, QUrl, QFile, QFileInfo, QRegExp, qWarning\n\nfrom E5Gui import E5MessageBox\n\nfrom .data import tld_rc # __IGNORE_WARNING__\n\n\nclass E5TldHostParts(object):\n \"\"\"\n Class implementing the host parts helper.\n \"\"\"\n def __init__(self):\n \"\"\"\n Constructor\n \"\"\"\n self.host = \"\"\n self.tld = \"\"\n self.domain = \"\"\n self.registrableDomain = \"\"\n self.subdomain = \"\"\n\n\nclass E5TldExtractor(QObject):\n \"\"\"\n Class implementing the TLD Extractor.\n \n Note: The module function instance() should be used to get a reference\n to a global object to avoid overhead.\n \"\"\"\n def __init__(self, withPrivate=False, parent=None):\n \"\"\"\n Constructor\n \n @param withPrivate flag indicating to load private TLDs as well\n @type bool\n @param parent reference to the parent object\n @type QObject\n \"\"\"\n super(E5TldExtractor, self).__init__(parent)\n \n self.__withPrivate = withPrivate\n self.__dataFileName = \"\"\n self.__dataSearchPaths = []\n \n self.__tldDict = collections.defaultdict(list)\n # dict with list of str as values\n \n self.setDataSearchPaths()\n \n def isDataLoaded(self):\n \"\"\"\n Public method to check, if the TLD data ia already loaded.\n \n @return flag indicating data is loaded\n @rtype bool\n \"\"\"\n return bool(self.__tldDict)\n \n def tld(self, host):\n \"\"\"\n Public method to get the top level domain for a host.\n \n @param host host name to get TLD for\n @type str\n @return TLD for host\n @rtype str\n \"\"\"\n if not host or host.startswith(\".\"):\n return \"\"\n \n cleanHost = self.__normalizedHost(host)\n \n tldPart = cleanHost[cleanHost.rfind(\".\") + 1:]\n cleanHost = bytes(QUrl.toAce(cleanHost)).decode(\"utf-8\")\n \n self.__loadData()\n \n if tldPart not in self.__tldDict:\n return tldPart\n \n tldRules = self.__tldDict[tldPart][:]\n \n if tldPart not in tldRules:\n tldRules.append(tldPart)\n \n maxLabelCount = 0\n isWildcardTLD = False\n \n for rule in tldRules:\n labelCount = rule.count(\".\") + 1\n \n if rule.startswith(\"!\"):\n rule = rule[1:]\n \n rule = bytes(QUrl.toAce(rule)).decode(\"utf-8\")\n \n # matches with exception TLD\n if cleanHost.endswith(rule):\n tldPart = rule[rule.find(\".\") + 1:]\n break\n \n if rule.startswith(\"*\"):\n rule = rule[1:]\n \n if rule.startswith(\".\"):\n rule = rule[1:]\n \n isWildcardTLD = True\n else:\n isWildcardTLD = False\n \n rule = bytes(QUrl.toAce(rule)).decode(\"utf-8\")\n testRule = \".\" + rule\n testUrl = \".\" + cleanHost\n \n if labelCount > maxLabelCount and testUrl.endswith(testRule):\n tldPart = rule\n maxLabelCount = labelCount\n \n if isWildcardTLD:\n temp = cleanHost\n temp = temp[:temp.rfind(tldPart)]\n \n if temp.endswith(\".\"):\n temp = temp[:-1]\n \n temp = temp[temp.rfind(\".\") + 1:]\n \n if temp:\n tldPart = temp + \".\" + rule\n else:\n tldPart = rule\n \n temp = self.__normalizedHost(host)\n tldPart = \".\".join(\n temp.split(\".\")[temp.count(\".\") - tldPart.count(\".\"):])\n \n return tldPart\n \n def domain(self, host):\n \"\"\"\n Public method to get the domain for a host.\n \n @param host host name to get the domain for\n @type str\n @return domain for host\n @rtype str\n \"\"\"\n tldPart = self.tld(host)\n \n return self.__domainHelper(host, tldPart)\n \n def registrableDomain(self, host):\n \"\"\"\n Public method to get the registrable domain for a host.\n \n @param host host name to get the registrable domain for\n @type str\n @return registrable domain for host\n @rtype str\n \"\"\"\n tldPart = self.tld(host)\n \n return self.__registrableDomainHelper(\n self.__domainHelper(host, tldPart), tldPart)\n \n def subdomain(self, host):\n \"\"\"\n Public method to get the subdomain for a host.\n \n @param host host name to get the subdomain for\n @type str\n @return subdomain for host\n @rtype str\n \"\"\"\n return self.__subdomainHelper(host, self.registrableDomain(host))\n \n def splitParts(self, host):\n \"\"\"\n Public method to split a host address into its parts.\n \n @param host host address to be split\n @type str\n @return splitted host address\n @rtype E5TldHostParts\n \"\"\"\n hostParts = E5TldHostParts()\n hostParts.host = host\n hostParts.tld = self.tld(host)\n hostParts.domain = self.__domainHelper(host, hostParts.tld)\n hostParts.registrableDomain = self.__registrableDomainHelper(\n hostParts.domain, hostParts.tld)\n hostParts.subdomain = self.__subdomainHelper(\n host, hostParts.registrableDomain)\n \n return hostParts\n \n def dataSearchPaths(self):\n \"\"\"\n Public method to get the search paths for the TLD data file.\n \n @return search paths for the TLD data file\n @rtype list of str\n \"\"\"\n return self.__dataSearchPaths[:]\n \n def setDataSearchPaths(self, searchPaths=None):\n \"\"\"\n Public method to set the search paths for the TLD data file.\n \n @param searchPaths search paths for the TLD data file or None,\n if the default search paths shall be set\n @type list of str\n \"\"\"\n if searchPaths:\n self.__dataSearchPaths = searchPaths[:]\n self.__dataSearchPaths.extend(self.__defaultDataSearchPaths())\n else:\n self.__dataSearchPaths = self.__defaultDataSearchPaths()[:]\n \n # remove duplicates\n paths = []\n for p in self.__dataSearchPaths:\n if p not in paths:\n paths.append(p)\n self.__dataSearchPaths = paths\n \n def __defaultDataSearchPaths(self):\n \"\"\"\n Private method to get the default search paths for the TLD data file.\n \n @return default search paths for the TLD data file\n @rtype list of str\n \"\"\"\n return [\":\"]\n \n def getTldDownloadUrl(self):\n \"\"\"\n Public method to get the TLD data file download URL.\n \n @return download URL\n @rtype QUrl\n \"\"\"\n return QUrl(\n \"http://mxr.mozilla.org/mozilla-central/source/netwerk/dns/\"\n \"effective_tld_names.dat?raw=1\")\n \n def __loadData(self):\n \"\"\"\n Private method to load the TLD data.\n \"\"\"\n if self.isDataLoaded():\n return\n \n dataFileName = \"\"\n parsedDataFileExist = False\n \n for path in self.__dataSearchPaths:\n dataFileName = (\n QFileInfo(path + \"/effective_tld_names.dat\").absoluteFilePath()\n )\n if QFileInfo(dataFileName).exists():\n parsedDataFileExist = True\n break\n \n if not parsedDataFileExist:\n tldDataFileDownloadLink = (\n \"http://mxr.mozilla.org/mozilla-central/source/netwerk/dns/\"\n \"effective_tld_names.dat?raw=1\"\n )\n E5MessageBox.information(\n None,\n self.tr(\"TLD Data File not found\"),\n self.tr(\"\"\"

The file 'effective_tld_names.dat' was not\"\"\"\n \"\"\" found!
You can download it from \"\"\"\n \"\"\"'here' to one of the\"\"\"\n \"\"\" following paths:

    {1}
\"\"\").format(\n tldDataFileDownloadLink,\n \"\".join([\"
  • {0}
    • \".format(p)\n for p in self.__dataSearchPaths]))\n )\n return\n \n self.__dataFileName = dataFileName\n if not self.__parseData(dataFileName,\n loadPrivateDomains=self.__withPrivate):\n qWarning(\n \"E5TldExtractor: There are some parse errors for file: {0}\"\n .format(dataFileName))\n \n def __parseData(self, dataFile, loadPrivateDomains=False):\n \"\"\"\n Private method to parse TLD data.\n \n @param dataFile name of the file containing the TLD data\n @type str\n @param loadPrivateDomains flag indicating to load private domains\n @type bool\n @return flag indicating success\n @rtype bool\n \"\"\"\n # start with a fresh dictionary\n self.__tldDict = collections.defaultdict(list)\n \n file = QFile(dataFile)\n \n if not file.open(QFile.ReadOnly | QFile.Text):\n return False\n \n seekToEndOfPrivateDomains = False\n \n while not file.atEnd():\n line = bytes(file.readLine()).decode(\"utf-8\").strip()\n if not line:\n continue\n \n if line.startswith(\".\"):\n line = line[1:]\n \n if line.startswith(\"//\"):\n if \"===END PRIVATE DOMAINS===\" in line:\n seekToEndOfPrivateDomains = False\n \n if (\n not loadPrivateDomains and\n \"===BEGIN PRIVATE DOMAINS===\" in line\n ):\n seekToEndOfPrivateDomains = True\n \n continue\n \n if seekToEndOfPrivateDomains:\n continue\n \n # only data up to the first whitespace is used\n line = line.split(None, 1)[0]\n \n if \".\" not in line:\n self.__tldDict[line].append(line)\n else:\n key = line[line.rfind(\".\") + 1:]\n self.__tldDict[key].append(line)\n \n return self.isDataLoaded()\n \n def __domainHelper(self, host, tldPart):\n \"\"\"\n Private method to get the domain name without TLD.\n \n @param host host address\n @type str\n @param tldPart TLD part of the host address\n @type str\n @return domain name\n @rtype str\n \"\"\"\n if not host or not tldPart:\n return \"\"\n \n temp = self.__normalizedHost(host)\n temp = temp[:temp.rfind(tldPart)]\n \n if temp.endswith(\".\"):\n temp = temp[:-1]\n \n return temp[temp.rfind(\".\") + 1:]\n \n def __registrableDomainHelper(self, domainPart, tldPart):\n \"\"\"\n Private method to get the registrable domain (i.e. domain plus TLD).\n \n @param domainPart domain part of a host address\n @type str\n @param tldPart TLD part of a host address\n @type str\n @return registrable domain name\n @rtype str\n \"\"\"\n if not tldPart or not domainPart:\n return \"\"\n else:\n return \"{0}.{1}\".format(domainPart, tldPart)\n \n def __subdomainHelper(self, host, registrablePart):\n \"\"\"\n Private method to get the subdomain of a host address (i.e. domain part\n without the registrable domain name).\n \n @param host host address\n @type str\n @param registrablePart registrable domain part of the host address\n @type str\n @return subdomain name\n @rtype str\n \"\"\"\n if not host or not registrablePart:\n return \"\"\n \n subdomain = self.__normalizedHost(host)\n \n subdomain = subdomain[:subdomain.rfind(registrablePart)]\n \n if subdomain.endswith(\".\"):\n subdomain = subdomain[:-1]\n \n return subdomain\n \n def __normalizedHost(self, host):\n \"\"\"\n Private method to get the normalized host for a host address.\n \n @param host host address to be normalized\n @type str\n @return normalized host address\n @rtype str\n \"\"\"\n return host.lower()\n \n #################################################################\n ## Methods below are for testing purposes\n #################################################################\n \n def test(self):\n \"\"\"\n Public method to execute the tests.\n \n @return flag indicating the test result\n @rtype bool\n \"\"\"\n self.__withPrivate = True\n self.__loadData()\n if not self.__tldDict:\n return False\n \n testDataFileName = \"\"\n testDataFileExist = False\n \n for path in self.__dataSearchPaths:\n testDataFileName = (\n QFileInfo(path + \"/test_psl.txt\").absoluteFilePath()\n )\n if QFileInfo(testDataFileName).exists():\n testDataFileExist = True\n break\n \n if not testDataFileExist:\n testFileDownloadLink = (\n \"http://mxr.mozilla.org/mozilla-central/source/netwerk/test/\"\n \"unit/data/test_psl.txt?raw=1\"\n )\n E5MessageBox.information(\n None,\n self.tr(\"TLD Data File not found\"),\n self.tr(\"\"\"

    The file 'test_psl.txt' was not found!\"\"\"\n \"\"\"
    You can download it from '\"\"\"\n \"\"\"here' to one of the following\"\"\"\n \"\"\" paths:

      {1}
    \"\"\").format(\n testFileDownloadLink,\n \"\".join([\"
  • {0}
    • \".format(p)\n for p in self.__dataSearchPaths]))\n )\n return False\n \n file = QFile(testDataFileName)\n \n if not file.open(QFile.ReadOnly | QFile.Text):\n return False\n \n testRegExp = QRegExp(\n \"checkPublicSuffix\\\\(('([^']+)'|null), ('([^']+)'|null)\\\\);\")\n allTestSuccess = True\n \n while not file.atEnd():\n line = bytes(file.readLine()).decode(\"utf-8\").strip()\n if not line or line.startswith(\"//\"):\n continue\n \n if testRegExp.indexIn(line) == -1:\n allTestSuccess = False\n else:\n hostName = testRegExp.cap(2)\n registrableName = testRegExp.cap(4)\n \n if not self.__checkPublicSuffix(hostName, registrableName):\n allTestSuccess = False\n \n if allTestSuccess:\n qWarning(\"E5TldExtractor: Test passed successfully.\")\n else:\n qWarning(\"E5TldExtractor: Test finished with some errors!\")\n \n # reset the TLD dictionary\n self.__tldDict = collections.defaultdict(list)\n \n return allTestSuccess\n \n def __checkPublicSuffix(self, host, registrableName):\n \"\"\"\n Private method to test a host name against a registrable name.\n \n @param host host name to test\n @type str\n @param registrableName registrable domain name to test against\n @type str\n @return flag indicating the check result\n @rtype bool\n \"\"\"\n regName = self.registrableDomain(host)\n if regName != registrableName:\n qWarning(\n \"E5TldExtractor Test Error: hostName: {0}\\n\"\n \" Correct registrableName: {1}\\n\"\n \" Calculated registrableName: {2}\".format(\n host, registrableName, regName))\n return False\n \n return True\n\n\n_TLDExtractor = None\n\n\ndef instance(withPrivate=False):\n \"\"\"\n Global function to get a reference to the TLD extractor and create it, if\n it hasn't been yet.\n \n @param withPrivate flag indicating to load private TLDs as well\n @type bool\n @return reference to the zoom manager object\n @rtype E5TldExtractor\n \"\"\"\n global _TLDExtractor\n \n if _TLDExtractor is None:\n _TLDExtractor = E5TldExtractor(withPrivate=withPrivate)\n \n return _TLDExtractor\n", "sub_path": "PYTHON/Python_GUI/eric6-19.11/eric/eric6/E5Network/E5TldExtractor.py", "file_name": "E5TldExtractor.py", "file_ext": "py", "file_size_in_byte": 17241, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "PyQt5.QtCore.QObject", "line_number": 40, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 62, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QUrl.toAce", "line_number": 91, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QUrl", "line_number": 91, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QUrl.toAce", "line_number": 112, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QUrl", "line_number": 112, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QUrl.toAce", "line_number": 129, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QUrl", "line_number": 129, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QUrl", "line_number": 261, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QFileInfo", "line_number": 277, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QFileInfo", "line_number": 279, "usage_type": "call"}, {"api_name": "E5Gui.E5MessageBox.information", "line_number": 288, "usage_type": "call"}, {"api_name": "E5Gui.E5MessageBox", "line_number": 288, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.qWarning", "line_number": 304, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 320, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QFile", "line_number": 322, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QFile.ReadOnly", "line_number": 324, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.QFile", "line_number": 324, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QFile.Text", "line_number": 324, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.QFileInfo", "line_number": 457, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QFileInfo", "line_number": 459, "usage_type": "call"}, {"api_name": "E5Gui.E5MessageBox.information", "line_number": 468, "usage_type": "call"}, {"api_name": "E5Gui.E5MessageBox", "line_number": 468, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QFile", "line_number": 481, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QFile.ReadOnly", "line_number": 483, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.QFile", "line_number": 483, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QFile.Text", "line_number": 483, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.QRegExp", "line_number": 486, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.qWarning", "line_number": 505, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.qWarning", "line_number": 507, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 510, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.qWarning", "line_number": 527, "usage_type": "call"}]} +{"seq_id": "535171971", "text": "import torch.nn as nn\nimport torch.nn.functional as F\n\nfrom . import utils\n\n\nclass LinearNet(utils.ReparamModule):\n def __init__(self, state):\n super(LinearNet, self).__init__()\n self.fc = nn.Linear(2, 1 if state.num_classes <= 2\n else state.num_classes, bias=True)\n self.l2 = state.L2_coef\n\n def forward(self, x):\n out = self.fc(x)\n if self.training:\n for p in self.parameters():\n out = out + self.l2*(p**2).sum()\n return out\n\n\nclass NonLinearNet(utils.ReparamModule):\n def __init__(self, state, mid_sz=10):\n super(NonLinearNet, self).__init__()\n self.fc1 = nn.Linear(2, mid_sz)\n self.fc2 = nn.Linear(mid_sz, 1 if state.num_classes <= 2\n else state.num_classes)\n\n def forward(self, x):\n out = F.relu(self.fc1(x), inplace=True)\n out = self.fc2(out)\n return out\n\n\nclass MoreNonLinearNet(utils.ReparamModule):\n def __init__(self, state, mid_sz=10):\n super(MoreNonLinearNet, self).__init__()\n self.fc1 = nn.Linear(2, mid_sz)\n self.fc2 = nn.Linear(mid_sz, mid_sz if state.num_classes <= 2\n else state.num_classes)\n self.fc3 = nn.Linear(mid_sz, mid_sz if state.num_classes <= 2\n else state.num_classes)\n self.fc4 = nn.Linear(mid_sz, 1 if state.num_classes <= 2\n else state.num_classes)\n\n def forward(self, x):\n out = F.relu(self.fc1(x), inplace=True)\n out = F.relu(self.fc2(out), inplace=True)\n out = F.relu(self.fc3(out), inplace=True)\n out = self.fc4(out)\n return out\n", "sub_path": "networks/networks.py", "file_name": "networks.py", "file_ext": "py", "file_size_in_byte": 1702, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "torch.nn.Linear", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 10, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 38, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 39, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 43, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 47, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 49, "usage_type": "name"}]} +{"seq_id": "590343192", "text": "from turtle import Turtle, Screen\nfrom paddle import Paddle\nfrom ball import Ball\nfrom scoreboard import Scoreboard\nimport time\n\nWIDTH = 800\nHEIGHT = 600\n\nscreen = Screen()\n\nscreen.setup(WIDTH + 4, HEIGHT + 8)\nscreen.bgcolor(\"black\")\nscreen.title(\"Pong\")\nscreen.tracer(0)\n\nr_paddle = Paddle((350, 0))\nl_paddle = Paddle((-350,0))\nball = Ball()\nscoreboard = Scoreboard()\n\nscreen.listen()\nscreen.onkey(r_paddle.go_up, \"Up\")\nscreen.onkey(r_paddle.go_down, \"Down\")\nscreen.onkey(l_paddle.go_up, \"w\")\nscreen.onkey(l_paddle.go_down, \"s\")\n\n\ngame_is_on = True\n\nwhile game_is_on:\n # time.sleep(0.07)\n screen.update()\n ball.move()\n\n #Detect collision with r_paddle\n if ball.distance(r_paddle) < 70 and ball.xcor() > 340:\n ball.bounce_x()\n elif ball.distance(l_paddle) < 70 and ball.xcor() < -340:\n ball.bounce_x()\n\n #if ball goes out of bounds (paddle misses it)\n if ball.xcor() > 380:\n ball.reset_position()\n scoreboard.add_l_score()\n\n if ball.xcor() < -380:\n ball.reset_position()\n scoreboard.add_r_score()\n\nscreen.exitonclick()", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1087, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "turtle.Screen", "line_number": 10, "usage_type": "call"}, {"api_name": "paddle.Paddle", "line_number": 17, "usage_type": "call"}, {"api_name": "paddle.Paddle", "line_number": 18, "usage_type": "call"}, {"api_name": "ball.Ball", "line_number": 19, "usage_type": "call"}, {"api_name": "scoreboard.Scoreboard", "line_number": 20, "usage_type": "call"}, {"api_name": "ball.move", "line_number": 34, "usage_type": "call"}, {"api_name": "ball.distance", "line_number": 37, "usage_type": "call"}, {"api_name": "ball.xcor", "line_number": 37, "usage_type": "call"}, {"api_name": "ball.bounce_x", "line_number": 38, "usage_type": "call"}, {"api_name": "ball.distance", "line_number": 39, "usage_type": "call"}, {"api_name": "ball.xcor", "line_number": 39, "usage_type": "call"}, {"api_name": "ball.bounce_x", "line_number": 40, "usage_type": "call"}, {"api_name": "ball.xcor", "line_number": 43, "usage_type": "call"}, {"api_name": "ball.reset_position", "line_number": 44, "usage_type": "call"}, {"api_name": "scoreboard.add_l_score", "line_number": 45, "usage_type": "call"}, {"api_name": "ball.xcor", "line_number": 47, "usage_type": "call"}, {"api_name": "ball.reset_position", "line_number": 48, "usage_type": "call"}, {"api_name": "scoreboard.add_r_score", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "352627092", "text": "import sys\r\nimport unittest\r\nfrom argparse import ArgumentParser\r\n\r\nfrom gooey import GooeyParser\r\nfrom gooey.python_bindings import argparse_to_json\r\nfrom gooey.util.functional import getin\r\n\r\n\r\nclass TestArgparse(unittest.TestCase):\r\n\r\n def test_mutex_groups_conversion(self):\r\n \"\"\"\r\n Ensure multiple mutex groups are processed correctly.\r\n \"\"\"\r\n parser = ArgumentParser()\r\n g1 = parser.add_mutually_exclusive_group(required=True)\r\n g1.add_argument('--choose1')\r\n g1.add_argument('--choose2')\r\n\r\n g2 = parser.add_mutually_exclusive_group(required=True)\r\n g2.add_argument('--choose3')\r\n g2.add_argument('--choose4')\r\n\r\n output = argparse_to_json.process(parser, {}, {}, {})\r\n\r\n # assert that we get two groups of two choices back\r\n items = output[0]['items']\r\n self.assertTrue(len(items) == 2)\r\n group1 = items[0]\r\n group2 = items[1]\r\n self.assertTrue(['--choose1'] in group1['data']['commands'])\r\n self.assertTrue(['--choose2'] in group1['data']['commands'])\r\n self.assertTrue(['--choose3'] in group2['data']['commands'])\r\n self.assertTrue(['--choose4'] in group2['data']['commands'])\r\n self.assertTrue(group1['type'] == 'RadioGroup')\r\n self.assertTrue(group2['type'] == 'RadioGroup')\r\n\r\n def test_json_iterable_conversion(self):\r\n \"\"\"\r\n Issue #312 - tuples weren't being coerced to list during argparse\r\n conversion causing downstream issues when concatenating\r\n \"\"\"\r\n # our original functionality accepted only lists as the choices arg\r\n parser = ArgumentParser()\r\n parser.add_argument(\"-foo\", choices=['foo','bar', 'baz'])\r\n result = argparse_to_json.action_to_json(parser._actions[-1], \"Dropdown\", {})\r\n\r\n choices = result['data']['choices']\r\n self.assertTrue(isinstance(choices, list))\r\n self.assertEqual(choices, ['foo','bar', 'baz'])\r\n\r\n # Now we allow tuples as well.\r\n parser = ArgumentParser()\r\n parser.add_argument(\"-foo\", choices=('foo','bar', 'baz'))\r\n result = argparse_to_json.action_to_json(parser._actions[-1], \"Dropdown\", {})\r\n\r\n choices = result['data']['choices']\r\n self.assertTrue(isinstance(choices, list))\r\n self.assertEqual(choices, ['foo','bar', 'baz'])\r\n\r\n\r\n def test_choice_string_cooersion(self):\r\n \"\"\"\r\n Issue 321 - must coerce choice types to string to support wx.ComboBox\r\n \"\"\"\r\n parser = ArgumentParser()\r\n parser.add_argument('--foo', default=1, choices=[1, 2, 3])\r\n choice_action = parser._actions[-1]\r\n result = argparse_to_json.action_to_json(choice_action, 'Dropdown', {})\r\n self.assertEqual(getin(result, ['data', 'choices']), ['1', '2', '3'])\r\n # default value is also converted to a string type\r\n self.assertEqual(getin(result, ['data', 'default']), '1')\r\n\r\n def test_choice_string_cooersion_no_default(self):\r\n \"\"\"\r\n Make sure that choice types without a default don't create\r\n the literal string \"None\" but stick with the value None\r\n \"\"\"\r\n parser = ArgumentParser()\r\n parser.add_argument('--foo', choices=[1, 2, 3])\r\n\r\n choice_action = parser._actions[-1]\r\n result = argparse_to_json.action_to_json(choice_action, 'Dropdown', {})\r\n self.assertEqual(getin(result, ['data', 'default']), None)\r\n \r\n\r\n def test_listbox_defaults_cast_correctly(self):\r\n \"\"\"\r\n Issue XXX - defaults supplied in a list were turned into a string\r\n wholesale (list and all). The defaults should be stored as a list\r\n proper with only the _internal_ values coerced to strings.\r\n \"\"\"\r\n parser = GooeyParser()\r\n parser.add_argument('--foo', widget=\"Listbox\", nargs=\"*\", choices=[1, 2, 3], default=[1, 2])\r\n\r\n choice_action = parser._actions[-1]\r\n result = argparse_to_json.action_to_json(choice_action, 'Listbox', {})\r\n self.assertEqual(getin(result, ['data', 'default']), ['1', '2'])\r\n\r\n\r\n def test_listbox_single_default_cast_correctly(self):\r\n \"\"\"\r\n Single arg defaults to listbox should be wrapped in a list and\r\n their contents coerced as usual.\r\n \"\"\"\r\n parser = GooeyParser()\r\n parser.add_argument('--foo', widget=\"Listbox\",\r\n nargs=\"*\", choices=[1, 2, 3], default=\"sup\")\r\n\r\n choice_action = parser._actions[-1]\r\n result = argparse_to_json.action_to_json(choice_action, 'Listbox', {})\r\n self.assertEqual(getin(result, ['data', 'default']), ['sup'])\r\n\r\n def test_non_data_defaults_are_dropped_entirely(self):\r\n \"\"\"\r\n This is a refinement in understanding of Issue #147\r\n\r\n Caused by Issue 377 - passing arbitrary objects as defaults\r\n causes failures.\r\n \"\"\"\r\n # passing plain data to cleaning function results in plain data\r\n # being returned\r\n data = ['abc',\r\n 123,\r\n ['a', 'b'],\r\n [1, 2, 3]]\r\n\r\n for datum in data:\r\n result = argparse_to_json.clean_default(datum)\r\n self.assertEqual(result, datum)\r\n\r\n # passing in complex objects results in None\r\n objects = [sys.stdout, sys.stdin, object(), max, min]\r\n\r\n for obj in objects:\r\n result = argparse_to_json.clean_default(obj)\r\n self.assertEqual(result, None)\r\n\r\n\r\n", "sub_path": "gooey/tests/test_argparse_to_json.py", "file_name": "test_argparse_to_json.py", "file_ext": "py", "file_size_in_byte": 5508, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "unittest.TestCase", "line_number": 10, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 16, "usage_type": "call"}, {"api_name": "gooey.python_bindings.argparse_to_json.process", "line_number": 25, "usage_type": "call"}, {"api_name": "gooey.python_bindings.argparse_to_json", "line_number": 25, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 45, "usage_type": "call"}, {"api_name": "gooey.python_bindings.argparse_to_json.action_to_json", "line_number": 47, "usage_type": "call"}, {"api_name": "gooey.python_bindings.argparse_to_json", "line_number": 47, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 54, "usage_type": "call"}, {"api_name": "gooey.python_bindings.argparse_to_json.action_to_json", "line_number": 56, "usage_type": "call"}, {"api_name": "gooey.python_bindings.argparse_to_json", "line_number": 56, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 67, "usage_type": "call"}, {"api_name": "gooey.python_bindings.argparse_to_json.action_to_json", "line_number": 70, "usage_type": "call"}, {"api_name": "gooey.python_bindings.argparse_to_json", "line_number": 70, "usage_type": "name"}, {"api_name": "gooey.util.functional.getin", "line_number": 71, "usage_type": "call"}, {"api_name": "gooey.util.functional.getin", "line_number": 73, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 80, "usage_type": "call"}, {"api_name": "gooey.python_bindings.argparse_to_json.action_to_json", "line_number": 84, "usage_type": "call"}, {"api_name": "gooey.python_bindings.argparse_to_json", "line_number": 84, "usage_type": "name"}, {"api_name": "gooey.util.functional.getin", "line_number": 85, "usage_type": "call"}, {"api_name": "gooey.GooeyParser", "line_number": 94, "usage_type": "call"}, {"api_name": "gooey.python_bindings.argparse_to_json.action_to_json", "line_number": 98, "usage_type": "call"}, {"api_name": "gooey.python_bindings.argparse_to_json", "line_number": 98, "usage_type": "name"}, {"api_name": "gooey.util.functional.getin", "line_number": 99, "usage_type": "call"}, {"api_name": "gooey.GooeyParser", "line_number": 107, "usage_type": "call"}, {"api_name": "gooey.python_bindings.argparse_to_json.action_to_json", "line_number": 112, "usage_type": "call"}, {"api_name": "gooey.python_bindings.argparse_to_json", "line_number": 112, "usage_type": "name"}, {"api_name": "gooey.util.functional.getin", "line_number": 113, "usage_type": "call"}, {"api_name": "gooey.python_bindings.argparse_to_json.clean_default", "line_number": 130, "usage_type": "call"}, {"api_name": "gooey.python_bindings.argparse_to_json", "line_number": 130, "usage_type": "name"}, {"api_name": "sys.stdout", "line_number": 134, "usage_type": "attribute"}, {"api_name": "sys.stdin", "line_number": 134, "usage_type": "attribute"}, {"api_name": "gooey.python_bindings.argparse_to_json.clean_default", "line_number": 137, "usage_type": "call"}, {"api_name": "gooey.python_bindings.argparse_to_json", "line_number": 137, "usage_type": "name"}]} +{"seq_id": "133030388", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.spatial import distance\nimport collections\nimport operator\nimport time\nimport multiprocessing as mp\n\ndef avgMove(pop1,pop2,distance):\n return((pop1*pop2)/distance)\n\ndef calcDistGrid(shape,point):\n i = 0\n x = shape[0]\n y = shape[1]\n distGrid = {}\n z = 0\n while i < x:\n j = 0\n while j < y:\n distGrid.update({(i,j): distance.euclidean((i,j),point)})\n j +=1\n z += 1\n i += 1\n return collections.OrderedDict(sorted(distGrid.items(),key=operator.itemgetter(1)))\n\n\ndef update(grid,distGrid,point1):\n for el in distGrid:\n m = 0\n if el != point1:\n m = .005*avgMove(grid[point1],grid[el],distance.euclidean(point1,el))\n if((grid[point1] >= m) and el != point1):\n grid[point1] -= m\n grid[el] += m\n return grid\n\n\ndef combUpdate(x,y,i,j,grid):\n distGrid = calcDistGrid((x,y),(i,j))\n gridTemp = np.copy(update(np.copy(grid),distGrid,(i,j))- grid)\n return_list.append(gridTemp)\n\ndef allUpdate(grid):\n allSum = 0\n allGrids = []\n i = 0\n print(\"--------\")\n procs = []\n while i < x:\n j = 0\n while j < y:\n proc = mp.Process(target=combUpdate, args=(x,y,i,j,grid,))\n procs.append(proc)\n proc.start()\n j += 1\n i += 1\n for el in procs:\n el.join()\n allGrids = return_list\n gridTemp = np.zeros((x,y))\n for el in allGrids:\n grid = grid+el\n gridTemp += el\n print(\"Number of migrants: \" + str(np.sum(np.absolute(gridTemp))/2))\n return grid\n\nif __name__ == '__main__':\n \n manager = mp.Manager()\n return_list = manager.list()\n \n grid = np.genfromtxt('grid.csv', delimiter=' ') \n # size of the grid\n x = grid.shape[0]\n y = grid.shape[1]\n\n\n # fraction of peope moving compared to the whole population\n movingRatio = 0.3\n\n\n gridT0 = np.copy(grid)\n t1 = time.time()\n gridT1 = allUpdate(np.copy(gridT0))\n t2 = time.time()\n print(str(t2-t1))\n print(\"Population: \" + str(np.sum(np.absolute(gridT1))))\n return_list = manager.list()\n gridT2 = allUpdate(np.copy(gridT1))\n print(\"Population: \" + str(np.sum(np.absolute(gridT2))))\n return_list = manager.list()\n gridT3 = allUpdate(np.copy(gridT2))\n print(\"Population: \" + str(np.sum(np.absolute(gridT3))))\n return_list = manager.list()\n gridT4 = allUpdate(np.copy(gridT3))\n print(\"Population: \" + str(np.sum(np.absolute(gridT4))))\n return_list = manager.list()\n gridT5 = allUpdate(np.copy(gridT4))\n print(\"Population: \" + str(np.sum(np.absolute(gridT5))))\n return_list = manager.list()\n gridT6 = allUpdate(np.copy(gridT5))\n print(\"Population: \" + str(np.sum(np.absolute(gridT6))))\n return_list = manager.list()\n gridT7 = allUpdate(np.copy(gridT6))\n print(\"Population: \" + str(np.sum(np.absolute(gridT7))))\n return_list = manager.list()\n gridT8 = allUpdate(np.copy(gridT7))\n t3 = time.time()\n print(str(t3-t1))\n \n fig, ax = plt.subplots(nrows=3, ncols=6)\n ax[0][0].matshow(grid)\n ax[0][1].matshow(gridT1)\n ax[0][2].matshow(gridT2)\n ax[1][0].matshow(gridT3)\n ax[1][1].matshow(gridT4)\n ax[1][2].matshow(gridT5)\n ax[2][0].matshow(gridT6)\n ax[2][1].matshow(gridT7)\n ax[2][2].matshow(gridT8)\n\n ax[0][3].matshow(gridT1- grid)\n ax[0][4].matshow(gridT2 - gridT1)\n ax[0][5].matshow(gridT2 - gridT3)\n ax[1][3].matshow(gridT3 - gridT4)\n ax[1][4].matshow(gridT4 - gridT5)\n ax[1][5].matshow(gridT5 - gridT6)\n ax[2][3].matshow(gridT6 - gridT7) \n ax[2][4].matshow(gridT7 - gridT8)\n ax[2][5].matshow(grid)\n\n\n plt.show()\n", "sub_path": "OldScripts/gravTest.py", "file_name": "gravTest.py", "file_ext": "py", "file_size_in_byte": 3721, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "scipy.spatial.distance", "line_number": 10, "usage_type": "name"}, {"api_name": "scipy.spatial.distance.euclidean", "line_number": 21, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 21, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 25, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 25, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.euclidean", "line_number": 32, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 32, "usage_type": "name"}, {"api_name": "numpy.copy", "line_number": 41, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 65, "usage_type": "call"}, {"api_name": "multiprocessing.Manager", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.genfromtxt", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 83, "usage_type": "call"}, {"api_name": "time.time", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 85, "usage_type": "call"}, {"api_name": "time.time", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 108, "usage_type": "call"}, {"api_name": "time.time", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 112, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}]} +{"seq_id": "581519129", "text": "from django.test import TestCase\nimport data_exploit.utils as utils\nimport pandas as pd\nfrom pandas.util.testing import assert_frame_equal\n\nfrom data_exploit.models import CO2_Data\n\n\nclass CO2CalculationsTestCase(TestCase):\n def setUp(self):\n data = [[\"2018-03-15 02:00:00\", 40, 3], [\"2018-03-15 03:00:00\", 50, 3], [\"2018-03-17 02:00:00\", 20, 5], [\"2018-03-17 04:00:00\", 30, 5]]\n df = pd.DataFrame(data, columns=['date_heure', 'taux_co2', 'weekday'])\n df['date_heure'] = pd.to_datetime(df['date_heure'])\n df['date_heure'] = df['date_heure'].dt.strftime('%Y-%m-%d %H:%M:%S+00:00')\n self.df = df.set_index(pd.DatetimeIndex(df['date_heure']))\n self.df = self.df.drop(columns=\"date_heure\")\n\n CO2_Data.objects.create(date_heure=\"2018-03-15 02:00:00\", taux_co2=\"40\", recordid=\"1\")\n CO2_Data.objects.create(date_heure=\"2018-03-15 03:00:00\", taux_co2=\"50\", recordid=\"2\")\n CO2_Data.objects.create(date_heure=\"2018-03-17 02:00:00\", taux_co2=\"20\", recordid=\"3\")\n CO2_Data.objects.create(date_heure=\"2018-03-17 04:00:00\", taux_co2=\"30\", recordid=\"4\")\n\n def test_calculate_weekend_mean(self):\n output = utils.calculate_weekend_mean(self.df)\n self.assertEqual(output, 25)\n\n def test_calculate_weekday_mean(self):\n output = utils.calculate_weekday_mean(self.df)\n self.assertEqual(output, 45)\n\n def test_get_co2_data(self):\n f_horaire, interpolated_df = utils.get_co2_data()\n assert_frame_equal(f_horaire, self.df)\n", "sub_path": "data_exploit/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 1523, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.test.TestCase", "line_number": 9, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 13, "usage_type": "call"}, {"api_name": "pandas.DatetimeIndex", "line_number": 15, "usage_type": "call"}, {"api_name": "data_exploit.models.CO2_Data.objects.create", "line_number": 18, "usage_type": "call"}, {"api_name": "data_exploit.models.CO2_Data.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "data_exploit.models.CO2_Data", "line_number": 18, "usage_type": "name"}, {"api_name": "data_exploit.models.CO2_Data.objects.create", "line_number": 19, "usage_type": "call"}, {"api_name": "data_exploit.models.CO2_Data.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "data_exploit.models.CO2_Data", "line_number": 19, "usage_type": "name"}, {"api_name": "data_exploit.models.CO2_Data.objects.create", "line_number": 20, "usage_type": "call"}, {"api_name": "data_exploit.models.CO2_Data.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "data_exploit.models.CO2_Data", "line_number": 20, "usage_type": "name"}, {"api_name": "data_exploit.models.CO2_Data.objects.create", "line_number": 21, "usage_type": "call"}, {"api_name": "data_exploit.models.CO2_Data.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "data_exploit.models.CO2_Data", "line_number": 21, "usage_type": "name"}, {"api_name": "data_exploit.utils.calculate_weekend_mean", "line_number": 24, "usage_type": "call"}, {"api_name": "data_exploit.utils", "line_number": 24, "usage_type": "name"}, {"api_name": "data_exploit.utils.calculate_weekday_mean", "line_number": 28, "usage_type": "call"}, {"api_name": "data_exploit.utils", "line_number": 28, "usage_type": "name"}, {"api_name": "data_exploit.utils.get_co2_data", "line_number": 32, "usage_type": "call"}, {"api_name": "data_exploit.utils", "line_number": 32, "usage_type": "name"}, {"api_name": "pandas.util.testing.assert_frame_equal", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "195550282", "text": "import requests\nimport shutil\nimport os\n\n\ndef image_download(img):\n url = \"http://img.omdbapi.com/?i=tt%s&h=600&apikey=aaa3a138\" % img\n try:\n response = requests.get(url,stream=True)\n with open('poster_images/'+str(img)+'.jpg','wb') as out:\n shutil.copyfileobj(response.raw,out)\n\n print(response)\n\n except Exception as e:\n print(e, \" \", img)\n\n\nL = []\nfor i in os.listdir('poster_images'):\n L.append(i.split(\".\")[0])\n\n\nwith open('ml-25m/links.csv') as f:\n line = f.read()\n\nfor i in line.split('\\n'):\n img_name = i.split(',')[1]\n if img_name in L:\n print(\"Already downloaded\")\n else:\n image_download(img_name)\n\n", "sub_path": "poster-download.py", "file_name": "poster-download.py", "file_ext": "py", "file_size_in_byte": 686, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "requests.get", "line_number": 9, "usage_type": "call"}, {"api_name": "shutil.copyfileobj", "line_number": 11, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "175168082", "text": "import functools\n\nfrom rdkit import Chem\nfrom rdkit.Chem.Draw import rdMolDraw2D\n\n\n@functools.lru_cache(1024)\ndef smiles_to_svg(smiles: str, image_width: int = 200, image_height: int = 200) -> str:\n \"\"\"Renders a 2D representation of a molecule based on its SMILES representation as\n an SVG string.\n\n Parameters\n ----------\n smiles\n The SMILES pattern.\n image_width\n The width to make the final SVG.\n image_height\n The height to make the final SVG.\n\n Returns\n -------\n The 2D SVG representation.\n \"\"\"\n\n # Parse the SMILES into an RDKit molecule\n smiles_parser = Chem.rdmolfiles.SmilesParserParams()\n smiles_parser.removeHs = False\n\n rdkit_molecule = Chem.MolFromSmiles(smiles, smiles_parser)\n\n # Generate a set of 2D coordinates.\n if not rdkit_molecule.GetNumConformers():\n Chem.rdDepictor.Compute2DCoords(rdkit_molecule)\n\n drawer = rdMolDraw2D.MolDraw2DSVG(image_width, image_height)\n rdMolDraw2D.PrepareAndDrawMolecule(drawer, rdkit_molecule)\n drawer.FinishDrawing()\n\n svg_content = drawer.GetDrawingText()\n return svg_content\n", "sub_path": "plotmol/utilities/rdkit.py", "file_name": "rdkit.py", "file_ext": "py", "file_size_in_byte": 1126, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "rdkit.Chem.rdmolfiles.SmilesParserParams", "line_number": 27, "usage_type": "call"}, {"api_name": "rdkit.Chem.rdmolfiles", "line_number": 27, "usage_type": "attribute"}, {"api_name": "rdkit.Chem", "line_number": 27, "usage_type": "name"}, {"api_name": "rdkit.Chem.MolFromSmiles", "line_number": 30, "usage_type": "call"}, {"api_name": "rdkit.Chem", "line_number": 30, "usage_type": "name"}, {"api_name": "rdkit.Chem.rdDepictor.Compute2DCoords", "line_number": 34, "usage_type": "call"}, {"api_name": "rdkit.Chem.rdDepictor", "line_number": 34, "usage_type": "attribute"}, {"api_name": "rdkit.Chem", "line_number": 34, "usage_type": "name"}, {"api_name": "rdkit.Chem.Draw.rdMolDraw2D.MolDraw2DSVG", "line_number": 36, "usage_type": "call"}, {"api_name": "rdkit.Chem.Draw.rdMolDraw2D", "line_number": 36, "usage_type": "name"}, {"api_name": "rdkit.Chem.Draw.rdMolDraw2D.PrepareAndDrawMolecule", "line_number": 37, "usage_type": "call"}, {"api_name": "rdkit.Chem.Draw.rdMolDraw2D", "line_number": 37, "usage_type": "name"}, {"api_name": "functools.lru_cache", "line_number": 7, "usage_type": "call"}]} +{"seq_id": "629811533", "text": "import numpy as np\n# from scipy.interpolate import *\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker\nimport statistics\n\ndef least_squares_fit(x, y, err_y):\n plt.rc('font', family = 'serif', serif = 'cmr10')\n plt.rcParams['mathtext.fontset'] = \"cm\" \n plt.rcParams[\"axes.linewidth\"] = 1.0 \n\n arr_size = int(len(x))\n N = arr_size - 1 # N = x(arr_size - 1) # where N is the final element in the array (of x vals)\n # we have to -1 as elements in array start count at 0 NOT 1\n\n p = np.polyfit(x, y, 1) # finds the coefficients for the 'best' fitting function\n \n f = np.polyval(p, x) # these are the 'y-values' of the fitting function\n sigma = statistics.stdev(f - y) # standard deviation of quantity 'f - y' amount of (vertical) deviation between the model and the data points\n\n # plt.figure(figsize = (8, 6))\n plt.errorbar(x, y, err_y, fmt = \"k\", capsize = 3, elinewidth = 0.6, MarkerSize = 2, markeredgewidth = 0.6, LineStyle = \"none\")\n # the last argument for the two lines below, is for the legend\n plt.plot(x, y, Marker = \"+\", MarkerSize = 4, MarkerEdgeColor = \"k\", MarkerFaceColor = \"k\", LineWidth = 0.6, LineStyle = \"none\")\n plt.plot(x, f, LineWidth = 0.6, Linestyle = \"-\", Color = \"g\", label = \"Model\") # 'line of best fit'\n\n # plt.title(\"Least Squares Fit\", fontsize = 12, fontweight = \"bold\")\n plt.xlabel(\"Frequency \" r\"$\\nu$(MHz)\" , fontsize = 10)\n plt.ylabel(\"Magnetic Field $B$(mT)\", fontsize = 10)\n plt.axis([0, 140, 0, 5])\n # plt.xlim(-1, 6)\n # ax.xaxis.set_major_locator(matplotlib.ticker.MultipleLocator(1))\n # plt.ylim(-2, 2)\n # ax.yaxis.set_major_locator(matplotlib.ticker.MultipleLocator(1))\n # plt.gca().ticklabel_format(axis='y',style='sci',scilimits=(1,4), useOffset=False)\n # plt.gca().yaxis.get_major_formatter().set_powerlimits((0, 1))\n # plt.gca().xaxis.set_major_formatter(matplotlib.ticker.FormatStrFormatter('%.1e'))\n plt.gca().yaxis.set_major_formatter(matplotlib.ticker.FormatStrFormatter('%.1f'))\n plt.gca().tick_params(width = 1.0, labelsize = 8)\n \n plt.savefig(\"Electron_plot1.pdf\")\n\n print(\"Gradient is: {:e}\" .format(p[0]))\n print(\"y-intercept is: {:e}\" .format(p[1]))\n print(\"Sigma is: {:e}\" .format(sigma))\n \n # The next two lines are for linear i.e. straight line fits only\n grad_err = (2 * sigma) / (x[N] - x[0])\n print(\"Gradient error is: {:e}\" .format(grad_err))", "sub_path": "PHY2026/Exp_2/LS_Poly4.py", "file_name": "LS_Poly4.py", "file_ext": "py", "file_size_in_byte": 2435, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "matplotlib.pyplot.rc", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 8, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 9, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 10, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "numpy.polyfit", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.polyval", "line_number": 18, "usage_type": "call"}, {"api_name": "statistics.stdev", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.errorbar", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ticker.FormatStrFormatter", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ticker", "line_number": 38, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}]} +{"seq_id": "418913491", "text": "\"\"\"Tests for SwarmSpawner\"\"\"\n\nfrom unittest import mock\n\nimport pytest\nfrom jupyterhub.tests.test_api import add_user, api_request\nfrom jupyterhub.tests.mocking import public_url\nfrom jupyterhub.tests.utils import async_requests\nfrom jupyterhub.utils import url_path_join\n\nfrom dockerspawner import SwarmSpawner\n\npytestmark = pytest.mark.usefixtures(\"swarmspawner\")\n\n\n@pytest.fixture\ndef swarmspawner(app):\n \"\"\"Configure JupyterHub to use DockerSpawner\"\"\"\n app.config.SwarmSpawner.prefix = \"dockerspawner-test\"\n with mock.patch.dict(\n app.tornado_settings, {\"spawner_class\": SwarmSpawner}\n ), mock.patch.dict(\n app.config.SwarmSpawner, {\"network_name\": \"bridge\"}\n ):\n yield\n\n\n@pytest.mark.gen_test\ndef test_start_stop(app):\n name = \"somebody\"\n add_user(app.db, app, name=name)\n user = app.users[name]\n assert isinstance(user.spawner, SwarmSpawner)\n token = user.new_api_token()\n # start the server\n r = yield api_request(app, \"users\", name, \"server\", method=\"post\")\n while r.status_code == 202:\n # request again\n r = yield api_request(app, \"users\", name, \"server\", method=\"post\")\n assert r.status_code == 201, r.text\n url = url_path_join(public_url(app, user), \"api/status\")\n r = yield async_requests.get(url, headers={\"Authorization\": \"token %s\" % token})\n assert r.url == url\n r.raise_for_status()\n print(r.text)\n assert \"kernels\" in r.json()\n", "sub_path": "tests/test_swarmspawner.py", "file_name": "test_swarmspawner.py", "file_ext": "py", "file_size_in_byte": 1440, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "pytest.mark.usefixtures", "line_number": 13, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 13, "usage_type": "attribute"}, {"api_name": "unittest.mock.patch.dict", "line_number": 20, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 20, "usage_type": "attribute"}, {"api_name": "unittest.mock", "line_number": 20, "usage_type": "name"}, {"api_name": "dockerspawner.SwarmSpawner", "line_number": 21, "usage_type": "name"}, {"api_name": "unittest.mock.patch.dict", "line_number": 22, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 22, "usage_type": "attribute"}, {"api_name": "unittest.mock", "line_number": 22, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 16, "usage_type": "attribute"}, {"api_name": "jupyterhub.tests.test_api.add_user", "line_number": 31, "usage_type": "call"}, {"api_name": "dockerspawner.SwarmSpawner", "line_number": 33, "usage_type": "argument"}, {"api_name": "jupyterhub.tests.test_api.api_request", "line_number": 36, "usage_type": "call"}, {"api_name": "jupyterhub.tests.test_api.api_request", "line_number": 39, "usage_type": "call"}, {"api_name": "jupyterhub.utils.url_path_join", "line_number": 41, "usage_type": "call"}, {"api_name": "jupyterhub.tests.mocking.public_url", "line_number": 41, "usage_type": "call"}, {"api_name": "jupyterhub.tests.utils.async_requests.get", "line_number": 42, "usage_type": "call"}, {"api_name": "jupyterhub.tests.utils.async_requests", "line_number": 42, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 28, "usage_type": "attribute"}]} +{"seq_id": "561940109", "text": "from enum import Enum\n\n\nclass AdapterRequestType(Enum):\n CREATE_VIRTUAL_SCHEMA = 1,\n DROP_VIRTUAL_SCHEMA = 2,\n SET_PROPERTIES = 3,\n REFRESH = 4,\n GET_CAPABILITIES = 5,\n PUSHDOWN = 6\n\n\nclass AdapterRequest(object):\n def __init__(self, request_type):\n self._request_type = request_type\n\n @property\n def request_type(self):\n return self._request_type\n\n @request_type.setter\n def request_type(self, value):\n self._request_type = value\n\n\nclass CreateVirtualSchemaAdapterRequest(AdapterRequest):\n def __init__(self, name, meta_connection, table_schema, websockets_lib_path):\n super(CreateVirtualSchemaAdapterRequest, self).__init__(AdapterRequestType.CREATE_VIRTUAL_SCHEMA)\n self._name = name\n self._meta_connection = meta_connection\n self._table_schema = table_schema\n self._websockets_lib_path = websockets_lib_path\n\n @property\n def name(self):\n return self._name\n\n @property\n def meta_connection(self):\n return self._meta_connection\n\n @property\n def table_schema(self):\n return self._table_schema\n\n @property\n def websockets_lib_path(self):\n return self._websockets_lib_path\n\n\nclass AdapterRequestFactory(object):\n REQUEST_TYPE_MAPPING = {\"createVirtualSchema\": AdapterRequestType.CREATE_VIRTUAL_SCHEMA,\n \"dropVirtualSchema\": AdapterRequestType.DROP_VIRTUAL_SCHEMA,\n \"refresh\": AdapterRequestType.REFRESH,\n \"pushdown\": AdapterRequestType.PUSHDOWN,\n \"getCapabilities\": AdapterRequestType.GET_CAPABILITIES,\n \"setProperties\": AdapterRequestType.SET_PROPERTIES}\n\n CREATE_VIRTUAL_SCHEMA_REQUIRED_ATTRIBUTES = {\"schemaMetadataInfo\": {\"name\": None,\n \"properties\": {\"META_CONNECTION\": None,\n \"TABLE_SCHEMA\": None,\n \"WEBSOCKETS_EGG_PATH\": None}}}\n\n @classmethod\n def check_request_base(cls, request_dict):\n \"\"\"\n Function checks base validity of request object.\n :param request_dict: json request represented as python dictionary\n :return:\n \"\"\"\n if \"type\" not in request_dict:\n raise ValueError(\"Missing request 'type' key.\")\n if request_dict[\"type\"] not in cls.REQUEST_TYPE_MAPPING:\n raise ValueError(\"'{0}' is not supported\".format(request_dict[\"type\"]))\n\n @classmethod\n def check_required_attributes(cls, required_attrs_dict, request_dict):\n for required_attribute in required_attrs_dict.keys():\n if required_attribute not in request_dict:\n raise ValueError(\"Attribute '{0}' is not found in request.\")\n if required_attrs_dict[required_attribute] is not None:\n AdapterRequestFactory.check_required_attributes(required_attrs_dict[required_attribute],\n request_dict[required_attribute])\n\n @classmethod\n def check_create_virtual_schema_request(cls, request_dict):\n AdapterRequestFactory.check_required_attributes(AdapterRequestFactory.CREATE_VIRTUAL_SCHEMA_REQUIRED_ATTRIBUTES,\n request_dict)\n\n @classmethod\n def get_adapter_request_type(cls, request_dict):\n return AdapterRequestFactory.REQUEST_TYPE_MAPPING[request_dict[\"type\"]]\n\n @classmethod\n def create_virtual_schema_request(cls, request_dict):\n schema_metadata = request_dict[\"schemaMetadataInfo\"]\n schema_properties = schema_metadata[\"properties\"]\n return CreateVirtualSchemaAdapterRequest(schema_metadata[\"name\"], schema_properties[\"META_CONNECTION\"],\n schema_properties[\"TABLE_SCHEMA\"],\n schema_properties[\"WEBSOCKETS_EGG_PATH\"])\n\n @classmethod\n def create_from_json_dict(cls, request_dict):\n AdapterRequestFactory.check_request_base(request_dict)\n request_type = AdapterRequestFactory.get_adapter_request_type(request_dict)\n if request_type == AdapterRequestType.CREATE_VIRTUAL_SCHEMA:\n AdapterRequestFactory.check_create_virtual_schema_request(request_dict)\n elif request_type == AdapterRequestType.GET_CAPABILITIES:\n raise NotImplementedError\n elif request_type == AdapterRequestType.SET_PROPERTIES:\n raise NotImplementedError\n elif request_type == AdapterRequestType.PUSHDOWN:\n raise NotImplementedError\n elif request_type == AdapterRequestType.REFRESH:\n raise NotImplementedError\n elif request_type == AdapterRequestType.DROP_VIRTUAL_SCHEMA:\n raise NotImplementedError\n else:\n raise ValueError(\"Can't handle '{0}' request type\".format(request_type))\n", "sub_path": "src/exa_impala_adapter_meta.py", "file_name": "exa_impala_adapter_meta.py", "file_ext": "py", "file_size_in_byte": 5084, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "enum.Enum", "line_number": 4, "usage_type": "name"}]} +{"seq_id": "222426878", "text": "from __future__ import absolute_import\n\nimport logging\nimport multiprocessing\n\nfrom .game import Game\nfrom .result_set import ResultSet\nfrom .tournament_type import RoundRobin\nfrom .payoff import payoff_matrix\nfrom .cooperation import cooperation_matrix\n\n\nclass Tournament(object):\n game = Game()\n\n def __init__(self, players, tournament_type=RoundRobin, name='axelrod',\n game=None, turns=200, repetitions=10, processes=None,\n prebuilt_cache=False, noise=0, with_morality=True,\n keep_matches=False):\n \"\"\"\n Parameters\n ----------\n players : list\n A list of axelrod.Player objects\n tournament_type : class\n A class that must be descended from axelrod.TournamentType\n name : string\n A name for the tournament\n game : axelrod.Game\n The game object used to score the tournament\n turns : integer\n The number of turns per match\n repetitions : integer\n The number of times the round robin should be repeated\n processes : integer\n The number of processes to be used for parallel processing\n prebuilt_cache : boolean\n Whether a cache has been passed in from an external object\n noise : float\n The probability that a player's intended action should be flipped\n with_morality : boolean\n Whether morality metrics should be calculated\n keep_matches : boolean\n Whether interaction results should be included in the output\n \"\"\"\n self.name = name\n self.turns = turns\n self.noise = noise\n if game is not None:\n self.game = game\n self.players = players\n self.repetitions = repetitions\n self.prebuilt_cache = prebuilt_cache\n self.deterministic_cache = {}\n self.tournament_type = tournament_type(\n players, turns, self.deterministic_cache)\n self._with_morality = with_morality\n self._keep_matches = keep_matches\n self._parallel_repetitions = repetitions\n self._processes = processes\n self._logger = logging.getLogger(__name__)\n self._outcome = {'payoff': [], 'cooperation': []}\n self.matches = []\n\n @property\n def players(self):\n return self._players\n\n @players.setter\n def players(self, players):\n \"\"\"Ensure that players are passed the tournament attributes\"\"\"\n newplayers = []\n for player in players:\n player.set_tournament_attributes(\n length=self.turns,\n game=self.game,\n noise=self.noise)\n newplayers.append(player)\n self._players = newplayers\n\n def play(self):\n \"\"\"\n Plays the tournament and passes the results to the ResultSet class\n\n Returns\n -------\n axelrod.ResultSet\n \"\"\"\n if self._processes is None:\n self._run_serial_repetitions(self._outcome)\n else:\n if self._build_cache_required():\n self._build_cache(self._outcome)\n self._run_parallel_repetitions(self._outcome)\n\n self.result_set = ResultSet(\n players=self.players,\n turns=self.turns,\n repetitions=self.repetitions,\n outcome=self._outcome,\n with_morality=self._with_morality)\n return self.result_set\n\n def _build_cache_required(self):\n \"\"\"\n A boolean to indicate whether it is necessary to build the\n deterministic cache.\n \"\"\"\n return (\n not self.noise and (\n len(self.deterministic_cache) == 0 or\n not self.prebuilt_cache))\n\n def _build_cache(self, outcome):\n \"\"\"\n For parallel processing, this runs a single round robin in order to\n build the deterministic cache.\n\n Parameters\n ----------\n outcome : dictionary\n The outcome dictionary to update with results\n \"\"\"\n self._logger.debug('Playing first round robin to build cache')\n self._run_single_repetition(outcome)\n self._parallel_repetitions -= 1\n\n def _run_single_repetition(self, outcome):\n \"\"\"\n Runs a single round robin and updates the outcome dictionary.\n \"\"\"\n matches = self.tournament_type.build_matches(\n cache_mutable=True, noise=self.noise)\n output = self._play_matches(matches)\n outcome['payoff'].append(output['payoff'])\n outcome['cooperation'].append(output['cooperation'])\n if self._keep_matches:\n self.matches.append(output['matches'])\n\n def _run_serial_repetitions(self, outcome):\n \"\"\"\n Runs all repetitions of the round robin in serial.\n\n Parameters\n ----------\n outcome : dictionary\n The outcome dictionary to update with results\n \"\"\"\n self._logger.debug('Playing %d round robins' % self.repetitions)\n for repetition in range(self.repetitions):\n self._run_single_repetition(outcome)\n return True\n\n def _run_parallel_repetitions(self, outcome):\n \"\"\"\n Run all except the first round robin using parallel processing.\n\n Parameters\n ----------\n outcome : dictionary\n The outcome dictionary to update with results\n \"\"\"\n # At first sight, it might seem simpler to use the multiprocessing Pool\n # Class rather than Processes and Queues. However, Pool can only accept\n # target functions which can be pickled and instance methods cannot.\n work_queue = multiprocessing.Queue()\n done_queue = multiprocessing.Queue()\n workers = self._n_workers()\n\n for repetition in range(self._parallel_repetitions):\n work_queue.put(repetition)\n\n self._logger.debug(\n 'Playing %d round robins with %d parallel processes' %\n (self._parallel_repetitions, workers))\n self._start_workers(workers, work_queue, done_queue)\n self._process_done_queue(workers, done_queue, outcome)\n\n return True\n\n def _n_workers(self):\n \"\"\"\n Determines the number of parallel processes to use.\n\n Returns\n -------\n integer\n \"\"\"\n if (2 <= self._processes <= multiprocessing.cpu_count()):\n n_workers = self._processes\n else:\n n_workers = multiprocessing.cpu_count()\n return n_workers\n\n def _start_workers(self, workers, work_queue, done_queue):\n \"\"\"\n Initiates the sub-processes to carry out parallel processing.\n\n Parameters\n ----------\n workers : integer\n The number of sub-processes to create\n work_queue : multiprocessing.Queue\n A queue containing an entry for each round robin to be processed\n done_queue : multiprocessing.Queue\n A queue containing the output dictionaries from each round robin\n \"\"\"\n for worker in range(workers):\n process = multiprocessing.Process(\n target=self._worker, args=(work_queue, done_queue))\n work_queue.put('STOP')\n process.start()\n return True\n\n def _process_done_queue(self, workers, done_queue, outcome):\n \"\"\"\n Retrieves the outcome dictionaries from the parallel sub-processes\n\n Parameters\n ----------\n workers : integer\n The number of sub-processes in existence\n done_queue : multiprocessing.Queue\n A queue containing the output dictionaries from each round robin\n outcome : dictionary\n The outcome dictionary to update with results\n \"\"\"\n stops = 0\n while stops < workers:\n output = done_queue.get()\n if output == 'STOP':\n stops += 1\n else:\n outcome['payoff'].append(output['payoff'])\n outcome['cooperation'].append(output['cooperation'])\n if self._keep_matches:\n self.matches.append(outcome['matches'])\n return True\n\n def _worker(self, work_queue, done_queue):\n \"\"\"\n The work for each parallel sub-process to execute.\n\n Parameters\n ----------\n work_queue : multiprocessing.Queue\n A queue containing an entry for each round robin to be processed\n done_queue : multiprocessing.Queue\n A queue containing the output dictionaries from each round robin\n \"\"\"\n for repetition in iter(work_queue.get, 'STOP'):\n matches = self.tournament_type.build_matches(\n cache_mutable=False, noise=self.noise)\n output = self._play_matches(matches)\n done_queue.put(output)\n done_queue.put('STOP')\n return True\n\n def _play_matches(self, matches):\n \"\"\"\n Play the supplied matches.\n\n Parameters\n ----------\n matches : dictionary\n Mapping a tuple of player index numbers to an axelrod Match object\n\n Returns\n -------\n dictionary\n Containing the payoff and cooperation matrices\n \"\"\"\n interactions = {}\n if self._keep_matches:\n matches_to_keep = []\n\n for key, match in matches.items():\n interactions[key] = match.play()\n if self._keep_matches:\n matches_to_keep.append(match)\n\n payoff = payoff_matrix(interactions, self.game)\n cooperation = cooperation_matrix(interactions)\n\n output = {'payoff': payoff, 'cooperation': cooperation}\n if self._keep_matches:\n output['matches'] = matches_to_keep\n return output\n", "sub_path": "axelrod/tournament.py", "file_name": "tournament.py", "file_ext": "py", "file_size_in_byte": 9795, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "game.Game", "line_number": 14, "usage_type": "call"}, {"api_name": "tournament_type.RoundRobin", "line_number": 16, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 61, "usage_type": "call"}, {"api_name": "result_set.ResultSet", "line_number": 96, "usage_type": "call"}, {"api_name": "multiprocessing.Queue", "line_number": 166, "usage_type": "call"}, {"api_name": "multiprocessing.Queue", "line_number": 167, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 189, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 192, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 209, "usage_type": "call"}, {"api_name": "payoff.payoff_matrix", "line_number": 282, "usage_type": "call"}, {"api_name": "cooperation.cooperation_matrix", "line_number": 283, "usage_type": "call"}]} +{"seq_id": "139468341", "text": "\nimport html\nimport wikipedia\nimport re\nfrom datetime import datetime\nfrom typing import Optional\nfrom covid import Covid\n\nimport requests\nfrom telegram import Message, Chat, Update, MessageEntity\nfrom telegram import ParseMode, ReplyKeyboardRemove, InlineKeyboardMarkup, InlineKeyboardButton\nfrom telegram.ext import CommandHandler, Filters\nfrom telegram.ext.callbackcontext import CallbackContext\nfrom telegram.utils.helpers import escape_markdown, mention_html\nfrom telegram.error import BadRequest\n\nfrom group_helper import CONFIG\nfrom group_helper.__main__ import GDPR\nfrom group_helper.__main__ import STATS, USER_INFO\nfrom group_helper.modules.disable import DisableAbleCommandHandler\nfrom group_helper.modules.helper_funcs.extraction import extract_user\n\nfrom group_helper.modules.tr_engine.strings import tld\n\nfrom requests import get\n\ncvid = Covid(source=\"worldometers\")\n\n\ndef get_id(update: Update, context: CallbackContext):\n args = context.args\n user_id = extract_user(update.effective_message, args)\n chat = update.effective_chat # type: Optional[Chat]\n if user_id:\n if update.effective_message.reply_to_message and update.effective_message.reply_to_message.forward_from:\n user1 = update.effective_message.reply_to_message.from_user\n user2 = update.effective_message.reply_to_message.forward_from\n update.effective_message.reply_markdown(\n tld(chat.id,\n \"misc_get_id_1\").format(escape_markdown(user2.first_name),\n user2.id,\n escape_markdown(user1.first_name),\n user1.id))\n else:\n user = context.bot.get_chat(user_id)\n update.effective_message.reply_markdown(\n tld(chat.id,\n \"misc_get_id_2\").format(escape_markdown(user.first_name),\n user.id))\n else:\n chat = update.effective_chat # type: Optional[Chat]\n if chat.type == \"private\":\n update.effective_message.reply_markdown(\n tld(chat.id, \"misc_id_1\").format(chat.id))\n\n else:\n update.effective_message.reply_markdown(\n tld(chat.id, \"misc_id_2\").format(chat.id))\n\n\ndef info(update: Update, context: CallbackContext):\n args = context.args\n msg = update.effective_message # type: Optional[Message]\n user_id = extract_user(update.effective_message, args)\n chat = update.effective_chat # type: Optional[Chat]\n\n if user_id:\n user = context.bot.get_chat(user_id)\n\n elif not msg.reply_to_message and not args:\n user = msg.from_user\n\n elif not msg.reply_to_message and (\n not args or\n (len(args) >= 1 and not args[0].startswith(\"@\")\n and not args[0].isdigit()\n and not msg.parse_entities([MessageEntity.TEXT_MENTION]))):\n msg.reply_text(tld(chat.id, \"I can't extract a user from this.\"))\n return\n\n else:\n return\n\n text = tld(chat.id, \"misc_info_1\")\n text += tld(chat.id, \"misc_info_id\").format(user.id)\n text += tld(chat.id,\n \"misc_info_first\").format(html.escape(user.first_name))\n\n if user.last_name:\n text += tld(chat.id,\n \"misc_info_name\").format(html.escape(user.last_name))\n\n if user.username:\n text += tld(chat.id,\n \"misc_info_username\").format(html.escape(user.username))\n\n text += tld(chat.id,\n \"misc_info_user_link\").format(mention_html(user.id, \"link\"))\n\n if user.id == CONFIG.owner_id:\n text += tld(chat.id, \"misc_info_is_owner\")\n else:\n if user.id == int(254318997):\n text += tld(chat.id, \"misc_info_is_original_owner\")\n\n if user.id in CONFIG.sudo_users:\n text += tld(chat.id, \"misc_info_is_sudo\")\n else:\n if user.id in CONFIG.whitelist_users:\n text += tld(chat.id, \"misc_info_is_whitelisted\")\n\n for mod in USER_INFO:\n mod_info = mod.__user_info__(user.id, chat.id).strip()\n if mod_info:\n text += \"\\n\\n\" + mod_info\n\n update.effective_message.reply_text(text, parse_mode=ParseMode.HTML)\n\n\ndef reply_keyboard_remove(update: Update, context: CallbackContext):\n reply_keyboard = []\n reply_keyboard.append([ReplyKeyboardRemove(remove_keyboard=True)])\n reply_markup = ReplyKeyboardRemove(remove_keyboard=True)\n old_message = context.bot.send_message(\n chat_id=update.message.chat_id,\n text='trying', # This text will not get translated\n reply_markup=reply_markup,\n reply_to_message_id=update.message.message_id)\n context.bot.delete_message(chat_id=update.message.chat_id,\n message_id=old_message.message_id)\n\n\ndef gdpr(update: Update, context: CallbackContext):\n update.effective_message.reply_text(\n tld(update.effective_chat.id, \"misc_gdpr\"))\n for mod in GDPR:\n mod.__gdpr__(update.effective_user.id)\n\n update.effective_message.reply_text(\"GDPR is done\",\n parse_mode=ParseMode.MARKDOWN)\n\n\ndef markdown_help(update: Update, context: CallbackContext):\n chat = update.effective_chat # type: Optional[Chat]\n update.effective_message.reply_text(tld(chat.id, \"misc_md_list\"),\n parse_mode=ParseMode.HTML)\n update.effective_message.reply_text(tld(chat.id, \"misc_md_try\"))\n update.effective_message.reply_text(tld(chat.id, \"misc_md_help\"))\n\n\ndef stats(update: Update, context: CallbackContext):\n update.effective_message.reply_text(\n # This text doesn't get translated as it is internal message.\n \"*Current Stats:*\\n\" + \"\\n\".join([mod.__stats__() for mod in STATS]),\n parse_mode=ParseMode.MARKDOWN)\n\n\ndef github(update: Update, context: CallbackContext):\n message = update.effective_message\n text = message.text[len('/git '):]\n usr = get(f'https://api.github.com/users/{text}').json()\n if usr.get('login'):\n text = f\"*Username:* [{usr['login']}](https://github.com/{usr['login']})\"\n\n whitelist = [\n 'name', 'id', 'type', 'location', 'blog', 'bio', 'followers',\n 'following', 'hireable', 'public_gists', 'public_repos', 'email',\n 'company', 'updated_at', 'created_at'\n ]\n\n difnames = {\n 'id': 'Account ID',\n 'type': 'Account type',\n 'created_at': 'Account created at',\n 'updated_at': 'Last updated',\n 'public_repos': 'Public Repos',\n 'public_gists': 'Public Gists'\n }\n\n goaway = [None, 0, 'null', '']\n\n for x, y in usr.items():\n if x in whitelist:\n if x in difnames:\n x = difnames[x]\n else:\n x = x.title()\n\n if x == 'Account created at' or x == 'Last updated':\n y = datetime.strptime(y, \"%Y-%m-%dT%H:%M:%SZ\")\n\n if y not in goaway:\n if x == 'Blog':\n x = \"Website\"\n y = f\"[Here!]({y})\"\n text += (\"\\n*{}:* {}\".format(x, y))\n else:\n text += (\"\\n*{}:* `{}`\".format(x, y))\n reply_text = text\n else:\n reply_text = \"User not found. Make sure you entered valid username!\"\n message.reply_text(reply_text,\n parse_mode=ParseMode.MARKDOWN,\n disable_web_page_preview=True)\n\n\ndef repo(update: Update, context: CallbackContext):\n message = update.effective_message\n text = message.text[len('/repo '):]\n usr = get(f'https://api.github.com/users/{text}/repos?per_page=40').json()\n reply_text = \"*Repo*\\n\"\n for i in range(len(usr)):\n reply_text += f\"[{usr[i]['name']}]({usr[i]['html_url']})\\n\"\n message.reply_text(reply_text,\n parse_mode=ParseMode.MARKDOWN,\n disable_web_page_preview=True)\n\n\ndef paste(update: Update, context: CallbackContext):\n args = context.args\n chat = update.effective_chat # type: Optional[Chat]\n BURL = 'https://del.dog'\n message = update.effective_message\n if message.reply_to_message:\n data = message.reply_to_message.text\n elif len(args) >= 1:\n data = message.text.split(None, 1)[1]\n else:\n message.reply_text(tld(chat.id, \"misc_paste_invalid\"))\n return\n\n r = requests.post(f'{BURL}/documents', data=data.encode('utf-8'))\n\n if r.status_code == 404:\n update.effective_message.reply_text(tld(chat.id, \"misc_paste_404\"))\n r.raise_for_status()\n\n res = r.json()\n\n if r.status_code != 200:\n update.effective_message.reply_text(res['message'])\n r.raise_for_status()\n\n key = res['key']\n if res['isUrl']:\n reply = tld(chat.id, \"misc_paste_success\").format(BURL, key, BURL, key)\n else:\n reply = f'{BURL}/{key}'\n update.effective_message.reply_text(reply,\n parse_mode=ParseMode.MARKDOWN,\n disable_web_page_preview=True)\n\n\ndef get_paste_content(update: Update, context: CallbackContext):\n args = context.args\n BURL = 'https://del.dog'\n message = update.effective_message\n chat = update.effective_chat # type: Optional[Chat]\n\n if len(args) >= 1:\n key = args[0]\n else:\n message.reply_text(tld(chat.id, \"misc_get_pasted_invalid\"))\n return\n\n format_normal = f'{BURL}/'\n format_view = f'{BURL}/v/'\n\n if key.startswith(format_view):\n key = key[len(format_view):]\n elif key.startswith(format_normal):\n key = key[len(format_normal):]\n\n r = requests.get(f'{BURL}/raw/{key}')\n\n if r.status_code != 200:\n try:\n res = r.json()\n update.effective_message.reply_text(res['message'])\n except Exception:\n if r.status_code == 404:\n update.effective_message.reply_text(\n tld(chat.id, \"misc_paste_404\"))\n else:\n update.effective_message.reply_text(\n tld(chat.id, \"misc_get_pasted_unknown\"))\n r.raise_for_status()\n\n update.effective_message.reply_text('```' + escape_markdown(r.text) +\n '```',\n parse_mode=ParseMode.MARKDOWN)\n\n\ndef get_paste_stats(update: Update, context: CallbackContext):\n args = context.args\n BURL = 'https://del.dog'\n message = update.effective_message\n chat = update.effective_chat # type: Optional[Chat]\n\n if len(args) >= 1:\n key = args[0]\n else:\n message.reply_text(tld(chat.id, \"misc_get_pasted_invalid\"))\n return\n\n format_normal = f'{BURL}/'\n format_view = f'{BURL}/v/'\n\n if key.startswith(format_view):\n key = key[len(format_view):]\n elif key.startswith(format_normal):\n key = key[len(format_normal):]\n\n r = requests.get(f'{BURL}/documents/{key}')\n\n if r.status_code != 200:\n try:\n res = r.json()\n update.effective_message.reply_text(res['message'])\n except Exception:\n if r.status_code == 404:\n update.effective_message.reply_text(\n tld(chat.id, \"misc_paste_404\"))\n else:\n update.effective_message.reply_text(\n tld(chat.id, \"misc_get_pasted_unknown\"))\n r.raise_for_status()\n\n document = r.json()['document']\n key = document['_id']\n views = document['viewCount']\n reply = f'Stats for **[/{key}]({BURL}/{key})**:\\nViews: `{views}`'\n update.effective_message.reply_text(reply, parse_mode=ParseMode.MARKDOWN)\n\n\ndef ud(update: Update, context: CallbackContext):\n message = update.effective_message\n text = message.text[len('/ud '):]\n if text == '':\n text = \"Cockblocked By Steve Jobs\"\n results = get(\n f'http://api.urbandictionary.com/v0/define?term={text}').json()\n reply_text = f'Word: {text}\\nDefinition: {results[\"list\"][0][\"definition\"]}'\n message.reply_text(reply_text)\n\n\ndef wiki(update: Update, context: CallbackContext):\n kueri = re.split(pattern=\"wiki\", string=update.effective_message.text)\n wikipedia.set_lang(\"en\")\n if len(str(kueri[1])) == 0:\n update.effective_message.reply_text(\"Enter keywords!\")\n else:\n try:\n pertama = update.effective_message.reply_text(\"🔄 Loading...\")\n keyboard = InlineKeyboardMarkup([[\n InlineKeyboardButton(text=\"🔧 More Info...\",\n url=wikipedia.page(kueri).url)\n ]])\n context.bot.editMessageText(chat_id=update.effective_chat.id,\n message_id=pertama.message_id,\n text=wikipedia.summary(kueri,\n sentences=10),\n reply_markup=keyboard)\n except wikipedia.PageError as e:\n update.effective_message.reply_text(\"⚠ Error: {}\".format(e))\n except BadRequest as et:\n update.effective_message.reply_text(\"⚠ Error: {}\".format(et))\n except wikipedia.exceptions.DisambiguationError as eet:\n update.effective_message.reply_text(\n \"⚠ Error\\n There are too many query! Express it more!\\nPossible query result:\\n{}\"\n .format(eet))\n\n\ndef covid(update: Update, context: CallbackContext):\n message = update.effective_message\n chat = update.effective_chat\n country = str(message.text[len('/covid '):])\n if country == '':\n country = \"world\"\n if country.lower() in [\"south korea\", \"korea\"]:\n country = \"s. korea\"\n try:\n c_case = cvid.get_status_by_country_name(country)\n except Exception:\n message.reply_text(tld(chat.id, \"misc_covid_error\"))\n return\n active = format_integer(c_case[\"active\"])\n confirmed = format_integer(c_case[\"confirmed\"])\n country = c_case[\"country\"]\n critical = format_integer(c_case[\"critical\"])\n deaths = format_integer(c_case[\"deaths\"])\n new_cases = format_integer(c_case[\"new_cases\"])\n new_deaths = format_integer(c_case[\"new_deaths\"])\n recovered = format_integer(c_case[\"recovered\"])\n total_tests = c_case[\"total_tests\"]\n if total_tests == 0:\n total_tests = \"N/A\"\n else:\n total_tests = format_integer(c_case[\"total_tests\"])\n reply = tld(chat.id,\n \"misc_covid\").format(country, confirmed, new_cases, active,\n critical, deaths, new_deaths, recovered,\n total_tests)\n message.reply_markdown(reply)\n\n\ndef format_integer(number, thousand_separator=','):\n def reverse(string):\n string = \"\".join(reversed(string))\n return string\n\n s = reverse(str(number))\n count = 0\n result = ''\n for char in s:\n count = count + 1\n if count % 3 == 0:\n if len(s) == count:\n result = char + result\n else:\n result = thousand_separator + char + result\n else:\n result = char + result\n return result\n\n\n__help__ = True\n\nID_HANDLER = DisableAbleCommandHandler(\"id\",\n get_id,\n pass_args=True,\n run_async=True,\n admin_ok=True)\nINFO_HANDLER = DisableAbleCommandHandler(\"info\",\n info,\n pass_args=True,\n run_async=True,\n admin_ok=True)\nGITHUB_HANDLER = DisableAbleCommandHandler(\"git\", github, admin_ok=True)\nREPO_HANDLER = DisableAbleCommandHandler(\"repo\",\n repo,\n pass_args=True,\n run_async=True,\n admin_ok=True)\nMD_HELP_HANDLER = CommandHandler(\"markdownhelp\",\n markdown_help,\n run_async=True,\n filters=Filters.chat_type.private)\n\nSTATS_HANDLER = CommandHandler(\"stats\",\n stats,\n run_async=True)\nGDPR_HANDLER = CommandHandler(\"gdpr\",\n gdpr,\n run_async=True,\n filters=Filters.chat_type.private)\nPASTE_HANDLER = DisableAbleCommandHandler(\"paste\",\n paste,\n pass_args=True,\n run_async=True)\nGET_PASTE_HANDLER = DisableAbleCommandHandler(\"getpaste\",\n get_paste_content,\n pass_args=True,\n run_async=True)\nPASTE_STATS_HANDLER = DisableAbleCommandHandler(\"pastestats\",\n get_paste_stats,\n pass_args=True,\n run_async=True)\nUD_HANDLER = DisableAbleCommandHandler(\"ud\", ud, run_async=True)\nWIKI_HANDLER = DisableAbleCommandHandler(\"wiki\", wiki, run_async=True)\nCOVID_HANDLER = DisableAbleCommandHandler(\"covid\",\n covid,\n run_async=True,\n admin_ok=True)\n\nCONFIG.dispatcher.add_handler(UD_HANDLER)\nCONFIG.dispatcher.add_handler(PASTE_HANDLER)\nCONFIG.dispatcher.add_handler(GET_PASTE_HANDLER)\nCONFIG.dispatcher.add_handler(PASTE_STATS_HANDLER)\nCONFIG.dispatcher.add_handler(ID_HANDLER)\nCONFIG.dispatcher.add_handler(INFO_HANDLER)\nCONFIG.dispatcher.add_handler(MD_HELP_HANDLER)\nCONFIG.dispatcher.add_handler(STATS_HANDLER)\nCONFIG.dispatcher.add_handler(GDPR_HANDLER)\nCONFIG.dispatcher.add_handler(GITHUB_HANDLER)\nCONFIG.dispatcher.add_handler(REPO_HANDLER)\nCONFIG.dispatcher.add_handler(\n DisableAbleCommandHandler(\"removebotkeyboard\", reply_keyboard_remove))\nCONFIG.dispatcher.add_handler(WIKI_HANDLER)\nCONFIG.dispatcher.add_handler(COVID_HANDLER)\n", "sub_path": "group_helper/modules/misc.py", "file_name": "misc.py", "file_ext": "py", "file_size_in_byte": 18542, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "covid.Covid", "line_number": 27, "usage_type": "call"}, {"api_name": "telegram.Update", "line_number": 30, "usage_type": "name"}, {"api_name": "telegram.ext.callbackcontext.CallbackContext", "line_number": 30, "usage_type": "name"}, {"api_name": "group_helper.modules.helper_funcs.extraction.extract_user", "line_number": 32, "usage_type": "call"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 39, "usage_type": "call"}, {"api_name": "telegram.utils.helpers.escape_markdown", "line_number": 40, "usage_type": "call"}, {"api_name": "telegram.utils.helpers.escape_markdown", "line_number": 42, "usage_type": "call"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 47, "usage_type": "call"}, {"api_name": "telegram.utils.helpers.escape_markdown", "line_number": 48, "usage_type": "call"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 54, "usage_type": "call"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 58, "usage_type": "call"}, {"api_name": "telegram.Update", "line_number": 61, "usage_type": "name"}, {"api_name": "telegram.ext.callbackcontext.CallbackContext", "line_number": 61, "usage_type": "name"}, {"api_name": "group_helper.modules.helper_funcs.extraction.extract_user", "line_number": 64, "usage_type": "call"}, {"api_name": "telegram.MessageEntity.TEXT_MENTION", "line_number": 77, "usage_type": "attribute"}, {"api_name": "telegram.MessageEntity", "line_number": 77, "usage_type": "name"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 78, "usage_type": "call"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 84, "usage_type": "call"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 85, "usage_type": "call"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 86, "usage_type": "call"}, {"api_name": "html.escape", "line_number": 87, "usage_type": "call"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 90, "usage_type": "call"}, {"api_name": "html.escape", "line_number": 91, "usage_type": "call"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 94, "usage_type": "call"}, {"api_name": "html.escape", "line_number": 95, "usage_type": "call"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 97, "usage_type": "call"}, {"api_name": "telegram.utils.helpers.mention_html", "line_number": 98, "usage_type": "call"}, {"api_name": "group_helper.CONFIG.owner_id", "line_number": 100, "usage_type": "attribute"}, {"api_name": "group_helper.CONFIG", "line_number": 100, "usage_type": "name"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 101, "usage_type": "call"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 104, "usage_type": "call"}, {"api_name": "group_helper.CONFIG.sudo_users", "line_number": 106, "usage_type": "attribute"}, {"api_name": "group_helper.CONFIG", "line_number": 106, "usage_type": "name"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 107, "usage_type": "call"}, {"api_name": "group_helper.CONFIG.whitelist_users", "line_number": 109, "usage_type": "attribute"}, {"api_name": "group_helper.CONFIG", "line_number": 109, "usage_type": "name"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 110, "usage_type": "call"}, {"api_name": "group_helper.__main__.USER_INFO", "line_number": 112, "usage_type": "name"}, {"api_name": "telegram.ParseMode.HTML", "line_number": 117, "usage_type": "attribute"}, {"api_name": "telegram.ParseMode", "line_number": 117, "usage_type": "name"}, {"api_name": "telegram.Update", "line_number": 120, "usage_type": "name"}, {"api_name": "telegram.ext.callbackcontext.CallbackContext", "line_number": 120, "usage_type": "name"}, {"api_name": "telegram.ReplyKeyboardRemove", "line_number": 122, "usage_type": "call"}, {"api_name": "telegram.ReplyKeyboardRemove", "line_number": 123, "usage_type": "call"}, {"api_name": "telegram.Update", "line_number": 133, "usage_type": "name"}, {"api_name": "telegram.ext.callbackcontext.CallbackContext", "line_number": 133, "usage_type": "name"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 135, "usage_type": "call"}, {"api_name": "group_helper.__main__.GDPR", "line_number": 136, "usage_type": "name"}, {"api_name": "telegram.ParseMode.MARKDOWN", "line_number": 140, "usage_type": "attribute"}, {"api_name": "telegram.ParseMode", "line_number": 140, "usage_type": "name"}, {"api_name": "telegram.Update", "line_number": 143, "usage_type": "name"}, {"api_name": "telegram.ext.callbackcontext.CallbackContext", "line_number": 143, "usage_type": "name"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 145, "usage_type": "call"}, {"api_name": "telegram.ParseMode.HTML", "line_number": 146, "usage_type": "attribute"}, {"api_name": "telegram.ParseMode", "line_number": 146, "usage_type": "name"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 147, "usage_type": "call"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 148, "usage_type": "call"}, {"api_name": "telegram.Update", "line_number": 151, "usage_type": "name"}, {"api_name": "telegram.ext.callbackcontext.CallbackContext", "line_number": 151, "usage_type": "name"}, {"api_name": "group_helper.__main__.STATS", "line_number": 154, "usage_type": "name"}, {"api_name": "telegram.ParseMode.MARKDOWN", "line_number": 155, "usage_type": "attribute"}, {"api_name": "telegram.ParseMode", "line_number": 155, "usage_type": "name"}, {"api_name": "telegram.Update", "line_number": 158, "usage_type": "name"}, {"api_name": "telegram.ext.callbackcontext.CallbackContext", "line_number": 158, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 161, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 190, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 190, "usage_type": "name"}, {"api_name": "telegram.ParseMode.MARKDOWN", "line_number": 203, "usage_type": "attribute"}, {"api_name": "telegram.ParseMode", "line_number": 203, "usage_type": "name"}, {"api_name": "telegram.Update", "line_number": 207, "usage_type": "name"}, {"api_name": "telegram.ext.callbackcontext.CallbackContext", "line_number": 207, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 210, "usage_type": "call"}, {"api_name": "telegram.ParseMode.MARKDOWN", "line_number": 215, "usage_type": "attribute"}, {"api_name": "telegram.ParseMode", "line_number": 215, "usage_type": "name"}, {"api_name": "telegram.Update", "line_number": 219, "usage_type": "name"}, {"api_name": "telegram.ext.callbackcontext.CallbackContext", "line_number": 219, "usage_type": "name"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 229, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 232, "usage_type": "call"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 235, "usage_type": "call"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 246, "usage_type": "call"}, {"api_name": "telegram.ParseMode.MARKDOWN", "line_number": 250, "usage_type": "attribute"}, {"api_name": "telegram.ParseMode", "line_number": 250, "usage_type": "name"}, {"api_name": "telegram.Update", "line_number": 254, "usage_type": "name"}, {"api_name": "telegram.ext.callbackcontext.CallbackContext", "line_number": 254, "usage_type": "name"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 263, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 274, "usage_type": "call"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 283, "usage_type": "call"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 286, "usage_type": "call"}, {"api_name": "telegram.utils.helpers.escape_markdown", "line_number": 289, "usage_type": "call"}, {"api_name": "telegram.ParseMode.MARKDOWN", "line_number": 291, "usage_type": "attribute"}, {"api_name": "telegram.ParseMode", "line_number": 291, "usage_type": "name"}, {"api_name": "telegram.Update", "line_number": 294, "usage_type": "name"}, {"api_name": "telegram.ext.callbackcontext.CallbackContext", "line_number": 294, "usage_type": "name"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 303, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 314, "usage_type": "call"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 323, "usage_type": "call"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 326, "usage_type": "call"}, {"api_name": "telegram.ParseMode.MARKDOWN", "line_number": 333, "usage_type": "attribute"}, {"api_name": "telegram.ParseMode", "line_number": 333, "usage_type": "name"}, {"api_name": "telegram.Update", "line_number": 336, "usage_type": "name"}, {"api_name": "telegram.ext.callbackcontext.CallbackContext", "line_number": 336, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 341, "usage_type": "call"}, {"api_name": "telegram.Update", "line_number": 347, "usage_type": "name"}, {"api_name": "telegram.ext.callbackcontext.CallbackContext", "line_number": 347, "usage_type": "name"}, {"api_name": "re.split", "line_number": 348, "usage_type": "call"}, {"api_name": "wikipedia.set_lang", "line_number": 349, "usage_type": "call"}, {"api_name": "telegram.InlineKeyboardMarkup", "line_number": 355, "usage_type": "call"}, {"api_name": "telegram.InlineKeyboardButton", "line_number": 356, "usage_type": "call"}, {"api_name": "wikipedia.page", "line_number": 357, "usage_type": "call"}, {"api_name": "wikipedia.summary", "line_number": 361, "usage_type": "call"}, {"api_name": "wikipedia.PageError", "line_number": 364, "usage_type": "attribute"}, {"api_name": "telegram.error.BadRequest", "line_number": 366, "usage_type": "name"}, {"api_name": "wikipedia.exceptions", "line_number": 368, "usage_type": "attribute"}, {"api_name": "telegram.Update", "line_number": 374, "usage_type": "name"}, {"api_name": "telegram.ext.callbackcontext.CallbackContext", "line_number": 374, "usage_type": "name"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 385, "usage_type": "call"}, {"api_name": "group_helper.modules.tr_engine.strings.tld", "line_number": 400, "usage_type": "call"}, {"api_name": "group_helper.modules.disable.DisableAbleCommandHandler", "line_number": 429, "usage_type": "call"}, {"api_name": "group_helper.modules.disable.DisableAbleCommandHandler", "line_number": 434, "usage_type": "call"}, {"api_name": "group_helper.modules.disable.DisableAbleCommandHandler", "line_number": 439, "usage_type": "call"}, {"api_name": "group_helper.modules.disable.DisableAbleCommandHandler", "line_number": 440, "usage_type": "call"}, {"api_name": "telegram.ext.CommandHandler", "line_number": 445, "usage_type": "call"}, {"api_name": "telegram.ext.Filters.chat_type", "line_number": 448, "usage_type": "attribute"}, {"api_name": "telegram.ext.Filters", "line_number": 448, "usage_type": "name"}, {"api_name": "telegram.ext.CommandHandler", "line_number": 450, "usage_type": "call"}, {"api_name": "telegram.ext.CommandHandler", "line_number": 453, "usage_type": "call"}, {"api_name": "telegram.ext.Filters.chat_type", "line_number": 456, "usage_type": "attribute"}, {"api_name": "telegram.ext.Filters", "line_number": 456, "usage_type": "name"}, {"api_name": "group_helper.modules.disable.DisableAbleCommandHandler", "line_number": 457, "usage_type": "call"}, {"api_name": "group_helper.modules.disable.DisableAbleCommandHandler", "line_number": 461, "usage_type": "call"}, {"api_name": "group_helper.modules.disable.DisableAbleCommandHandler", "line_number": 465, "usage_type": "call"}, {"api_name": "group_helper.modules.disable.DisableAbleCommandHandler", "line_number": 469, "usage_type": "call"}, {"api_name": "group_helper.modules.disable.DisableAbleCommandHandler", "line_number": 470, "usage_type": "call"}, {"api_name": "group_helper.modules.disable.DisableAbleCommandHandler", "line_number": 471, "usage_type": "call"}, {"api_name": "group_helper.CONFIG.dispatcher.add_handler", "line_number": 476, "usage_type": "call"}, {"api_name": "group_helper.CONFIG.dispatcher", "line_number": 476, "usage_type": "attribute"}, {"api_name": "group_helper.CONFIG", "line_number": 476, "usage_type": "name"}, {"api_name": "group_helper.CONFIG.dispatcher.add_handler", "line_number": 477, "usage_type": "call"}, {"api_name": "group_helper.CONFIG.dispatcher", "line_number": 477, "usage_type": "attribute"}, {"api_name": "group_helper.CONFIG", "line_number": 477, "usage_type": "name"}, {"api_name": "group_helper.CONFIG.dispatcher.add_handler", "line_number": 478, "usage_type": "call"}, {"api_name": "group_helper.CONFIG.dispatcher", "line_number": 478, "usage_type": "attribute"}, {"api_name": "group_helper.CONFIG", "line_number": 478, "usage_type": "name"}, {"api_name": "group_helper.CONFIG.dispatcher.add_handler", "line_number": 479, "usage_type": "call"}, {"api_name": "group_helper.CONFIG.dispatcher", "line_number": 479, "usage_type": "attribute"}, {"api_name": "group_helper.CONFIG", "line_number": 479, "usage_type": "name"}, {"api_name": "group_helper.CONFIG.dispatcher.add_handler", "line_number": 480, "usage_type": "call"}, {"api_name": "group_helper.CONFIG.dispatcher", "line_number": 480, "usage_type": "attribute"}, {"api_name": "group_helper.CONFIG", "line_number": 480, "usage_type": "name"}, {"api_name": "group_helper.CONFIG.dispatcher.add_handler", "line_number": 481, "usage_type": "call"}, {"api_name": "group_helper.CONFIG.dispatcher", "line_number": 481, "usage_type": "attribute"}, {"api_name": "group_helper.CONFIG", "line_number": 481, "usage_type": "name"}, {"api_name": "group_helper.CONFIG.dispatcher.add_handler", "line_number": 482, "usage_type": "call"}, {"api_name": "group_helper.CONFIG.dispatcher", "line_number": 482, "usage_type": "attribute"}, {"api_name": "group_helper.CONFIG", "line_number": 482, "usage_type": "name"}, {"api_name": "group_helper.CONFIG.dispatcher.add_handler", "line_number": 483, "usage_type": "call"}, {"api_name": "group_helper.CONFIG.dispatcher", "line_number": 483, "usage_type": "attribute"}, {"api_name": "group_helper.CONFIG", "line_number": 483, "usage_type": "name"}, {"api_name": "group_helper.CONFIG.dispatcher.add_handler", "line_number": 484, "usage_type": "call"}, {"api_name": "group_helper.CONFIG.dispatcher", "line_number": 484, "usage_type": "attribute"}, {"api_name": "group_helper.CONFIG", "line_number": 484, "usage_type": "name"}, {"api_name": "group_helper.CONFIG.dispatcher.add_handler", "line_number": 485, "usage_type": "call"}, {"api_name": "group_helper.CONFIG.dispatcher", "line_number": 485, "usage_type": "attribute"}, {"api_name": "group_helper.CONFIG", "line_number": 485, "usage_type": "name"}, {"api_name": "group_helper.CONFIG.dispatcher.add_handler", "line_number": 486, "usage_type": "call"}, {"api_name": "group_helper.CONFIG.dispatcher", "line_number": 486, "usage_type": "attribute"}, {"api_name": "group_helper.CONFIG", "line_number": 486, "usage_type": "name"}, {"api_name": "group_helper.CONFIG.dispatcher.add_handler", "line_number": 487, "usage_type": "call"}, {"api_name": "group_helper.CONFIG.dispatcher", "line_number": 487, "usage_type": "attribute"}, {"api_name": "group_helper.CONFIG", "line_number": 487, "usage_type": "name"}, {"api_name": "group_helper.modules.disable.DisableAbleCommandHandler", "line_number": 488, "usage_type": "call"}, {"api_name": "group_helper.CONFIG.dispatcher.add_handler", "line_number": 489, "usage_type": "call"}, {"api_name": "group_helper.CONFIG.dispatcher", "line_number": 489, "usage_type": "attribute"}, {"api_name": "group_helper.CONFIG", "line_number": 489, "usage_type": "name"}, {"api_name": "group_helper.CONFIG.dispatcher.add_handler", "line_number": 490, "usage_type": "call"}, {"api_name": "group_helper.CONFIG.dispatcher", "line_number": 490, "usage_type": "attribute"}, {"api_name": "group_helper.CONFIG", "line_number": 490, "usage_type": "name"}]} +{"seq_id": "618212552", "text": "#! /usr/bin/env python\n# -*- encoding: UTF-8 -*-\n\nimport cv2\nfrom keras.models import load_model\nimport numpy as np\nimport dlib\n\nswitch = True\nglobal frame\n\n\ndef preprocess_input(x, v2=True):\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\n\ndef start():\n # 加载模型\n gender_model_path = '/home/jiashi/Desktop/Link to RoboCup2019/gender_predict/model/simple_CNN.81-0.96.hdf5'\n gender_classifier = load_model(gender_model_path, compile=False)\n gender_target_size = gender_classifier.input_shape[1:3]\n\n num_man = 0\n num_woman = 0\n # 框住人脸的矩形边框颜色\n color = (0, 255, 0)\n\n gender_labels = {0: 'woman', 1: 'man'}\n # 人脸识别分类器本地存储路径\n cascade_path = \"/usr/local/share/OpenCV/haarcascades/haarcascade_frontalface_alt2.xml\"\n detector = dlib.get_frontal_face_detector()\n # 循环检测识别人脸\n num = 0\n if switch == True:\n frame = cv2.imread(\"/home/jiashi/Desktop/Link to RoboCup2019/person_image/image_0.jpg\")\n # 图像灰化，降低计算复杂度\n frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n faceRects = detector(frame_gray, 0)\n '''\n # 使用人脸识别分类器，读入分类器\n cascade = cv2.CascadeClassifier(cascade_path)\n # 利用分类器识别出哪个区域为人脸\n faceRects = cascade.detectMultiScale(frame_gray, scaleFactor=1.2, minNeighbors=3, minSize=(32, 32))\n '''\n if len(faceRects) > 0:\n for faceRect in faceRects:\n print(faceRect)\n num += 1\n x, y, w, h = faceRect.left(), faceRect.top(), faceRect.right(), faceRect.bottom()\n # 截取脸部图像提交给模型识别这是谁\n image = frame_gray[y - 10: h + 10, x - 10: w + 10]\n gray_face = cv2.resize(image, (gender_target_size))\n gray_face = np.expand_dims(gray_face, 0)\n # gray_face = np.expand_dims(gray_face, -1)\n gray_face = preprocess_input(gray_face, False)\n faceRects = gender_classifier.predict(gray_face)\n gender_label_arg = np.argmax(faceRects)\n\n gender_text = gender_labels[gender_label_arg]\n if gender_text == 'man':\n num_man += 1\n elif gender_text == 'woman':\n num_woman += 1\n cv2.rectangle(frame, (x - 10, y - 10), (w + 10, h + 10), color, thickness=2)\n # 文字提示是谁\n cv2.putText(frame, gender_text, (x + 30, y + 30), # 坐标\n cv2.FONT_HERSHEY_SIMPLEX, # 字体\n 1, # 字号\n (255, 0, 255), # 颜色\n 2) # 字的线宽\n cv2.namedWindow(\"aaa\", cv2.WINDOW_NORMAL)\n cv2.imwrite(\"/home/jiashi/Desktop/Link to RoboCup2019/person_image/image_result.jpg\", frame)\n cv2.imshow(\"aaa\", frame)\n # 等待10毫秒看是否有按键输入\n cv2.waitKey(1)\n # 如果输入q则退出循环\n # 释放摄像头并销毁所有窗口\n # cap.release()\n cv2.destroyAllWindows()\n return num_man, num_woman\n\n\n#if __name__ == '__main__':\n# start()", "sub_path": "find_person/gender_predict/image_predict.py", "file_name": "image_predict.py", "file_ext": "py", "file_size_in_byte": 3330, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "keras.models.load_model", "line_number": 25, "usage_type": "call"}, {"api_name": "dlib.get_frontal_face_detector", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 42, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 62, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 69, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 71, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 72, "usage_type": "attribute"}, {"api_name": "cv2.namedWindow", "line_number": 76, "usage_type": "call"}, {"api_name": "cv2.WINDOW_NORMAL", "line_number": 76, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 77, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 78, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 80, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 84, "usage_type": "call"}]} +{"seq_id": "385540992", "text": "#######################################################################\n# Tests for JobRunner.py module\n#######################################################################\nfrom JobRunner import *\nimport bcf_utils\nimport unittest\nimport tempfile\nimport shutil\n\nclass TestSimpleJobRunner(unittest.TestCase):\n\n def setUp(self):\n # Create a temporary directory to work in\n self.working_dir = tempfile.mkdtemp()\n\n def cleanUp(self):\n shutil.rmtree(self.working_dir)\n\n def test_simple_job_runner(self):\n \"\"\"Test SimpleJobRunner with basic shell command\n\n \"\"\"\n # Create a runner and execute the echo command\n runner = SimpleJobRunner()\n jobid = runner.run('test',self.working_dir,'echo','this is a test')\n while runner.isRunning(jobid):\n # Wait for job to finish\n pass\n # Check outputs\n self.assertEqual(runner.name(jobid),'test')\n self.assertTrue(os.path.isfile(runner.logFile(jobid)))\n self.assertTrue(os.path.isfile(runner.errFile(jobid)))\n # Check log files are in the working directory\n self.assertEqual(os.path.dirname(runner.logFile(jobid)),self.working_dir)\n self.assertEqual(os.path.dirname(runner.errFile(jobid)),self.working_dir)\n\n def test_simple_job_runner_join_logs(self):\n \"\"\"Test SimpleJobRunner joining stderr to stdout\n\n \"\"\"\n # Create a runner and execute the echo command\n runner = SimpleJobRunner(join_logs=True)\n jobid = runner.run('test',self.working_dir,'echo','this is a test')\n while runner.isRunning(jobid):\n # Wait for job to finish\n pass\n # Check outputs\n self.assertEqual(runner.name(jobid),'test')\n self.assertTrue(os.path.isfile(runner.logFile(jobid)))\n self.assertEqual(runner.errFile(jobid),None)\n # Check log file is in the working directory\n self.assertEqual(os.path.dirname(runner.logFile(jobid)),self.working_dir)\n\nclass TestGEJobRunner(unittest.TestCase):\n\n def setUp(self):\n # Skip the test if Grid Engine not available\n if bcf_utils.find_program('qstat') is None:\n raise unittest.SkipTest(\"'qstat' not found, Grid Engine not available\")\n # Create a temporary directory to work in\n self.working_dir = tempfile.mkdtemp(dir=os.getcwd())\n\n def cleanUp(self):\n shutil.rmtree(self.working_dir)\n\n def test_ge_job_runner(self):\n \"\"\"Test GEJobRunner with basic shell command\n\n \"\"\"\n # Create a runner and execute the echo command\n runner = GEJobRunner()\n try:\n jobid = runner.run('test',self.working_dir,'echo','this is a test')\n except OSError:\n self.cleanUp() # Not sure why but should do clean up manually\n self.fail(\"Unable to run GE job\")\n poll_interval = 5\n ntries = 0\n while runner.isRunning(jobid) or not os.path.exists(runner.logFile(jobid)):\n # Wait for job to finish\n ntries += 1\n if ntries > 30:\n self.cleanUp() # Not sure why but should do clean up manually\n self.fail(\"Timed out waiting for test job\")\n else:\n time.sleep(poll_interval)\n # Check outputs\n self.assertEqual(runner.name(jobid),'test')\n self.assertTrue(os.path.isfile(runner.logFile(jobid)))\n # Check log files are in the working directory\n self.assertEqual(os.path.dirname(runner.logFile(jobid)),self.working_dir)\n\n#######################################################################\n# Main program\n#######################################################################\n\nif __name__ == \"__main__\":\n # Turn off most logging output for tests\n logging.getLogger().setLevel(logging.CRITICAL)\n # Run tests\n unittest.main()\n", "sub_path": "share/test_JobRunner.py", "file_name": "test_JobRunner.py", "file_ext": "py", "file_size_in_byte": 3865, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "unittest.TestCase", "line_number": 10, "usage_type": "attribute"}, {"api_name": "tempfile.mkdtemp", "line_number": 14, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 17, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 54, "usage_type": "attribute"}, {"api_name": "bcf_utils.find_program", "line_number": 58, "usage_type": "call"}, {"api_name": "unittest.SkipTest", "line_number": 59, "usage_type": "call"}, {"api_name": "tempfile.mkdtemp", "line_number": 61, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 64, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 101, "usage_type": "call"}]} +{"seq_id": "71230920", "text": "#!/usr/bin/env python\nimport os\nfrom scipy.ndimage import gaussian_filter\nfrom skimage import restoration, io\nfrom scipy import ndimage\nimport numpy\nimport numpy as np\nfrom scipy import interpolate\nfrom hybescope_config.microscope_config import *\nfrom fish_results import *\nfrom metadata import Metadata\nfrom functools import partial\nimport importlib\nimport multiprocessing\nimport pickle\nimport traceback\nfrom flowdec import data as fd_data\nfrom flowdec import restoration as fd_restoration\nfrom tensorflow.python.client import device_lib\n\nif __name__ == '__main__':\n import argparse\n parser = argparse.ArgumentParser()\n parser.add_argument(\"md_path\", type=str, help=\"Path to root of imaging folder to initialize metadata.\")\n parser.add_argument(\"cword_config\", type=str, help=\"Path to python file initializing the codewords and providing bitmap variable.\")\n parser.add_argument(\"tforms_path\", type=str, help=\"Path pickle dictionary of tforms per position name.\")\n parser.add_argument(\"out_path\", type=str, help=\"Path to save output.\")\n parser.add_argument(\"-p\", \"--nthreads\", type=int, dest=\"ncpu\", default=8, action='store', help=\"Number of cores to utilize (default 4).\")\n parser.add_argument(\"-k\", type=int, dest=\"k\", default=2, action='store', help=\"Number z-slices above and below to max project together.\")\n parser.add_argument(\"-s\", \"--zstart\", type=int, dest=\"zstart\", default=4, action='store', help=\"Start making max projections centered at zstart.\")\n parser.add_argument(\"-m\", \"--zmax\", type=int, dest=\"zmax\", default=15, action='store', help=\"End making max projections centered at zmax.\")\n parser.add_argument(\"-i\", \"--zskip\", type=int, dest=\"zskip\", default=4, action='store', help=\"Skip this many z-slices between centers of max projections.\")\n parser.add_argument(\"--decon_iters\", type=int, dest=\"niter\", default=25, action='store', help=\"Skip this many z-slices between centers of max projections.\")\n parser.add_argument(\"--decon_gpu\", type=int, dest=\"gpu\", default=0, action='store', help=\"Skip this many z-slices between centers of max projections.\")\n parser.add_argument(\"--flatfield\", type=str, dest=\"flatfield_path\", default='/home/rfor10/repos/pyspots/hybescope_config/flatfields_october2018.pkl', action='store', help=\"Path to dictionary with flatfield matrix for each channel.\")\n\n args = parser.parse_args()\n \ndef hdata_multi_z_pseudo_maxprjZ_wrapper(pos_hdata, posname, tforms_xy, tforms_z, md_path, bitmap, cstk_save_dir, reg_ref='hybe1', zstart=5, k=2, zskip=4, zmax=26, ndecon_iter = 20):\n codestacks = {}\n norm_factors = {}\n class_imgs = {}\n for z_i in list(range(zstart, zmax, zskip)):\n try:\n cstk, nf = pseudo_maxproject_positions_and_tform(posname, md_path, tforms_xy, tforms_z, bitmap, zstart=z_i, k=k)\n pos_hdata.add_and_save_data(cstk, posname, z_i, 'cstk')\n pos_hdata.add_and_save_data(nf, posname, z_i, 'nf')\n pos_hdata.add_and_save_data(np.zeros((cstk.shape[0], cstk.shape[1])), posname, z_i, 'cimg')\n except Exception as e:\n pos_hdata.remove_metadata_by_zindex(z_i)\n print(e)\n return 'Failed'\n return 'Passed'\n #codestacks[z_i] = cstk.astype('uint16')\n #norm_factors[z_i] = nf\n #class_imgs[z_i] = np.empty((cstk.shape[0], cstk.shape[1]))\n# if not os.path.exists(cstk_save_dir):\n# os.makedirs(cstk_save_dir)\n# np.savez(os.path.join(cstk_save_dir, posname), cstks=codestacks, \n# norm_factors = norm_factors, class_imgs = class_imgs)\ndef pfunc_img_process(img, channel, hybe, t_xy, fltfield, niter=0):\n img = np.divide(img, fltfield)\n img = tform_image(img, channel, t_xy, niter=niter)\n return img\ndef pseudo_maxproject_positions_and_tform(posname, md_path, tforms_xy, tforms_z, bitmap, zstart=6, k=2, reg_ref = 'hybe1', ndecon_iter=20, nf_init_qtile=95):\n \"\"\"\n Wrapper for multiple Z codestack where each is max_projection of few frames above and below.\n \"\"\"\n global flatfield_dict, use_gpu\n md = Metadata(md_path)\n xy = tforms_xy\n z = tforms_z\n z = {k: int(np.round(np.mean(v))) for k, v in z.items()}\n z[reg_ref] = 0\n xy[reg_ref] = (0,0)\n seqs, hybes, channels = zip(*bitmap)\n psf_map = {'Orange': orange_psf, 'FarRed': farred_psf, 'Green': green_psf}\n cstk = np.stack([md.stkread(Channel=chan, hybe=hybe, Position=posname, Zindex=list(range(zstart-z[hybe]-k, zstart-z[hybe]+k+1))).max(axis=2) for seq, hybe, chan in bitmap], axis=2)\n \n if use_gpu:\n \n cstk = [dogonvole(cstk[:,:,i], psf_map[chan], niter=ndecon_iter) for i, chan in enumerate(channels)]\n inputs = [(cstk[i], channels[i], hybes[i], xy[hybes[i]], flatfield_dict[channels[i]]) for i in range(len(cstk))]\n with multiprocessing.Pool(8) as ppool:\n cstk = ppool.starmap(pfunc_img_process, inputs)\n else:\n print('did not use GPU')\n inputs = [(cstk[:,:,i], channels[i], hybes[i], xy[hybes[i]], flatfield_dict[channels[i]], ndecon_iter) for i in range(cstk.shape[2])]\n with multiprocessing.Pool(ncpu) as ppool:\n cstk = ppool.starmap(pfunc_img_process, inputs)\n cstk = np.stack(cstk, axis=2)\n nf = np.percentile(cstk, nf_init_qtile, axis=(0, 1))\n return cstk, nf\n\ndef tform_image(cstk, channel, tvect, niter=20):\n \"\"\"\n Warp images to correct chromatic abberation and translational stage drift.\n \n Parameters\n ----------\n cstk : ndarray\n Image(s) to be warped\n channel : str\n Name of channel (used to determine which chromatic warping to apply)\n tvect : tuple\n Tuple of floats to correct for stage drift\n \n Returns\n -------\n cstk : ndarray float32\n Warped image of same shape as input cstk\n \n Notes - Chromatic abberation maps are imported from seqfish_config and as accessed as globals\n \"\"\"\n if channel == 'DeepBlue':\n xs, ys = xshift_db+tvect[1], yshift_db+tvect[0]\n #return cstk.astype('float32')\n if channel == 'Orange':\n xs, ys = xshift_o+tvect[1], yshift_o+tvect[0]\n if niter>0:\n cstk = dogonvole(cstk, orange_psf, niter=niter)\n elif channel=='Green':\n xs, ys = numpy.linspace(0, 2047, 2048)+tvect[1], numpy.linspace(0, 2047, 2048)+tvect[0]\n if niter>0:\n cstk = dogonvole(cstk, green_psf, niter=niter)\n elif channel=='FarRed':\n xs, ys = xshift_fr+tvect[1], yshift_fr+tvect[0]\n if niter>0:\n cstk = dogonvole(cstk, farred_psf, niter=niter)\n cstk = interp_warp(cstk, xs, ys)\n return cstk.astype('float32')\n\ndef interp_warp(img, x, y):\n \"\"\"\n Apply chromatic abberation shifts to images.\n \n Parameters\n ----------\n img : ndarray\n x : array\n y : array\n \n Returns\n -------\n nimg : ndarray - same size as img but interpolated from x, y onto 0, 1, ... , img.shape\n \"\"\"\n i2 = interpolate.interp2d(x, y, img)\n nimg = i2(range(img.shape[0]), range(img.shape[1]))\n return nimg\n\ndef dogonvole(image, psf, kernel=(2., 2., 0.), blur=(1.2, 1.2, 0.), niter=20):\n \"\"\"\n Perform deconvolution and difference of gaussian processing.\n\n Parameters\n ----------\n image : ndarray\n psf : ndarray\n kernel : tuple\n blur : tuple\n niter : int\n\n Returns\n -------\n image : ndarray\n Processed image same shape as image input.\n \"\"\"\n global hot_pixels, use_gpu, gpu_algorithm\n if not psf.sum() == 1.:\n raise ValueError(\"psf must be normalized so it sums to 1\")\n image = image.astype('float32')\n imin = image.min()\n for y, x in hot_pixels:\n image[y, x] = imin;\n \n img_bg = ndimage.gaussian_filter(image, kernel[:len(image.shape)])\n image = numpy.subtract(image, img_bg)\n numpy.place(image, image<0, 1./2**16)\n image = image.astype('uint16')\n if len(image.shape)==3:\n for i in range(image.shape[2]):\n if use_gpu==1:\n image[:,:,i] = gpu_algorithm.run(fd_data.Acquisition(data=image, kernel=psf), niter=niter).data\n else:\n image[:,:,i] = restoration.richardson_lucy(image[:,:,i], psf,\n niter, clip=False)\n elif len(image.shape)==2:\n if use_gpu==1:\n image = gpu_algorithm.run(fd_data.Acquisition(data=image, kernel=psf), niter=niter).data\n else:\n image = restoration.richardson_lucy(image, psf, niter, clip=False)\n else:\n raise ValueError('image is not a supported dimensionality.')\n image = ndimage.gaussian_filter(image, blur[:len(image.shape)])\n return image\n\n \nif __name__=='__main__':\n niter = args.niter\n md_path = args.md_path\n k = args.k\n zstart = args.zstart\n zskip = args.zskip\n zmax = args.zmax\n out_path = args.out_path\n use_gpu = args.gpu\n ncpu = args.ncpu\n flatfield_path = args.flatfield_path\n flatfield_dict = pickle.load(open(flatfield_path, 'rb'))\n if use_gpu == 1:\n print(device_lib.list_local_devices())\n gpu_algorithm = fd_restoration.RichardsonLucyDeconvolver(2).initialize()\n# assert(ncpu==1, 'If using GPU only use single-threaded to prevent conflicts with GPU usage.')\n os.environ['MKL_NUM_THREADS'] = '16'\n os.environ['GOTO_NUM_THREADS'] = '16'\n os.environ['OMP_NUM_THREADS'] = '16'\n if not os.path.exists(out_path):\n os.makedirs(out_path)\n \n os.environ['MKL_NUM_THREADS'] = '4'\n os.environ['GOTO_NUM_THREADS'] = '4'\n os.environ['OMP_NUM_THREADS'] = '4'\n print(args)\n seqfish_config = importlib.import_module(args.cword_config)\n pfunc = partial(hdata_multi_z_pseudo_maxprjZ_wrapper, md_path=args.md_path, bitmap=seqfish_config.bitmap, k=args.k, zstart=args.zstart, zskip=args.zskip, zmax=args.zmax, cstk_save_dir=args.out_path, ndecon_iter=niter)\n good_positions = pickle.load(open(args.tforms_path, 'rb'))['good']\n func_inputs = []\n for p, t in good_positions.items():\n tforms_xyz = {k: (v[0][0], v[0][1], int(np.round(np.mean(v[0][2])))) for k, v in t.items()}\n txy = {k: (v[0], v[1]) for k, v in tforms_xyz.items()}\n tzz = {k: v[2] for k, v in tforms_xyz.items()}\n func_inputs.append((HybeData(os.path.join(out_path, p)), p, txy, tzz))\n if (ncpu>1) and not use_gpu:\n with multiprocessing.Pool(ncpu) as ppool:\n ppool.starmap(pfunc, func_inputs)\n else:\n for hdata, pos, txy, tzz in func_inputs:\n pfunc(hdata, pos, txy, tzz)\n", "sub_path": "analysis_scripts/decon_codestacks.py", "file_name": "decon_codestacks.py", "file_ext": "py", "file_size_in_byte": 10569, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 62, "usage_type": "call"}, {"api_name": "metadata.Metadata", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 78, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 84, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 123, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp2d", "line_number": 147, "usage_type": "call"}, {"api_name": "scipy.interpolate", "line_number": 147, "usage_type": "name"}, {"api_name": "scipy.ndimage.gaussian_filter", "line_number": 176, "usage_type": "call"}, {"api_name": "scipy.ndimage", "line_number": 176, "usage_type": "name"}, {"api_name": "numpy.subtract", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.place", "line_number": 178, "usage_type": "call"}, {"api_name": "flowdec.data.Acquisition", "line_number": 183, "usage_type": "call"}, {"api_name": "flowdec.data", "line_number": 183, "usage_type": "name"}, {"api_name": "skimage.restoration.richardson_lucy", "line_number": 185, "usage_type": "call"}, {"api_name": "skimage.restoration", "line_number": 185, "usage_type": "name"}, {"api_name": "flowdec.data.Acquisition", "line_number": 189, "usage_type": "call"}, {"api_name": "flowdec.data", "line_number": 189, "usage_type": "name"}, {"api_name": "skimage.restoration.richardson_lucy", "line_number": 191, "usage_type": "call"}, {"api_name": "skimage.restoration", "line_number": 191, "usage_type": "name"}, {"api_name": "scipy.ndimage.gaussian_filter", "line_number": 194, "usage_type": "call"}, {"api_name": "scipy.ndimage", "line_number": 194, "usage_type": "name"}, {"api_name": "pickle.load", "line_number": 209, "usage_type": "call"}, {"api_name": "tensorflow.python.client.device_lib.list_local_devices", "line_number": 211, "usage_type": "call"}, {"api_name": "tensorflow.python.client.device_lib", "line_number": 211, "usage_type": "name"}, {"api_name": "flowdec.restoration.RichardsonLucyDeconvolver", "line_number": 212, "usage_type": "call"}, {"api_name": "flowdec.restoration", "line_number": 212, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 214, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 215, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 216, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 217, "usage_type": "call"}, {"api_name": "os.path", "line_number": 217, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 218, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 220, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 221, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 222, "usage_type": "attribute"}, {"api_name": "importlib.import_module", "line_number": 224, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 225, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 229, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 229, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 232, "usage_type": "call"}, {"api_name": "os.path", "line_number": 232, "usage_type": "attribute"}, {"api_name": "multiprocessing.Pool", "line_number": 234, "usage_type": "call"}]} +{"seq_id": "226153224", "text": "from tqdm import tqdm\nfrom nltk.corpus import stopwords\nimport re\n\n\ndef load_run(run_path, run_type='trec'):\n run = {}\n with open(run_path, 'r') as f:\n for line in tqdm(f, desc=\"loading run....\"):\n if run_type == 'msmarco':\n qid, docid, score = line.strip().split(\"\\t\")\n elif run_type == 'trec':\n qid, _, docid, rank, score, _ = line.strip().split(\" \")\n qid = int(qid)\n docid = int(docid)\n if qid not in run.keys():\n run[qid] = []\n run[qid].append(docid)\n return run\n\n\ndef load_collection(collection_path):\n collection = {}\n with open(collection_path, 'r') as f:\n for line in tqdm(f, desc=\"loading collection....\"):\n docid, text = line.strip().split(\"\\t\")\n collection[int(docid)] = text\n return collection\n\n\ndef load_queries(query_path):\n query = {}\n with open(query_path, 'r') as f:\n for line in tqdm(f, desc=\"loading query....\"):\n qid, text = line.strip().split(\"\\t\")\n query[int(qid)] = text\n return query\n\n\ndef get_batch_text(start, end, docids, collection):\n batch_text = []\n for docid in docids[start: end]:\n batch_text.append(collection[docid])\n return batch_text\n\n\ndef clean_vacab(tokenizer, do_stopwords=True):\n if do_stopwords:\n stop_words = set(stopwords.words('english'))\n # keep some common words in ms marco questions\n stop_words.difference_update([\"where\", \"how\", \"what\", \"when\", \"which\", \"why\", \"who\"])\n\n vocab = tokenizer.get_vocab()\n tokens = vocab.keys()\n\n # good_token = []\n good_ids = []\n # bad_token = []\n bad_ids = []\n\n for stop_word in stop_words:\n ids = tokenizer(stop_word, add_special_tokens=False)[\"input_ids\"]\n if len(ids) == 1:\n # bad_token.append(stop_word)\n bad_ids.append(ids[0])\n\n for token in tokens:\n token_id = vocab[token]\n if token_id in bad_ids:\n continue\n\n if token[0] == '#' and len(token) > 1:\n # bad_token.append(token)\n good_ids.append(token_id)\n else:\n if not re.match(\"^[A-Za-z0-9_-]*$\", token):\n # bad_token.append(token)\n bad_ids.append(token_id)\n else:\n # good_token.append(token)\n good_ids.append(token_id)\n\n return good_ids, bad_ids\n", "sub_path": "scripts/utility.py", "file_name": "utility.py", "file_ext": "py", "file_size_in_byte": 2433, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "tqdm.tqdm", "line_number": 9, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 25, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 34, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 49, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 49, "usage_type": "name"}, {"api_name": "re.match", "line_number": 76, "usage_type": "call"}]} +{"seq_id": "170064953", "text": "from django.test import TestCase\n\nfrom .serializers import QuizSerializer, ArticleSerializer\nfrom .models import * # noqa\n\n\nclass ArticleCase(TestCase):\n def test_serialization(self):\n feature_type = FeatureType.objects.create(name=\"A.V. Q&A\")\n article = Article.objects.create(title=\"Some thinkpiece\", feature_type=feature_type)\n\n serializer = ArticleSerializer(instance=article)\n\n self.assertEqual(serializer.data, {\n 'id': article.id,\n 'title': 'Some thinkpiece',\n 'feature_type': {\n 'id': feature_type.id,\n 'name': 'A.V. Q&A'\n },\n 'tags': []\n })\n\n def test_nested_create(self):\n self.assertEqual(FeatureType.objects.count(), 0)\n serializer = ArticleSerializer(data={\n 'title': 'testing',\n 'feature_type': {\n 'name': 'testing'\n }\n })\n assert serializer.is_valid()\n _ = serializer.save()\n\n self.assertEqual(FeatureType.objects.count(), 1)\n\n ft = FeatureType.objects.get()\n serializer = ArticleSerializer(data={\n 'title': 'testing 2',\n 'feature_type': {\n 'id': ft.id,\n 'name': ft.name\n }\n })\n assert serializer.is_valid()\n _ = serializer.save()\n\n self.assertEqual(FeatureType.objects.count(), 1)\n\n def test_nested_list_create(self):\n \"\"\"POST\n {\n \"title\": \"whatever\",\n \"feature_type\": {\n \"name\": \"some new feature type\"\n },\n \"tags\": [{\n \"id\": 1,\n \"name\": \"some existing tag\"\n }, {\n \"id\": 2,\n \"name\": \"another existing tag\"\n }]\n }\n \"\"\"\n self.assertEqual(Tag.objects.count(), 0)\n tag1 = Tag.objects.create(name='tag uno')\n tag2 = Tag.objects.create(name='tag dos')\n serializer = ArticleSerializer(data={\n 'title': 'lets test some tags',\n 'feature_type': {\n 'name': 'tag tester',\n },\n 'tags': [{\n 'id': tag1.id,\n 'name': tag1.name,\n }, {\n 'id': tag2.id,\n 'name': tag2.name,\n }]\n })\n assert serializer.is_valid()\n _ = serializer.save()\n self.assertEqual(FeatureType.objects.count(), 1)\n self.assertEqual(Tag.objects.count(), 2)\n\n def test_nested_list_create_full(self):\n \"\"\"POST\n {\n \"title\": \"whatever\",\n \"feature_type\": {\n \"name\": \"some new feature type\"\n },\n \"tags\": [{\n \"name\": \"some new tag\"\n }, {\n \"name\": \"another new tag\"\n }]\n }\n \"\"\"\n self.assertEqual(Tag.objects.count(), 0)\n self.assertEqual(Tag.objects.count(), 0)\n serializer = ArticleSerializer(data={\n 'title': 'lets test some tags',\n 'feature_type': {\n 'name': 'tag tester',\n },\n 'tags': [{\n 'name': 'tag 1'\n }, {\n 'name': 'tag 2'\n }]\n })\n assert serializer.is_valid()\n _ = serializer.save()\n\n self.assertEqual(_.feature_type.name, 'tag tester')\n self.assertEqual(_.tags.count(), 2)\n\n self.assertEqual(FeatureType.objects.count(), 1)\n self.assertEqual(Tag.objects.count(), 2)\n\n def test_nested_update(self):\n \"\"\"PUT\n {\n \"title\": \"whatever\",\n \"feature_type\": {\n \"id\": 2,\n \"name\": \"some existing feature type that wasn't the originally used one\"\n }\n }\n \"\"\"\n self.assertEqual(FeatureType.objects.count(), 0)\n serializer = ArticleSerializer(data={\n 'title': 'testing',\n 'feature_type': {\n 'name': 'DVR Club',\n },\n })\n assert serializer.is_valid()\n _ = serializer.save()\n self.assertEqual(FeatureType.objects.count(), 1)\n ft = FeatureType.objects.create(name='AV Undercover')\n\n updated_data = serializer.data\n updated_data['feature_type'] = {\n 'id': ft.id,\n 'name': ft.name,\n }\n serializer = ArticleSerializer(data=updated_data)\n assert serializer.is_valid()\n _ = serializer.save()\n self.assertEqual(_.feature_type.id, ft.id)\n\n def test_nested_list_update(self):\n \"\"\"PUT\n {\n \"title\": \"whatever\",\n \"feature_type\": {\n \"id\": 1,\n \"name\": \"some existing feature type\"\n },\n \"tags\": [{\n \"id\": 1,\n \"name\": \"some existing tag that wasn't originally used in post\"\n }]\n }\n \"\"\"\n serializer = ArticleSerializer(data={\n 'title': 'testing',\n 'feature_type': {\n 'name': 'Blah blah blah',\n },\n })\n assert serializer.is_valid()\n _ = serializer.save()\n self.assertEqual(Tag.objects.count(), 0)\n tag = Tag.objects.create(name='this is a tag')\n\n updated_data = serializer.data\n updated_data['tags'].append({\n 'id': tag.id,\n 'name': tag.name,\n })\n serializer = ArticleSerializer(data=updated_data)\n assert serializer.is_valid()\n _ = serializer.save()\n self.assertEqual(Tag.objects.count(), 1)\n\n def test_nested_list_update_full(self):\n \"\"\"PUT\n {\n \"title\": \"whatever\",\n \"feature_type\": {\n \"id\": 1,\n \"name\": \"some existing feature type\"\n },\n \"tags\": [{\n \"name\": \"some new tag\"\n }]\n }\n \"\"\"\n self.assertEqual(Tag.objects.count(), 0)\n serializer = ArticleSerializer(data={\n 'title': 'testing',\n 'feature_type': {\n 'name': 'Blah blah blah',\n },\n })\n assert serializer.is_valid()\n _ = serializer.save()\n self.assertEqual(Tag.objects.count(), 0)\n\n\n updated_data = serializer.data\n updated_data['tags'].append({\n 'name': 'this is another tag',\n })\n serializer = ArticleSerializer(data=updated_data)\n assert serializer.is_valid()\n _ = serializer.save()\n self.assertEqual(Tag.objects.count(), 1)\n\n\nclass QuizCase(TestCase):\n def setUp(self):\n self.quiz = Quiz.objects.create(title='testing quiz')\n self.outcome = QuizOutcome.objects.create(text='You win', quiz=self.quiz)\n self.question = QuizQuestion.objects.create(quiz=self.quiz, text='What is the meaning?')\n self.answer = QuizAnswer.objects.create(question=self.question, outcome=self.outcome, text=\"42\")\n\n def test_serialization(self):\n serializer = QuizSerializer(instance=self.quiz)\n self.assertEqual(serializer.data, {\n 'id': self.quiz.id,\n 'title': 'testing quiz',\n 'outcome_set': [{\n 'quiz': self.quiz.id,\n 'id': self.outcome.id,\n 'text': 'You win'\n }],\n 'question_set': [{\n 'id': self.question.id,\n 'text': 'What is the meaning?',\n 'quiz': self.quiz.id,\n 'answer_set': [{\n 'id': self.answer.id,\n 'text': '42',\n 'question': self.question.id,\n 'outcome': self.outcome.id\n }]\n }]\n })\n\n def test_update_field(self):\n serializer = QuizSerializer(instance=self.quiz)\n updated_data = serializer.data\n\n updated_data['title'] = 'universe quiz'\n updated_data['outcome_set'][0]['text'] = 'You really won that!'\n\n self.assertEqual(QuizOutcome.objects.count(), 1)\n\n serializer = QuizSerializer(instance=self.quiz, data=updated_data)\n assert serializer.is_valid()\n serializer.save()\n\n self.assertEqual(serializer.data['title'], 'universe quiz')\n self.assertEqual(serializer.data['outcome_set'][0]['id'], self.outcome.id)\n self.assertEqual(serializer.data['outcome_set'][0]['text'], 'You really won that!')\n\n self.assertEqual(QuizOutcome.objects.count(), 1)\n\n quiz = Quiz.objects.get(id=self.quiz.id)\n self.assertEqual(quiz.title, 'universe quiz')\n\n def test_nested_errors(self):\n serializer = QuizSerializer(instance=self.quiz)\n updated_data = serializer.data\n\n updated_data['outcome_set'].append({\n 'quiz': self.quiz.id\n })\n serializer = QuizSerializer(instance=self.quiz, data=updated_data)\n assert serializer.is_valid() is False\n self.assertEqual(serializer.errors, {'outcome_set': [{}, {'text': ['This field is required.']}]})\n\n def test_add_remove_outcome(self):\n serializer = QuizSerializer(instance=self.quiz)\n updated_data = serializer.data\n\n updated_data['outcome_set'].append({\n 'text': 'You lost...',\n 'quiz': self.quiz.id\n })\n\n self.assertEqual(QuizOutcome.objects.count(), 1)\n\n serializer = QuizSerializer(instance=self.quiz, data=updated_data)\n assert serializer.is_valid()\n serializer.save()\n\n self.assertEqual(len(serializer.data['outcome_set']), 2)\n self.assertEqual(QuizOutcome.objects.count(), 2)\n\n updated_data['outcome_set'].pop() # Kill that last item\n\n serializer = QuizSerializer(instance=self.quiz, data=updated_data)\n assert serializer.is_valid()\n serializer.save()\n\n self.assertEqual(len(serializer.data['outcome_set']), 1)\n self.assertEqual(QuizOutcome.objects.count(), 1)\n", "sub_path": "example/app/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 9843, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.test.TestCase", "line_number": 7, "usage_type": "name"}, {"api_name": "serializers.ArticleSerializer", "line_number": 12, "usage_type": "call"}, {"api_name": "serializers.ArticleSerializer", "line_number": 26, "usage_type": "call"}, {"api_name": "serializers.ArticleSerializer", "line_number": 38, "usage_type": "call"}, {"api_name": "serializers.ArticleSerializer", "line_number": 69, "usage_type": "call"}, {"api_name": "serializers.ArticleSerializer", "line_number": 103, "usage_type": "call"}, {"api_name": "serializers.ArticleSerializer", "line_number": 134, "usage_type": "call"}, {"api_name": "serializers.ArticleSerializer", "line_number": 150, "usage_type": "call"}, {"api_name": "serializers.ArticleSerializer", "line_number": 169, "usage_type": "call"}, {"api_name": "serializers.ArticleSerializer", "line_number": 185, "usage_type": "call"}, {"api_name": "serializers.ArticleSerializer", "line_number": 204, "usage_type": "call"}, {"api_name": "serializers.ArticleSerializer", "line_number": 219, "usage_type": "call"}, {"api_name": "django.test.TestCase", "line_number": 225, "usage_type": "name"}, {"api_name": "serializers.QuizSerializer", "line_number": 233, "usage_type": "call"}, {"api_name": "serializers.QuizSerializer", "line_number": 256, "usage_type": "call"}, {"api_name": "serializers.QuizSerializer", "line_number": 264, "usage_type": "call"}, {"api_name": "serializers.QuizSerializer", "line_number": 278, "usage_type": "call"}, {"api_name": "serializers.QuizSerializer", "line_number": 284, "usage_type": "call"}, {"api_name": "serializers.QuizSerializer", "line_number": 289, "usage_type": "call"}, {"api_name": "serializers.QuizSerializer", "line_number": 299, "usage_type": "call"}, {"api_name": "serializers.QuizSerializer", "line_number": 308, "usage_type": "call"}]} +{"seq_id": "105520939", "text": "from typing import Any\nimport WDL\n\n\nclass Base():\n \"\"\"\n Helper base class for traversing the WDL abstract syntax tree. When called\n on a node, invokes the appropriate method (document, workflow, call,\n scatter, conditional, decl, task). The base implementations of these\n methods recurse into the node's \"children.\" Overriding subclasses can thus\n invoke their super at the appropriate point for preorder or postorder\n traversal.\n\n ``\n class PrintUnconditionalCallNames(Walker.Base):\n def conditional(self, obj):\n # skip everything inside conditionals by NOT calling\n # super().conditional(obj)\n pass\n def call(self, obj):\n print(obj.name)\n walker = PrintUnconditionalCallNames()\n walker(wdl_document)\n ``\n \"\"\"\n\n def __init__(self) -> None:\n pass\n\n def __call__(self, obj: WDL.Error.SourceNode) -> Any:\n if isinstance(obj, WDL.Tree.Document):\n return self.document(obj)\n if isinstance(obj, WDL.Tree.Workflow):\n return self.workflow(obj)\n if isinstance(obj, WDL.Tree.Call):\n return self.call(obj)\n if isinstance(obj, WDL.Tree.Scatter):\n return self.scatter(obj)\n if isinstance(obj, WDL.Tree.Conditional):\n return self.conditional(obj)\n if isinstance(obj, WDL.Tree.Decl):\n return self.decl(obj)\n if isinstance(obj, WDL.Tree.Task):\n return self.task(obj)\n if isinstance(obj, WDL.Expr.Base):\n return self.expr(obj)\n assert False\n\n def document(self, obj: WDL.Tree.Document) -> Any:\n for _, _, subdoc in obj.imports:\n assert isinstance(subdoc, WDL.Tree.Document)\n self(subdoc)\n for task in obj.tasks:\n self(task)\n if obj.workflow:\n self(obj.workflow)\n\n def workflow(self, obj: WDL.Tree.Workflow) -> Any:\n for elt in obj.elements:\n self(elt)\n\n def call(self, obj: WDL.Tree.Call) -> Any:\n for _, expr in obj.inputs.items():\n self(expr)\n\n def scatter(self, obj: WDL.Tree.Scatter) -> Any:\n for elt in obj.elements:\n self(elt)\n\n def conditional(self, obj: WDL.Tree.Conditional) -> Any:\n for elt in obj.elements:\n self(elt)\n\n def decl(self, obj: WDL.Tree.Decl) -> Any:\n if obj.expr:\n self(obj.expr)\n\n def task(self, obj: WDL.Tree.Task) -> Any:\n for elt in obj.inputs + obj.postinputs:\n self(elt)\n self(obj.command)\n for elt in obj.outputs:\n self(elt)\n # TODO: traverse runtime section\n\n def expr(self, obj: WDL.Expr.Base) -> Any:\n if isinstance(obj, WDL.Expr.Placeholder):\n self(obj.expr)\n elif isinstance(obj, WDL.Expr.String):\n for p in obj.parts:\n if isinstance(p, WDL.Expr.Base):\n self(p)\n elif isinstance(obj, WDL.Expr.Array):\n for elt in obj.items:\n self(elt)\n elif isinstance(obj, WDL.Expr.IfThenElse):\n self(obj.condition)\n self(obj.consequent)\n self(obj.alternative)\n elif isinstance(obj, WDL.Expr.Apply):\n for elt in obj.arguments:\n self(elt)\n elif isinstance(obj, WDL.Expr.Pair):\n self(obj.left)\n self(obj.right)\n elif isinstance(obj, WDL.Expr.Map):\n for k, v in obj.items.items():\n self(k)\n self(v)\n else:\n pass\n\n\nclass SetParents(Base):\n \"\"\"\n Add ``parent`` to each node.\n\n On Document, the document which imports this document (None at top level)\n\n On Workflow and Task, the containing document.\n\n On Call, Scatter, and Conditional, the containing Workflow, Scatter, or\n Conditional.\n\n On Decl, the contaning Task, Workflow, Scatter, or Conditional.\n\n On each Expr, the containing Decl or (for command placeholders) Task\n \"\"\"\n\n def document(self, obj: WDL.Tree.Document) -> None:\n super().document(obj)\n obj.parent = None\n for _, _, subdoc in obj.imports:\n subdoc.parent = obj\n for task in obj.tasks:\n task.parent = obj\n if obj.workflow:\n obj.workflow.parent = obj\n\n def workflow(self, obj: WDL.Tree.Workflow) -> None:\n super().workflow(obj)\n obj.parent = None\n for elt in obj.elements:\n elt.parent = obj\n\n def scatter(self, obj: WDL.Tree.Scatter) -> None:\n super().scatter(obj)\n obj.parent = None\n for elt in obj.elements:\n elt.parent = obj\n\n def conditional(self, obj: WDL.Tree.Conditional) -> None:\n super().conditional(obj)\n obj.parent = None\n for elt in obj.elements:\n elt.parent = obj\n\n def task(self, obj: WDL.Tree.Task) -> None:\n setattr(self, '_parent_task', obj)\n super().task(obj)\n obj.parent = None\n for elt in obj.inputs + obj.postinputs + obj.outputs:\n elt.parent = obj\n\n def decl(self, obj: WDL.Tree.Decl) -> None:\n setattr(self, '_parent_decl', obj)\n super().decl(obj)\n delattr(self, '_parent_decl')\n\n def expr(self, obj: WDL.Expr.Base) -> None:\n super().expr(obj)\n obj.parent = getattr(self, '_parent_decl',\n getattr(self, '_parent_task'))\n\n\nclass MarkCalled(Base):\n \"\"\"\n Mark each Task and Workflow with ``called : bool`` according to whether\n there exists a Call to it in the top-level workflow (or a subworkflow it\n calls). Requires SetParents to have been applied previously.\n \"\"\"\n marking: bool = False # True while recursing from the top-level workflow\n\n def workflow(self, obj: WDL.Tree.Workflow) -> None:\n obj.called = False\n if obj.parent.parent is None: # pyre-ignore\n assert not self.marking\n self.marking = True\n super().workflow(obj)\n self.marking = False\n elif self.marking:\n super().workflow(obj)\n\n def call(self, obj: WDL.Tree.Call) -> None:\n assert self.marking\n if isinstance(obj.callee, WDL.Tree.Workflow):\n self(obj.callee)\n obj.callee.called = True\n\n def task(self, obj: WDL.Tree.Task) -> None:\n obj.called = False\n", "sub_path": "WDL/Walker.py", "file_name": "Walker.py", "file_ext": "py", "file_size_in_byte": 6377, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "WDL.Error", "line_number": 30, "usage_type": "attribute"}, {"api_name": "WDL.Tree", "line_number": 31, "usage_type": "attribute"}, {"api_name": "WDL.Tree", "line_number": 33, "usage_type": "attribute"}, {"api_name": "WDL.Tree", "line_number": 35, "usage_type": "attribute"}, {"api_name": "WDL.Tree", "line_number": 37, "usage_type": "attribute"}, {"api_name": "WDL.Tree", "line_number": 39, "usage_type": "attribute"}, {"api_name": "WDL.Tree", "line_number": 41, "usage_type": "attribute"}, {"api_name": "WDL.Tree", "line_number": 43, "usage_type": "attribute"}, {"api_name": "WDL.Expr", "line_number": 45, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 30, "usage_type": "name"}, {"api_name": "WDL.Tree", "line_number": 49, "usage_type": "attribute"}, {"api_name": "WDL.Tree", "line_number": 51, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 49, "usage_type": "name"}, {"api_name": "WDL.Tree", "line_number": 58, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 58, "usage_type": "name"}, {"api_name": "WDL.Tree", "line_number": 62, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 62, "usage_type": "name"}, {"api_name": "WDL.Tree", "line_number": 66, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 66, "usage_type": "name"}, {"api_name": "WDL.Tree", "line_number": 70, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 70, "usage_type": "name"}, {"api_name": "WDL.Tree", "line_number": 74, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 74, "usage_type": "name"}, {"api_name": "WDL.Tree", "line_number": 78, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 78, "usage_type": "name"}, {"api_name": "WDL.Expr", "line_number": 86, "usage_type": "attribute"}, {"api_name": "WDL.Expr", "line_number": 87, "usage_type": "attribute"}, {"api_name": "WDL.Expr", "line_number": 89, "usage_type": "attribute"}, {"api_name": "WDL.Expr", "line_number": 91, "usage_type": "attribute"}, {"api_name": "WDL.Expr", "line_number": 93, "usage_type": "attribute"}, {"api_name": "WDL.Expr", "line_number": 96, "usage_type": "attribute"}, {"api_name": "WDL.Expr", "line_number": 100, "usage_type": "attribute"}, {"api_name": "WDL.Expr", "line_number": 103, "usage_type": "attribute"}, {"api_name": "WDL.Expr", "line_number": 106, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 86, "usage_type": "name"}, {"api_name": "WDL.Tree", "line_number": 130, "usage_type": "attribute"}, {"api_name": "WDL.Tree", "line_number": 140, "usage_type": "attribute"}, {"api_name": "WDL.Tree", "line_number": 146, "usage_type": "attribute"}, {"api_name": "WDL.Tree", "line_number": 152, "usage_type": "attribute"}, {"api_name": "WDL.Tree", "line_number": 158, "usage_type": "attribute"}, {"api_name": "WDL.Tree", "line_number": 165, "usage_type": "attribute"}, {"api_name": "WDL.Expr", "line_number": 170, "usage_type": "attribute"}, {"api_name": "WDL.Tree", "line_number": 184, "usage_type": "attribute"}, {"api_name": "WDL.Tree", "line_number": 194, "usage_type": "attribute"}, {"api_name": "WDL.Tree", "line_number": 196, "usage_type": "attribute"}, {"api_name": "WDL.Tree", "line_number": 200, "usage_type": "attribute"}]} +{"seq_id": "503600483", "text": "\"\"\"Provides the view of a help topic.\"\"\"\nfrom django.http import HttpResponse\nfrom apps.widgets.status import analysis\n\n\ndef supply(request, page_name):\n \"\"\" supply view_objects for widget rendering.\"\"\"\n\n _ = request\n _ = page_name\n\n return {}\n\n\ndef analysis_view(request, command):\n \"\"\"analysis\"\"\"\n _ = request\n\n if command == \"summary\":\n result = analysis.calculate_summary_stats()\n elif command == \"actions\":\n result = analysis.calculate_action_stats()\n elif command == \"users\":\n result = analysis.calculate_user_stats()\n elif command == \"timestamps\":\n team = request.GET.get(\"team\", \"\")\n date_start = request.GET.get(\"date_start\", \"\")\n date_end = request.GET.get(\"date_end\", \"\")\n result = analysis.user_timestamps(team, date_start, date_end)\n else:\n result = \"please specify an analysis command.\"\n\n return HttpResponse(result, content_type=\"text\", mimetype='text/plain')\n", "sub_path": "makahiki/apps/widgets/status/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 967, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "apps.widgets.status.analysis.calculate_summary_stats", "line_number": 20, "usage_type": "call"}, {"api_name": "apps.widgets.status.analysis", "line_number": 20, "usage_type": "name"}, {"api_name": "apps.widgets.status.analysis.calculate_action_stats", "line_number": 22, "usage_type": "call"}, {"api_name": "apps.widgets.status.analysis", "line_number": 22, "usage_type": "name"}, {"api_name": "apps.widgets.status.analysis.calculate_user_stats", "line_number": 24, "usage_type": "call"}, {"api_name": "apps.widgets.status.analysis", "line_number": 24, "usage_type": "name"}, {"api_name": "apps.widgets.status.analysis.user_timestamps", "line_number": 29, "usage_type": "call"}, {"api_name": "apps.widgets.status.analysis", "line_number": 29, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "43622866", "text": "#!/usr/bin/python\n\n# SHA1 Base32 to Base 16 Convertor\n# For converting SHA1 hash values from Gnutella file sharing network\n# Author: bluesmoke564\n# Gitub repo: http://github.com/bluesmoke564/SHA1-BaseConverter-GUI\n\nimport base64, datetime, tkFileDialog\n\nfrom Tkinter import *\nfrom ttk import *\n\n\nfiletext = 'Select Source File'\ndirtext = 'Select Output Folder'\n\n\ndef openFile():\n global filename\n filename = tkFileDialog.askopenfilename(parent=root, initialdir='/home/', title=filetext,\n filetypes=[('text files', '.txt'), ('all files', '.*')])\n filebut[\"text\"] = str(filename) if filename else filetext\n\n\ndef openDirectory():\n global dirname\n dirname = tkFileDialog.askdirectory(parent=root, initialdir='/home/', title=dirtext)\n dirbut[\"text\"] = str(dirname) if dirname else dirtext\n\n\ndef baseConv():\n try:\n with open(filename, 'r') as f:\n with open(dirname + '/BaseConverterResults.txt', 'w') as g:\n g.write(\"Case #: \" + casebox.get() + \"\\n\")\n g.write(\"Evidence #: \" + evidbox.get() + \"\\n\")\n g.write(\"Examiner: \" + exambox.get() + \"\\n\")\n g.write(\"Notes: \" + notesbox.get() + \"\\n\")\n g.write(\"Source File: \" + filename + \"\\n\")\n g.write(\"Output Directory: \" + dirname + \"\\n\")\n g.write(\"Date/Time: \" + datetime.datetime.now().strftime(\"%m-%d-%y, %H:%M:%S\") + \"\\n\")\n g.write(\"\\n\")\n g.write(\"Base32\\t--->\\tBase16(Hex)\\n\")\n convertbut[\"text\"] = \"Converting...\"\n for x in f:\n x = x.rstrip()\n if not x: continue\n g.write(x + \"\\t\" + \"--->\\t\")\n if len(str(x)) != 32:\n g.write(\"Invalid value; not 32 digits.\\n\")\n else:\n try:\n g.write(base64.b16encode(base64.b32decode(x)) + \"\\n\")\n except:\n g.write(\"Error; invalid value. \\n\")\n f.close()\n g.close()\n convertbut[\"text\"] = \"Conversion Finished\"\n\n except NameError:\n errorWindow()\n return\n\n\ndef onExit():\n root.quit()\n\n\ndef aboutWindow():\n top = Toplevel()\n top.title(\"About this application...\")\n msg = Message(top, width=400, text=aboutText())\n msg.pack()\n button = Button(top, text=\"Dismiss\", command=top.destroy)\n button.pack()\n\n\ndef aboutText():\n about = ( \"***SHA1 Base32 to Base16 Converter v0.2***\\n\\n\" +\n \"For converting Base32 SHA1 hash values to Base16\\n\\n\" +\n \"Author: bluesmoke564\\n\" +\n \"Email: bluesmoke564@gmail.com\\n\" +\n \"\\nDate: 10/5/2014\\n\" + \"\\n\")\n return about\n\n\ndef instructWindow():\n top = Toplevel()\n top.title(\"Instructions\")\n msg = Message(top, width=400, text=instructText())\n msg.pack()\n button = Button(top, text=\"Dismiss\", command=top.destroy)\n button.pack()\n\n\ndef instructText():\n instruct = (\"How to use SHA1 Base32 to Base16 Converter:\\n\\n\" +\n \"Source File:\\n SHA1 Base32 hash values should be in a text file with one value per line.\\n\\n\" +\n \"Output Directory:\\nSelect an output directory where the results file will be saved.\\n\\n\" +\n \"Output File:\\nThe output file will be saved in the user designated output directory as 'BaseConvertResults.txt'. \" +\n \"Although it is a .txt file, the hash values are saved as tab separated values. \" +\n \"This allows for the file to be opened in spreadsheet software such as Excel for easy manipulation and copying of results. \" +\n \"When viewing in a text editor such as Notepad, you may have to deselect 'word wrap' to prevent the output from appearing out of alignment. \" +\n \"\\n\")\n return instruct\n\n\ndef errorWindow():\n top = Toplevel()\n top.title(\"Error!\")\n msg = Message(top, width=400, text=\"\\nMake sure to select source file and output directory!\\n\")\n msg.pack()\n button = Button(top, text=\"Dismiss\", command=top.destroy)\n button.pack()\n\nroot = Tk()\nroot.title('SHA1 Base32 to Base16 Converter')\n\nmenubar = Menu(root)\nroot.config(menu=menubar)\nfileMenu = Menu(menubar, tearoff=0)\nfileMenu.add_command(label=\"Exit\", underline=0, command=onExit)\nmenubar.add_cascade(label=\"File\", menu=fileMenu, underline=0)\n\nhelpMenu = Menu(root, tearoff=0)\nhelpMenu.menu = Menu(menubar)\nhelpMenu.add_command(label=\"Instructions\", underline=0, command=instructWindow)\nhelpMenu.add_command(label=\"About\", underline=0, command=aboutWindow)\nmenubar.add_cascade(label=\"Help\", menu=helpMenu, underline=0)\n\nfilelabel = Label(root, text=\"Source File:\")\nfilelabel.grid(row=0, pady=5)\nfilebut = Button(root, width=50, text=filetext, command=openFile)\nfilebut.grid(row=0, column=1)\n\ndirlabel = Label(root, text=\"Destination Directory:\")\ndirlabel.grid(row=1, pady=5)\ndirbut = Button(root, width=50, text=dirtext, command=openDirectory)\ndirbut.grid(row=1, column=1)\n\ncaselabel = Label(root, text=\"Case Number:\")\ncaselabel.grid(row=2, pady=5)\ncasebox = Entry(root)\ncasebox.grid(row=2, column=1, sticky=W)\n\nevidlabel = Label(root, text=\"Evidence Item Number:\")\nevidlabel.grid(row=3, pady=5)\nevidbox = Entry(root)\nevidbox.grid(row=3, column=1, sticky=W)\n\nexamlabel = Label(root, text=\"Examiner:\")\nexamlabel.grid(row=4, pady=5)\nexambox = Entry(root)\nexambox.grid(row=4, column=1, sticky=W)\n\nnoteslabel = Label(root, text=\"Notes:\")\nnoteslabel.grid(row=5, pady=5)\nnotesbox = Entry(root, width=50)\nnotesbox.grid(row=5, column=1, sticky=W)\n\nconvertbut = Button(text='Convert', width=30, command=baseConv)\nconvertbut.grid(row=6, columnspan=2, pady=5)\n\nroot.mainloop()\n", "sub_path": "BaseConvert.py", "file_name": "BaseConvert.py", "file_ext": "py", "file_size_in_byte": 5774, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "tkFileDialog.askopenfilename", "line_number": 20, "usage_type": "call"}, {"api_name": "tkFileDialog.askdirectory", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 41, "usage_type": "attribute"}, {"api_name": "base64.b16encode", "line_number": 53, "usage_type": "call"}, {"api_name": "base64.b32decode", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "44794310", "text": "from .base import *\n\nimport os\nimport dj_database_url\nfrom datetime import date, timedelta\nDEBUG = False\n\nTEMPLATE_DEBUG = False\n\nALLOWED_HOSTS = ['*']\n\n# Honor the 'X-Forwarded-Proto' header for request.is_secure()\nSECURE_PROXY_SSL_HEADER = ('HTTP_X_FORWARDED_PROTO', 'https')\n\n#INSTALLED_APPS += ('storages','s3_folder_storage')\n\n# Configuracion de Storage en Amazon S3\nAWS_QUERYSTRING_AUTH = False\nAWS_ACCESS_KEY_ID = os.getenv('AWS_ACCESS_KEY_ID')\nAWS_SECRET_ACCESS_KEY = os.getenv('AWS_SECRET_ACCESS_KEY')\nAWS_STORAGE_BUCKET_NAME = os.getenv('AWS_STORAGE_BUCKET_NAME')\n\n# Expires 20 years in the future at 8PM GMT\ntenyrs = date.today() + timedelta(days=365*10)\nAWS_HEADERS = {\n 'Expires': tenyrs.strftime('%a, %d %b %Y 20:00:00 GMT')\n}\n\nSTATICFILES_STORAGE = 's3_folder_storage.s3.StaticStorage'\nSTATIC_URL = 'http://%s.s3.amazonaws.com/static/' % AWS_STORAGE_BUCKET_NAME\nSTATIC_S3_PATH = 'static/'\n\nADMIN_MEDIA_PREFIX = STATIC_URL + 'admin/'\n\nMEDIA_ROOT = ''\nDEFAULT_FILE_STORAGE = 's3_folder_storage.s3.DefaultStorage'\nDEFAULT_S3_PATH = 'media/'\nMEDIA_URL = 'http://%s.s3.amazonaws.com/media/' % AWS_STORAGE_BUCKET_NAME\n\n # Parse database configuration from $DATABASE_URL\nDATABASES = {\n 'default' : dj_database_url.config()\n}\n\n", "sub_path": "easycash/settings/production.py", "file_name": "production.py", "file_ext": "py", "file_size_in_byte": 1240, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "os.getenv", "line_number": 19, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 20, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 24, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 24, "usage_type": "call"}, {"api_name": "dj_database_url.config", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "300899934", "text": "import numpy as np\r\nimport sqlalchemy\r\nfrom sqlalchemy.ext.automap import automap_base\r\nfrom sqlalchemy.orm import Session\r\nfrom sqlalchemy import create_engine, func\r\nfrom flask import Flask, jsonify\r\nengine = create_engine(\"sqlite:///Resources/hawaii.sqlite\")\r\n\r\nBase = automap_base()\r\n\r\nBase.prepare(engine, reflect=True)\r\nBase.classes.keys()\r\n\r\nMeasurement = Base.classes.measurement\r\nStation = Base.classes.station\r\napp = Flask(__name__)\r\n\r\n@app.route(\"/\")\r\ndef Hawaii():\r\n \"\"\"List all available api routes.\"\"\"\r\n return (\r\n f\"Available Routes:
    \"\r\n f\"/api/v1.0/precipitation
    \"\r\n f\"/api/v1.0/stations
    \"\r\n f\"/api/v1.0/tobs
    \"\r\n f\"/api/v1.0/
    \"\r\n f\"/api/v1.0//\"\r\n )\r\n@app.route(\"/api/v1.0/precipitation\")\r\ndef precipitation():\r\n \r\n session = Session(engine)\r\n \"\"\"Return a list of all precipitaion with date\"\"\"\r\n \r\n prcp_data= session.query(Measurement.date, Measurement.prcp).all()\r\n print(prcp_data)\r\n session.close()\r\n \r\n all_data = []\r\n for date, prcp in prcp_data:\r\n prcp_dict = {}\r\n prcp_dict[\"date\"] = date\r\n prcp_dict[\"prcp\"] = prcp\r\n all_data.append(prcp_dict)\r\n return jsonify(all_data)\r\n\r\n@app.route(\"/api/v1.0/stations\")\r\ndef stations():\r\n \r\n session = Session(engine)\r\n \"\"\"Return a list of passenger data including the name, age, and sex of each passenger\"\"\"\r\n \r\n results = session.query(Station.name, Station.latitude, Station.longitude, Station.elevation).all()\r\n session.close()\r\n \r\n all_stations = []\r\n for name, latitude, longitude, elevation in results:\r\n station_dict = {}\r\n station_dict[\"name\"] = name\r\n station_dict[\"latitude\"] = latitude\r\n station_dict[\"longitude\"] = longitude\r\n station_dict[\"elevation\"] = elevation\r\n all_stations.append(station_dict)\r\n return jsonify(all_stations)\r\n@app.route(\"/api/v1.0/tobs\")\r\ndef tobs():\r\n \r\n session = Session(engine)\r\n \r\n tobs_results = session.query(Measurement.station, Measurement.tobs).filter(Measurement.date.between('2016-08-01', '2017-08-01')).all()\r\n tobs_list=[]\r\n for tobs in tobs_results:\r\n tobs_dict = {}\r\n tobs_dict[\"station\"] = tobs[0]\r\n tobs_dict[\"tobs\"] = float(tobs[1])\r\n tobs_list.append(tobs_dict)\r\n return jsonify(tobs_list)\r\n\r\n@app.route(\"/api/v1.0/\")\r\ndef start(start):\r\n results = session.query(*data).filter(Measurement.station==Station.station).filter(Measurement.date>=start_date).group_by(Station.name).order_by(func.sum(Measurement.prcp).desc()).all()\r\n results\r\n@app.route(\"/api/v1.0//\")\r\ndef startend(start,end):\r\n result = session.query(*data).filter(Measurement.station==Station.station).filter(Measurement.date>=start_date).filter(Measurement.date<=end_date).group_by(Station.name).order_by(func.sum(Measurement.prcp).desc()).all()\r\n result\r\nif __name__ == '__main__':\r\n app.run(debug=True)\r\n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 2952, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sqlalchemy.create_engine", "line_number": 7, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.automap.automap_base", "line_number": 9, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 16, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 32, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 45, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 64, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 68, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 77, "usage_type": "call"}, {"api_name": "sqlalchemy.func.sum", "line_number": 81, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 81, "usage_type": "name"}, {"api_name": "sqlalchemy.func.sum", "line_number": 85, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 85, "usage_type": "name"}]} +{"seq_id": "578041070", "text": "# Урок 5. Задание 2 (task_2):\n# Написать программу сложения и умножения двух шестнадцатеричных чисел.\n# При этом каждое число представляется как массив, элементы которого — цифры числа.\n# Например, пользователь ввёл A2 и C4F. Нужно сохранить их как [‘A’, ‘2’] и [‘C’, ‘4’, ‘F’] соответственно.\n# Сумма чисел из примера: [‘C’, ‘F’, ‘1’], произведение - [‘7’, ‘C’, ‘9’, ‘F’, ‘E’].\n\nfrom collections import deque, defaultdict\n\nhex_dict = defaultdict(list, {'0': 0, '1': 1, '2': 2, '3': 3, '4': 4, '5': 5, '6': 6, '7': 7, '8': 8, '9': 9,\n 'A': 10, 'B': 11, 'C': 12, 'D': 13, 'E': 14, 'F': 15})\ndec_dict = defaultdict(list, {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9',\n 10: 'A', 11: 'B', 12: 'C', 13: 'D', 14: 'E', 15: 'F'})\n\n\ndef hex_to_dec(num):\n res = 0\n for idx, num in enumerate(reversed(num)):\n res += hex_dict[num] * 16 ** idx\n return res\n\n\ndef dec_to_hex(num):\n res = deque()\n while num > 0:\n n = num\n num = num // 16\n n = n - num * 16\n res.appendleft(dec_dict[n])\n return list(res)\n\n\nn1 = list(input('Введите число 1: ').upper())\nn2 = list(input('Введите число 2: ').upper())\n\nres_sum = hex_to_dec(n1) + hex_to_dec(n2)\nres_mul = hex_to_dec(n1) * hex_to_dec(n2)\nprint(f'Сумма: {dec_to_hex(res_sum)}')\nprint(f'Произведение: {dec_to_hex(res_mul)}')\n", "sub_path": "lesson_05/task_2.py", "file_name": "task_2.py", "file_ext": "py", "file_size_in_byte": 1721, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "collections.defaultdict", "line_number": 9, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 11, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "131121269", "text": "import S3L as S3L, random as rnd, math as math\nimport matplotlib.pyplot as plt\nagentmodel = S3L.S3L_agent(2, 3.1, 4)\ndef realLearnStep():\n policy = agentmodel.selectPolicy()\n performance = evaluatePolicy(policy)\n agentmodel.updateQTable(policy, performance)\n print(\"Agent tried policy {} with result {}. \".format(policy, performance))\n return [policy, performance]\ndef evaluatePolicy(policy):\n realmax = [0.7, 0.7]\n dist = getL2NDist(realmax, policy)\n initial = (2 - dist) + 1\n otherdist = getL2NDist([0.2,0.2], policy)\n otherinitial = 2 - otherdist\n if dist < otherdist:\n return initial\n else:\n return otherinitial\ndef getL2NDist(a,b):\n sumd = 0\n for i in range(len(a)):\n sumd += (a[i] - b[i]) ** 2\n return math.sqrt(sumd)\ndef pureExplorationStep():\n policy = [ rnd.random() for _ in range(2)]\n performance = evaluatePolicy(policy)\n agentmodel.updateQTable(policy, performance)\n print(\"Agent explored policy {} with result {}. \".format(policy, performance))\ndef realLearning():\n for _ in range(2):\n pureExplorationStep()\n notdone = True\n cnt = 1\n while notdone:\n realLearnStep()\n try:\n [bestpolicy, bestperformance] = list(reversed(sorted(\n agentmodel.qtable,\n key = lambda x : x[1]\n )))[0]\n notdone = bestperformance < 2.9\n print(\"NOT DONE\" if notdone else \"DONE\")\n except:\n notdone = True\n print(\"{} steps have passed. \".format(cnt))\n cnt += 1\n top = list(reversed(sorted(agentmodel.qtable, key = lambda x : x[1])))[0]\n [policy, performance] = top\n print(\"==============DONE==============\")\n print(\"The agent decided on policy {}. \".format(policy))\n print(\"This policy had performance {}. \".format(performance))\n toplot = [ i[0] for i in agentmodel.qtable] + [[0,0],[0,1],[1,0],[1,1]]\n plt.scatter([ i[0] for i in toplot ],[ i[1] for i in toplot ])\n plt.show()\nrealLearning()", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1875, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "S3L.S3L_agent", "line_number": 3, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 24, "usage_type": "call"}, {"api_name": "random.random", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}]} +{"seq_id": "170329458", "text": "#e!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n#from PyPDF2 import PdfFileWriter, PdfFileReader\nfrom cStringIO import StringIO\nfrom pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter\nfrom pdfminer.converter import TextConverter\nfrom pdfminer.layout import LAParams\nfrom pdfminer.pdfpage import PDFPage\n\ndef convert(fname, pages=None):\n if not pages:\n pagenums = set()\n else:\n pagenums = set(pages)\n\n output = StringIO()\n manager = PDFResourceManager()\n converter = TextConverter(manager, output, laparams=LAParams())\n interpreter = PDFPageInterpreter(manager, converter)\n infile = file(fname, 'rb')\n for page in PDFPage.get_pages(infile, pagenums):\n interpreter.process_page(page)\n infile.close()\n converter.close()\n text = output.getvalue()\n output.close\n return text \n\n\n\nif __name__ == '__main__':\n f = open('test.txt','w')\n# f.write(get_pdf_text('overview-clinical.pdf').encode(\"utf-8\"))\n f.write(convert(\"overview-clinical.pdf\"))\n\n f.close()\n\n\n\n", "sub_path": "pdf_reader.py", "file_name": "pdf_reader.py", "file_ext": "py", "file_size_in_byte": 1036, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "cStringIO.StringIO", "line_number": 17, "usage_type": "call"}, {"api_name": "pdfminer.pdfinterp.PDFResourceManager", "line_number": 18, "usage_type": "call"}, {"api_name": "pdfminer.converter.TextConverter", "line_number": 19, "usage_type": "call"}, {"api_name": "pdfminer.layout.LAParams", "line_number": 19, "usage_type": "call"}, {"api_name": "pdfminer.pdfinterp.PDFPageInterpreter", "line_number": 20, "usage_type": "call"}, {"api_name": "pdfminer.pdfpage.PDFPage.get_pages", "line_number": 22, "usage_type": "call"}, {"api_name": "pdfminer.pdfpage.PDFPage", "line_number": 22, "usage_type": "name"}]} +{"seq_id": "422994985", "text": "#! encoding = utf-8\n'''\n频谱仪使用，网口控制 192.168.1.11\n'''\nimport time\nimport logging\nimport pandas as pd\nimport numpy as np\n\nfrom commoninterface.fsvbase import FSVBase\n\nlogger = logging.getLogger('ghost')\n\n\nclass FSVCtrl(FSVBase):\n\n def __init__(self):\n FSVBase.__init__(self)\n\n def set_offset(self, offset):\n '''\n 设置外部衰减器衰减值\n :param offset: 单位dB\n :return:\n '''\n self.offset = float(offset)\n logger.debug('offset={}'.format(offset))\n\n def reset_fsv(self):\n if self.handle:\n self.handle.write_termination = '\\n'\n self.handle.timeout = 10000\n self.handle.write('*RST')\n self.handle.write('*CLS')\n\n self.handle.write('INIT:CONT OFF')\n self.handle.write('SYST:DISP:UPD ON')\n self.ext_error_checking()\n self.handle.write('INST SAN') # mode选择Spectrum\n logger.debug('reset_fsv...')\n\n def set_freq(self, freq):\n '''\n 为测输出功率进行设置\n :param freq:\n :return:\n '''\n if self.handle:\n self.handle.write('FREQ:CENT {}MHz'.format(freq)) # center freq\n\n def set_for_txatt(self, ref_level, freq):\n logger.debug('set_for_txatt')\n logger.debug(self.handle)\n if self.handle:\n self.handle.write('*RST;*CLS')\n self.handle.write('INST SAN') # mode选择Spectrum\n self.handle.write('FREQ:CENT {}MHz'.format(freq)) # center freq\n self.handle.write('DISP:TRAC:Y:RLEV:OFFSET {}dB'.format(self.offset)) # REF OFFSET 加了衰减器的40dB\n self.handle.write('DISP:TRAC:Y:RLEV {}dBm'.format(ref_level)) # reference level\n # self.handle.write('DISP:TRAC:Y:RLEV:OFFS -10dB') # reference level offset\n self.handle.write('CALC:MARKER:FUNC:POW:PRES EUTRA') # EUTRA/LTE Square\n\n self.handle.write('POW:ACH:BWID:CHAN1 4.5MHz') # TX bandwidth\n self.handle.write('POW:ACH:BWID:ACH 4.5MHz') # adj channel bandwidth\n self.handle.write('POW:ACH:SPAC:CHAN 4.5MHz') # TX Spacing\n self.handle.write('POW:ACH:SPAC 5MHz') # 载波和ADJ之间的spacing\n\n # for test\n self.handle.write('SWE:TIME 1s') # sweep time\n self.handle.write('POW:ACH:MODE REL') # 取dB相对值\n self.handle.write('CALC:MARK:FUNC:POW:SEL ACP')\n self.handle.write('DET RMS')\n\n self.handle.write('BAND 100 KHz') # resolution bandwidth\n self.handle.write('BAND:VIDeo 30 kHz') # video bandwidth\n\n def set_for_evm(self, mode, freq):\n '''\n 为测EVM进行设置\n :param freq:\n :return:\n '''\n if self.handle:\n mode = str(mode).upper()\n self.handle.write('*RST;*CLS')\n self.handle.write('INIT:CONT OFF')\n self.handle.write('SYST:DISP:UPD ON')\n # self.handle.ext_error_checking()\n\n self.handle.write('INST LTE') # mode选择LTE\n # self.handle.write('SWE:COUN 3')\n self.handle.write('TRIG:MODE EXT') # external trigger\n # for test\n # self.handle.write('TRIG:MODE IMM') # free run\n self.handle.write('DISP:TRAC:Y:RLEV:OFFS {}dB'.format(self.offset)) # Ext Att 40dB,与外部衰减对应\n self.handle.write('FREQ:CENT {}MHz'.format(freq)) # CENT FREQ\n self.handle.write('CONF:DUPL {}'.format(mode)) # TDD/FDD\n self.handle.write('CONF:LDIR DL') # DL/UL\n self.handle.write('CONF:DL:BW BW5_00') # dl-bandwidth 5MHz\n\n def get_single_CCDF(self):\n '''\n 设置同读EVM时设置一样\n :return:\n '''\n\n try:\n if self.handle:\n self.handle.write('INIT:CONT OFF')\n self.handle.write('DISP:TABL ON') # Display list\n self.handle.write('INIT;*WAI')\n # self.handle.write('INIT:CONM;*WAI')\n sampling_error = self.handle.query('FETCH:SUMMARY:SERROR?', delay=1)\n # power = self.handle.query('FETCH:SUMMARY:POW?')\n self.handle.query('*OPC?')\n se = sampling_error.split('\\n')[0]\n\n crest_factor = self.handle.query('FETCH:SUMMARY:CREST?', delay=1) # 默认取mean\n self.handle.query('*OPC?')\n cf = crest_factor.split('\\n')[0]\n return '%.2f' % float(se), '%.2f' % float(cf)\n return None\n except Exception as e:\n logger.error(e)\n return None\n\n def get_several_ccdf(self, mode, freq):\n r = 0\n while True:\n n = yield r\n if not n:\n return\n resstr = self.get_single_CCDF()\n if resstr is not None:\n r = resstr\n else:\n r = None\n self.close_inst()\n self.init_again()\n time.sleep(1)\n self.reset_fsv()\n self.set_for_evm(mode, freq)\n time.sleep(1)\n time.sleep(.5)\n\n def get_CCDF(self, mode, freq):\n self.set_for_evm(mode, freq)\n consumer = self.get_several_ccdf(mode, freq)\n ccdf = self.sweep_ccdf(consumer)\n return ccdf\n\n def sweep_ccdf(self, c):\n c.__next__()\n n = 0\n ccdf = None\n while n < 6:\n n = n + 1\n r = c.send(n)\n if r is not None:\n ccdf = r\n break\n c.close()\n return ccdf\n\n def get_EVM(self, mode, freq):\n '''\n 读取EVM\n :return:\n '''\n self.set_for_evm(mode, freq)\n consumer = self.gen_maxEVM(mode, freq)\n maxevmlist = self.sweep_several(consumer)\n return max(maxevmlist)\n\n def sweep_several(self, c):\n '''\n single sweep 多次，结果取平均值\n :return:[float,float]\n '''\n c.__next__()\n n = 0\n evmlist = []\n while n < 6:\n n = n + 1\n r = c.send(n)\n evmlist.append(r)\n c.close()\n evmlist = [float(evm) for evm in evmlist]\n logger.debug('evmaxlist={}'.format(evmlist))\n return evmlist\n\n def gen_asum(self):\n '''\n 产生asum数据字符串\n :return:\n '''\n try:\n self.handle.write('INIT:CONT OFF')\n self.handle.write('UNIT:EVM PCT')\n self.handle.write('DISP:TABL OFF')\n # self.handle.write(\"CALC1:FEED 'STAT:ASUM'\")\n self.handle.write(\"CALC2:FEED 'STAT:ASUM'\")\n self.handle.write('INIT;*WAI')\n # self.handle.write('INIT:CONM;*WAI')\n res = self.handle.query('TRAC:DATA? TRACE1', delay=1) # 以,分割的字符串,成员均为字符串\n self.handle.query('*OPC?')\n return res\n except Exception as e:\n logger.error(e)\n return None\n\n def gen_maxEVM(self, mode, freq):\n r = 0\n while True:\n n = yield r\n if not n:\n return\n resstr = self.gen_asum()\n if resstr:\n maxret = self.fetch_max(resstr)\n if maxret:\n r = maxret\n else:\n r = 0\n else:\n r = 0\n self.close_inst()\n self.init_again()\n time.sleep(1)\n self.reset_fsv()\n self.set_for_evm(mode, freq)\n time.sleep(1)\n time.sleep(.5)\n\n def fetch_max(self, resstr):\n '''\n\n :param resstr:\n :return:\n '''\n if not resstr:\n return None\n reslist = resstr.strip('\\n\\r\\t').split(',')\n # logger.debug('reslist={}'.format(reslist))\n reslen = len(reslist)\n narray = np.array(reslist)\n frame = pd.DataFrame(narray.reshape((reslen // 7, 7)), columns=['subframe', 'alloc_ID', 'num_RB',\n 'rel_power', 'modulation', 'abs_power', 'EVM']\n )\n # frame['EVM']=frame['EVM'].map(lambda x:'%.4f'%x)\n frame['EVM'] = frame['EVM'].apply(pd.to_numeric) # str->float\n allid_frame = frame[frame['alloc_ID'] == '-5'] # RS Ant1\n if not allid_frame.empty:\n sort_frame = allid_frame.sort_values(by='EVM', ascending=False)\n maxEVM = sort_frame.iloc[0]['EVM']\n return '%.2f' % float(maxEVM)\n return None\n\n def save_screenshot(self, dest):\n '''\n 截图\n :return:\n '''\n # 截图\n self.handle.write('HCOP:DEV:LANG PNG')\n self.handle.write(\"MMEM:NAME 'c:\\\\Temp\\\\PC_Screenshot.PNG'\")\n self.handle.write(\"HCOP:DEST 'MMEM'\")\n self.handle.write('HCOP:ITEM:ALL')\n self.handle.write('HCOP')\n self.handle.query('*OPC?')\n self.ext_error_checking()\n\n self.ext_query_bin_data_to_file(\"MMEM:DATA? 'c:\\\\Temp\\\\PC_Screenshot.PNG'\", r'{}'.format(dest))\n self.handle.query('*OPC?')\n\n def ext_query_bin_data_to_file(self, query, pc_file_path):\n if self.handle:\n file_data = self.handle.query_binary_values(query, datatype='s')[0]\n new_file = open(pc_file_path, \"wb\")\n new_file.write(file_data)\n new_file.close()\n\n def get_power(self, ref_level, freq):\n # self.set_for_txatt(freq)\n consumer = self.gen_several_power(ref_level, freq)\n power = self.sweep_ccdf(consumer)\n return power\n\n def gen_several_power(self, ref_level, freq):\n r = 0\n while True:\n n = yield r\n if not n:\n return\n resstr = self.get_single_power()\n if resstr:\n r = resstr\n else:\n logger.error('get None')\n r = None\n self.close_inst()\n self.init_again()\n time.sleep(3)\n self.reset_fsv()\n self.set_for_txatt(ref_level, freq)\n time.sleep(1)\n time.sleep(1)\n\n def get_single_power(self):\n '''\n continue single sweep\n 获取输出功率\n :return:[str tx power,str adj lower,str adj upper]\n '''\n try:\n if self.handle:\n self.handle.write('*CLS')\n\n self.handle.write('INIT:CONT OFF')\n self.handle.write('CALC:MARK:FUNC:POW:SEL ACP')\n self.handle.write('DET RMS')\n #取5次的平均值\n self.handle.write('SWE:COUN 5')\n self.handle.write('DISP:TRAC1:MODE AVER')\n self.handle.write('INIT;*WAI')\n # self.handle.write('INIT:CONM;*WAI')\n # ACP 返回，分割的字符串 TX Power,Adj lower,Adj Upper,Alt1 lower,Alt1 Upper\n # time.sleep(1)\n cpower = self.handle.query('CALC:MARK:FUNC:POW:RES? ACP', delay=5)\n time.sleep(5)\n self.handle.query('*OPC?')\n cpower1 = [item.strip() for item in cpower.split(',')]\n\n ret = ['%.2f' % float(item) for item in cpower1]\n\n return ret[:3]\n except Exception as e:\n logger.error('get_power error:{}'.format(e))\n return None\n else:\n return None\n\n\nif __name__ == '__main__':\n fsv = FSVCtrl()\n fsv.init_inst('192.168.1.11')\n fsv.reset_fsv()\n fsv.set_offset(42.6)\n print('***')\n time.sleep(1)\n fsv.set_for_txatt(42.5, 2365)\n print(fsv.get_power(42.5, 2365))\n # fsv.get_EVM('TDD',2350)\n fsv.close_inst()\n", "sub_path": "WEB21-1-12/WEB2/tempcomp/api/fsv.py", "file_name": "fsv.py", "file_ext": "py", "file_size_in_byte": 11839, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "commoninterface.fsvbase.FSVBase", "line_number": 15, "usage_type": "name"}, {"api_name": "commoninterface.fsvbase.FSVBase.__init__", "line_number": 18, "usage_type": "call"}, {"api_name": "commoninterface.fsvbase.FSVBase", "line_number": 18, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 140, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 143, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 144, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 229, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 232, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 233, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 246, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 247, "usage_type": "call"}, {"api_name": "pandas.to_numeric", "line_number": 251, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 303, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 306, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 307, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 330, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 350, "usage_type": "call"}]} +{"seq_id": "540855866", "text": "from projects.qm_brain.utils.utils import *\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom scipy.interpolate import griddata\nfrom scipy import sparse\nmain_path = '/home/user/Desktop/QMBrain/New Data/'\n\nfilepathX = main_path + 'x_chanloc.csv'\nfilepathY = main_path + 'y_chanloc.csv'\n\nx = load_matrix(filepathX)\ny = load_matrix(filepathY)\n\nx_inds = np.argsort(x)\ny_inds = np.argsort(y)\n\n#grid_x, grid_y = np.mgrid[min(x):max(x):300j,min(y):max(y):200j]\n\ncondition_list = ['Cond10/','Cond12/']\n\ndim = np.stack((x_inds,y_inds))\n\nfor condition in condition_list:\n\n for i in range(3):\n\n subject_path = main_path + condition + str(i + 1) + '/'\n\n if file_exists(subject_path + 'DeltaX.csv'):\n\n print('Running for subject ', i+1, 'in folder ', condition)\n\n filepathData = subject_path + 'data.csv'\n\n data = load_matrix(filepathData)\n new_dat = np.zeros(shape=(data.shape[1],data.shape[1],data.shape[0]))\n #new_dat = griddata(np.stack((x,y)),data,(x,y))\n for t in range(data.shape[0]):\n new_dat[...,t] = sparse.coo_matrix((data[t,:],dim),(data.shape[1],data.shape[1])).A\n\n print(new_dat.shape)\n\n", "sub_path": "projects/qm_brain/ft_test_2.py", "file_name": "ft_test_2.py", "file_ext": "py", "file_size_in_byte": 1223, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "numpy.argsort", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 37, "usage_type": "call"}, {"api_name": "scipy.sparse.coo_matrix", "line_number": 40, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 40, "usage_type": "name"}]} +{"seq_id": "382492906", "text": "from flask import Flask, request, session\n\nimport logging\nimport os\nimport socket\nimport time\nimport uuid\n\napp = Flask(__name__)\n\napp.config['REMOTE_HOST'] = ('dylanscott.com.au', 22)\napp.config['DEBUG'] = True\n\nconnections = dict()\n\ndef delete_old_connections(connections):\n \"\"\" Delete connections that have not been used in the last minute. \"\"\"\n # Get the current timestamp.\n timestamp = time.time()\n # Loop over the current connection table.\n for connection_uuid, connection in list(connections.items()):\n # If the connection hasn't been used in the last minute\n if timestamp - connection.timestamp > 60:\n # Delete the connection from the connection table\n del connections[connection_uuid]\n # Make a note in the log.\n app.logger.debug('Deleted {}'.format(connection_uuid))\n\nclass Connection:\n def __init__(self):\n # Generate a UUID.\n self.uuid = str(uuid.uuid4())\n # Add a timestamp for the connection.\n self.timestamp = time.time()\n # Create a new socket.\n self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n # Connect to the remote host.\n self.socket.connect(app.config['REMOTE_HOST'])\n def send(self, data):\n # Update the timestamp\n self.timestamp = time.time()\n # Send the data to the socket.\n self.socket.sendall(data)\n def recv(self):\n # Update the timestamp\n self.timestamp = time.time()\n # Return the data received from the socket.\n return self.socket.recv(2048)\n\n@app.route('/', methods=['POST', 'PUT', 'GET'])\ndef forward():\n\n # POST is used to open a new connection.\n if request.method == 'POST':\n # Clean up any old connections.\n delete_old_connections(connections)\n # Create a new connection.\n connection = Connection()\n # Get the connection UUID.\n connection_uuid = connection.uuid\n # Add the connection to the connection table by UUID.\n connections[connection_uuid] = connection\n # Add the connection UUID to the session.\n session['connection_uuid'] = connection_uuid\n # Make a note in the log.\n app.logger.debug('Added {}'.format(connection_uuid))\n app.logger.debug('Size of table {}'.format(len(connections)))\n # Return the UUID of the connection.\n return connection_uuid\n\n # PUT is used to send data to the socket.\n elif request.method == 'PUT':\n # Get the UUID from the session.\n connection_uuid = session['connection_uuid']\n # Get the connection from the connection table.\n connection = connections[connection_uuid]\n # Send the data to the socket.\n connection.send(request.data)\n # Return the UUID of the connection.\n return connection_uuid\n\n # GET is used to recieve data from the socket.\n elif request.method == 'GET':\n # Get the UUID from the session.\n connection_uuid = session['connection_uuid']\n # Get the connection from the connection table.\n connection = connections[connection_uuid]\n # Get the data from the socket.\n return connection.recv()\n\nif __name__ == '__main__':\n\n # Randomise the secret key since it doesn't need to be kept.\n app.secret_key = os.urandom(32)\n # Run the server in threaded mode.\n app.run(threaded=True)\n", "sub_path": "server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 3399, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "flask.Flask", "line_number": 9, "usage_type": "call"}, {"api_name": "time.time", "line_number": 19, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 32, "usage_type": "call"}, {"api_name": "time.time", "line_number": 34, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 36, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 36, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 36, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 41, "usage_type": "call"}, {"api_name": "time.time", "line_number": 46, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 54, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 64, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 72, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 72, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 74, "usage_type": "name"}, {"api_name": "flask.request.data", "line_number": 78, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 78, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 83, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 83, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 85, "usage_type": "name"}, {"api_name": "os.urandom", "line_number": 94, "usage_type": "call"}]} +{"seq_id": "76624607", "text": "'''\n================================================\n DOWNLOAD_AUDIOSET REPOSITORY\n================================================\n\nrepository name: download_audioset\nrepository version: 1.0\nrepository link: https://github.com/jim-schwoebel/download_audioset\nauthor: Jim Schwoebel\nauthor contact: js@neurolex.co\ndescription: downloads the raw audio files from AudioSet (released by Google).\nlicense category: opensource\nlicense: Apache 2.0 license\norganization name: NeuroLex Laboratories, Inc.\nlocation: Seattle, WA\nwebsite: https://neurolex.ai\nrelease date: 2018-11-08\n\nThis code (download_audioset) is hereby released under a Apache 2.0 license license.\n\nFor more information, check out the license terms below.\n\n================================================\n SPECIAL NOTES\n================================================\n\nThis script parses through the entire balanced audioset dataset and downloads\nall the raw audio files. The files are arranged in folders according to their\nrepresentative classes.\n\nPlease ensure that you have roughly 35GB of free space on your computer before\ndownloading the files. Note that it may take up to 2 days to fully download\nall the files.\n\nEnjoy! - :)\n\n-Jim\n\n================================================\n LICENSE TERMS\n================================================\n\nCopyright 2018 NeuroLex Laboratories, Inc.\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\n================================================\n SERVICE STATEMENT\n================================================\n\nIf you are using the code written for a larger project, we are\nhappy to consult with you and help you with deployment. Our team\nhas >10 world experts in Kafka distributed architectures, microservices\nbuilt on top of Node.js / Python / Docker, and applying machine learning to\nmodel speech and text data.\n\nWe have helped a wide variety of enterprises - small businesses,\nresearchers, enterprises, and/or independent developers.\n\nIf you would like to work with us let us know @ develop@neurolex.co.\n'''\n\n################################################################################\n## IMPORT STATEMENTS ##\n################################################################################\n\nimport os, time, ffmpy\nfrom natsort import natsorted\nimport pandas as pd\nimport soundfile as sf\nfrom tqdm import tqdm\n\n\n################################################################################\n## HELPER FUNCTIONS ##\n################################################################################\n\n# function to clean labels\ndef convertlabels(sortlist, labels, textlabels):\n clabels = list()\n try:\n index = labels.index(sortlist)\n clabel = textlabels[index]\n # pull out converted label\n clabels.append(clabel)\n except:\n clabels = []\n\n return clabels\n\n\ndef download_audio(link):\n listdir = os.listdir()\n # TODO os.system(\"youtube-dl -f 'bestaudio[ext=m4a]' ytsearch:'%s'\" % (link))\n os.system(\"youtube-dl --format m4a %s\" % (link))\n listdir2 = os.listdir()\n filename = ''\n for i in range(len(listdir2)):\n if listdir2[i] not in listdir and listdir2[i].endswith('.m4a'):\n filename = listdir2[i]\n break\n\n return filename\n\n\n################################################################################\n## MAIN SCRIPT ##\n################################################################################\n# TODO\nd_sound = ['/m/01b_21', '/m/05tny_', '/m/0ghcn6', '/m/0cdnk', '/m/078jl', '/t/dd00036', '/t/dd00037',\n '/m/02_41', '/m/0k4j', '/m/01bjv', '/m/04qvtq', '/m/012n7d', '/m/012ndj', '/m/0284vy3',\n '/m/02x984l', '/m/03l9g', '/m/01d380', '/m/0g6b5', '/m/07qnq_y', '/m/07pws3f', '/m/07pjjrj',\n '/m/07pc8lb', '/m/04_sv', '/m/07qb_dv', '/m/07qv4k0', '/m/07plct2']\n\ns_sound = ['/t/dd00129', '/m/015p6', '/m/0ytgt', '/m/01h8n0', '/m/01j3sz', '/t/dd00001', '/m/03m9d0z',\n '/m/07pggtn', '/m/07pbtc8', '/m/0838f', '/m/0j6m2', '/m/04k94']\n\ndefaultdir = os.getcwd()\nos.chdir(defaultdir)\n\n# load labels of the videos\n\n# number, label, words\nloadfile = pd.read_excel('labels.xlsx')\n\nnumber = loadfile.iloc[:, 0].tolist()\nlabels = loadfile.iloc[:, 1].tolist()\ntextlabels = loadfile.iloc[:, 2].tolist()\n\n# remove spaces for folders\nfor i in range(len(textlabels)):\n textlabels[i] = textlabels[i].replace(' ', '')\n\n# now load data for youtube\nloadfile2 = pd.read_excel('unbalanced_train_segments.xlsx')\n\n# ylabels have to be cleaned to make a good list (CSV --> LIST)\nyid = loadfile2.iloc[:, 0].tolist()[2:]\nystart = loadfile2.iloc[:, 1].tolist()[2:]\nyend = loadfile2.iloc[:, 2].tolist()[2:]\nylabels = loadfile2.iloc[:, 3].tolist()[2:]\n\nprint(set(ylabels))\n\n# make folders\ntry:\n defaultdir2 = os.getcwd() + '\\\\audiosetdata\\\\'\n os.chdir(os.getcwd() + '\\\\audiosetdata')\nexcept:\n defaultdir2 = os.getcwd() + '\\\\audiosetdata\\\\'\n os.mkdir(os.getcwd() + '\\\\audiosetdata')\n os.chdir(os.getcwd() + '\\\\audiosetdata')\n\nexisting_wavfiles = list()\nfor i in range(len(textlabels)):\n if d_sound.count(labels[i]) == 0: # TODO\n continue\n\n try:\n os.mkdir(textlabels[i])\n except:\n os.chdir(textlabels[i])\n listdir = os.listdir()\n for j in range(len(listdir)):\n if listdir[j].endswith('.wav'):\n existing_wavfiles.append(listdir[j])\n os.chdir(defaultdir2)\n\n# get last file checkpoint to leave off\nexisting_wavfiles = natsorted(existing_wavfiles)\nprint(existing_wavfiles)\ntry:\n lastfile = int(existing_wavfiles[-1][7:][0:-4])\nexcept:\n lastfile = 0\n\n# iterate through entire CSV file, look for '--' if found, find index, delete section, then go to next index\n\nslink = \"https://www.youtube.com/watch?v=\"\n\nfor i in tqdm(range(len(yid))):\n if i < lastfile:\n print('skipping, already downloaded file...')\n else:\n link = slink + yid[i]\n start = float(ystart[i])\n end = float(yend[i])\n print(ylabels[i])\n clabels = convertlabels(ylabels[i], labels, textlabels)\n print(clabels)\n\n if clabels != [] and d_sound.count(ylabels[i]) > 0: # TODO\n\n # change to the right directory\n newdir = defaultdir2 + clabels[0] + '\\\\'\n print('newdir', newdir)\n os.chdir(newdir)\n # if it is the first download, pursue this path to download video\n lastdir = os.getcwd() + '\\\\'\n print('lastdir', lastdir)\n\n if 'snipped' + str(i) + '.wav' not in os.listdir():\n print('str(i)', str(i))\n try:\n # use YouTube DL to download audio\n filename = download_audio(link)\n print('link', link)\n extension = '.m4a'\n print('extension', extension)\n # get file extension and convert to .wav for processing later\n # TODO error 부분\n os.rename(filename, '%s%s' % (str(i), extension))\n print('str(i)',str(i))\n filename = '%s%s' % (str(i), extension)\n print('filename', filename)\n if extension not in ['.wav']:\n xindex = filename.find(extension)\n filename = filename[0:xindex]\n ff = ffmpy.FFmpeg(\n inputs={filename + extension: None},\n outputs={filename + '.wav': None})\n ff.run()\n # TODO\n # song = AudioSegment.from_file(filename, extension)\n # wav_filename = filename.replace(extension, 'wav')\n # song.export(filename + extension, format=\"wav\")\n os.remove(filename + extension)\n file = filename + '.wav'\n print('file', file)\n data, samplerate = sf.read(file)\n totalframes = len(data)\n print('totalframes',totalframes)\n totalseconds = totalframes / samplerate\n print('totalseconds',totalseconds)\n startsec = start\n startframe = samplerate * startsec\n endsec = end\n endframe = samplerate * endsec\n print('startframe',startframe)\n print('endframe',endframe)\n sf.write('snipped' + file, data[int(startframe):int(endframe)], samplerate)\n snippedfile = 'snipped' + file\n os.remove(file)\n except:\n print('no urls')\n # sleep 3 second sleep to prevent IP from getting banned\n time.sleep(3)\n else:\n print('skipping, already downloaded file...')", "sub_path": "sound/download.py", "file_name": "download.py", "file_ext": "py", "file_size_in_byte": 9584, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.listdir", "line_number": 102, "usage_type": "call"}, {"api_name": "os.system", "line_number": 104, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 105, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 127, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 128, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 133, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 144, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 156, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 157, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 157, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 159, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 160, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 160, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 161, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 161, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 169, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 171, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 172, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 176, "usage_type": "call"}, {"api_name": "natsort.natsorted", "line_number": 179, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 190, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 206, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 208, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 211, "usage_type": "call"}, {"api_name": "os.rename", "line_number": 221, "usage_type": "call"}, {"api_name": "ffmpy.FFmpeg", "line_number": 228, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 236, "usage_type": "call"}, {"api_name": "soundfile.read", "line_number": 239, "usage_type": "call"}, {"api_name": "soundfile.write", "line_number": 250, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 252, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 256, "usage_type": "call"}]} +{"seq_id": "40789012", "text": "from functools import wraps\nimport urlparse\n\nfrom flask import redirect\nfrom flask import render_template\nfrom flask import request\nfrom flask import session\nfrom flask import url_for\n\nfrom feedback.league import get_league\n\n\ndef login_required(func):\n @wraps(func)\n def wrapper(*args, **kwargs):\n if 'current_user' not in session:\n # Store the url that the user was attempting to reach and redirect\n url = request.url\n parsed_url = urlparse.urlsplit(url)\n next_url = '%s?%s' % (parsed_url.path, parsed_url.query)\n session['next_url'] = next_url.encode('base64', 'strict')\n return redirect(url_for('hello', next_url=next_url))\n return func(*args, **kwargs)\n return wrapper\n\n\ndef league_required(func):\n @wraps(func)\n def wrapper(*args, **kwargs):\n if 'league_id' not in kwargs:\n return redirect(url_for('hello'))\n league = get_league(kwargs.get('league_id'))\n if not league:\n return redirect(url_for('hello'))\n kwargs['league'] = league\n return func(*args, **kwargs)\n return wrapper\n\n\ndef templated(template=None):\n def decorator(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n template_name = template\n if template_name is None:\n template_name = request.endpoint \\\n .replace('.', '/') + '.html'\n ctx = f(*args, **kwargs)\n if ctx is None:\n ctx = {}\n elif not isinstance(ctx, dict):\n return ctx\n return render_template(template_name, **ctx)\n return decorated_function\n return decorator\n", "sub_path": "feedback/routes/decorators.py", "file_name": "decorators.py", "file_ext": "py", "file_size_in_byte": 1707, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "flask.session", "line_number": 16, "usage_type": "name"}, {"api_name": "flask.request.url", "line_number": 18, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 18, "usage_type": "name"}, {"api_name": "urlparse.urlsplit", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 21, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 22, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 31, "usage_type": "call"}, {"api_name": "feedback.league.get_league", "line_number": 32, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 34, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.request.endpoint.replace", "line_number": 46, "usage_type": "call"}, {"api_name": "flask.request.endpoint", "line_number": 46, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 46, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 53, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "602121536", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nimport collections\n\nfrom itertools import product\n\ndef fixed_aspect_ratio(ratio):\n '''\n Set a fixed aspect ratio on matplotlib plots \n regardless of axis units\n '''\n xvals, yvals = (plt.gca().axes.get_xlim(), \n plt.gca().axes.get_ylim())\n \n xrange = xvals[1]-xvals[0]\n yrange = yvals[1]-yvals[0]\n plt.gca().set_aspect(ratio*(xrange/yrange), adjustable='box')\n\ndef better_savefig(name, dpi=72, pad=0.0, remove_border=True):\n '''\n This function is for saving images without a bounding box and at the proper resolution\n The tiff files produced are huge because compression is not supported py matplotlib\n \n \n name : str\n The string containing the name of the desired save file and its resolution\n \n dpi : int\n The desired dots per linear inch\n \n pad : float\n Add a tiny amount of whitespace if necessary\n \n remove_border : bool\n Whether to remove axes and padding (for example, for images)\n \n '''\n if remove_border:\n plt.gca().set_axis_off()\n plt.subplots_adjust(top = 1+pad, bottom = 0+pad, right = 1+pad, left = 0+pad, \n hspace = 0, wspace = 0)\n plt.margins(0,0)\n plt.gca().xaxis.set_major_locator(plt.NullLocator())\n plt.gca().yaxis.set_major_locator(plt.NullLocator())\n\n plt.savefig(name, bbox_inches='tight', pad_inches=0, dpi=dpi)\n\ndef cmap1D(all_col, N):\n '''Generate a continuous colormap between two values\n \n Parameters\n ----------\n \n all_col : list of 3-tuples\n The colors to linearly interpolate\n \n N : int\n The number of values to interpolate\n \n Returns\n -------\n \n col_list : list of tuples\n An ordered list of colors for the colormap\n \n '''\n \n n_col = len(all_col)\n all_col = [np.array([item/255. for item in col]) for col in all_col]\n\n all_vr = list()\n runlens= [len(thing) for thing in np.array_split(range(N), n_col-1)]\n for col1, col2, runlen in zip(all_col[:-1], all_col[1:], runlens):\n vr = list()\n for ii in range(3):\n vr.append(np.linspace(col1[ii], col2[ii], runlen))\n vr = np.array(vr).T\n all_vr.extend(vr)\n return [tuple(thing) for thing in all_vr]\n\ndef tup2str(tup, delim=''):\n '''Convert a tuple to an ordered string'''\n return delim.join([str(item) for item in tup])\n\n\ndef get_slope(vec):\n m, b = np.polyfit(np.arange(0,len(vec)), vec, 1) \n return (m, b)\n\n\ndef bin2int(arr, axis=0): \n \"\"\"\n Convert a binary array to an integer along the \n specified axis\n\n Dev: this overflows when the size of the numbers is greater\n than 64 bits\n \"\"\"\n pow2 = 2**np.arange(arr.shape[axis], dtype=np.uint64)\n return np.sum(arr*pow2, axis=axis).astype(int)\n\n\n\n\ndef all_combinations(m,d=9):\n '''\n Make an array of all d dimensional inputs\n consisting of m possible values\n '''\n \n sq = int(np.sqrt(d))\n \n indices = np.tile(np.array([np.arange(m)]).T,d)\n\n all_combos = list(product(*list(indices.T)))\n out = np.reshape(np.array(all_combos),(-1, sq, sq))\n \n return out\n \n\ndef relu(arr0):\n arr = np.copy(arr0)\n arr[arr<=0] = 0\n return arr\n\ndef normalize_hist(hist_dict0):\n '''\n Given a histogram in dictionary form consisting\n of 'key' : count, generate a new histogram normalized\n by the count totals\n '''\n \n hist_dict = hist_dict0.copy()\n \n all_vals = list(hist_dict.values())\n sum_vals = np.sum(all_vals)\n\n # modify in place\n hist_dict.update((k, v/sum_vals) for k, v in hist_dict.items())\n \n return hist_dict\n\n\ndef shannon_entropy(pi_set0):\n '''\n Given a set of probabilities, compute the Shannon\n entropy, dropping any zeros\n '''\n pi_set = np.array(pi_set0)\n pi_set_nonzero = np.copy(pi_set[pi_set>0])\n \n hi = pi_set_nonzero.dot(np.log2(pi_set_nonzero))\n \n out = -np.sum(hi)\n \n return out\n\ndef layer_entropy(arr):\n '''\n Find entropy an array assuming that the last\n axis are binary features, and the earlier axes\n index samples\n\n Interpretation: Finds the average firing rate of \n each neuron across all training examples.\n Assumes on/off firing rate\n\n '''\n\n num_feats = arr.shape[-1] \n arr_flat = np.reshape(arr, (-1, num_feats)) \n pf = np.mean(arr_flat, axis=0)\n\n ent_vals = [shannon_entropy([pf_val, 1-pf_val]) for pf_val in pf]\n\n return np.array(ent_vals)\n\n\ndef find_dead(arr, axis=-1):\n '''\n Given an array, count the number of axes where\n all samples evaluated to the same value\n \n Inputs:\n arra : np.array\n an array of shape (n_samples, n_features)\n \n Returns:\n where_dead : list\n The axes of the dead neurons\n '''\n where_dead = list()\n for ax_ind in range(arr.shape[axis]):\n vals = arr[...,ax_ind]\n val_med = np.median(vals)\n if np.allclose(vals, val_med):\n where_dead.append(ax_ind)\n \n return where_dead\n\n\n\n\n\n\n", "sub_path": "utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 5121, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "matplotlib.pyplot.gca", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.margins", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.NullLocator", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.NullLocator", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.array_split", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.polyfit", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.uint64", "line_number": 100, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 114, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.log2", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 197, "usage_type": "call"}]} +{"seq_id": "407318494", "text": "# -*- coding: utf-8 -*-\r\nimport shapefile\r\nimport os\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd\r\nfrom scipy import stats\r\nimport re\r\nimport matplotlib.patches as patches\r\nimport itertools\r\nimport pyproj\r\n\r\n# ----------------------------------------------------------------------------------------------------------------- #\r\ndef dms2dd(degrees, minutes, seconds, direction):\r\n dd = float(degrees) + float(minutes) / 60 + float(seconds) / (60 * 60);\r\n if direction == 'S' or direction == 'W':\r\n dd *= -1\r\n return dd;\r\n\r\n# ----------------------------------------------------------------------------------------------------------------- #\r\ndef parse_dms(dms):\r\n parts = re.split('[°\\'\"]+', dms)\r\n dd = dms2dd(parts[0], parts[1], parts[2], parts[3])\r\n return dd\r\n\r\n# ----------------------------------------------------------------------------------------------------------------- #\r\ndef find_nearest(array, value): \r\n array = np.array(array)\r\n idx = (np.abs(array - value)).argmin()\r\n return idx, array[idx]\r\n\r\n# ----------------------------------------------------------------------------------------------------------------- #\r\nclass fileHandling(object):\r\n\r\n '''\r\n Basic Object Properties for file handling\r\n '''\r\n\r\n def __init__(self,fn,ddir):\r\n self.shDir = ddir\r\n self.fn = fn\r\n\r\n# ----------------------------------------------------------------------------------------------------------------- #\r\nclass analyzeShape(fileHandling):\r\n '''\r\n Functions to analyze shapefiles\r\n '''\r\n # ----------------------------------------------------------------------------------------------------------------- #\r\n def __init__(self,fn,ddir):\r\n \"\"\"\r\n\r\n :rtype: object\r\n \"\"\"\r\n super(analyzeShape,self).__init__(fn, ddir)\r\n fln = self.fn + '.shp'\r\n fn = os.path.join(self.shDir, fln)\r\n myshp = open(fn, \"rb\")\r\n\r\n fln = self.fn + '.dbf'\r\n fn = os.path.join(self.shDir, fln)\r\n mydbf = open(fn, \"rb\")\r\n\r\n self.sf = shapefile.Reader(shp=myshp, dbf=mydbf)\r\n\r\n # ----------------------------------------------------------------------------------------------------------------- #\r\n def scatterPlotPoints(self):\r\n \"\"\"\r\n Funtion to scatter plot the points which are inside a shape file.\r\n Pay attention: fileName is the file name but without extension!\r\n :param fileName:\r\n :param shDir:\r\n :return:\r\n \"\"\"\r\n\r\n # get both geometry and records simultaneously\r\n shapeRecs = self.sf.shapeRecords()\r\n\r\n # Make scatter plot of x and y\r\n xco = []\r\n yco = []\r\n for i in np.arange(len(shapeRecs)):\r\n x, y = shapeRecs[i].record[2], shapeRecs[i].record[3]\r\n xco.append(x)\r\n yco.append(y)\r\n\r\n fig, ax = plt.subplots()\r\n ax.scatter(xco,yco)\r\n plt.show()\r\n\r\n # ----------------------------------------------------------------------------------------------------------------- #\r\n def scatterPlotPointsAndChoseROI(self):\r\n \"\"\"\r\n Chose your region of interest (ROI) by first clicking on the left bottom corner, and\r\n then on the upper right corner.\r\n :return:\r\n \"\"\"\r\n\r\n # Simple mouse click function to store coordinates\r\n def onclick(event):\r\n global ix, iy\r\n ix, iy = event.xdata, event.ydata\r\n #print 'x = %d, y = %d'% (ix, iy)\r\n\r\n self.coords.append((ix, iy))\r\n\r\n # Disconnect after 2 clicks\r\n if len(self.coords) == 2:\r\n fig.canvas.mpl_disconnect(cid)\r\n plt.close(1)\r\n\r\n return\r\n\r\n # get both geometry and records simultaneously\r\n shapeRecs = self.sf.shapeRecords()\r\n\r\n # Make scatter plot of x and y\r\n xco = []\r\n yco = []\r\n for i in np.arange(len(shapeRecs)):\r\n x, y = shapeRecs[i].record[2], shapeRecs[i].record[3]\r\n xco.append(x)\r\n yco.append(y)\r\n\r\n fig = plt.figure(1)\r\n ax = fig.add_subplot(111)\r\n ax.scatter(xco, yco)\r\n\r\n self.coords = []\r\n\r\n # Call click func\r\n cid = fig.canvas.mpl_connect('button_press_event', onclick)\r\n\r\n plt.title('Chose ROI (Rect.): 1/ left bottom 2/ upper right corner')\r\n\r\n plt.show(1)\r\n\r\n fig = plt.figure()\r\n ax = fig.add_subplot(111) # , aspect = 'equal')\r\n ax.scatter(xco, yco)\r\n\r\n cornerx = self.coords[0][0]\r\n cornery = self.coords[0][1]\r\n width = np.abs(self.coords[0][0] - self.coords[1][0])\r\n height = np.abs(self.coords[0][1] - self.coords[1][1])\r\n\r\n ax.add_patch(patches.Rectangle((cornerx, cornery), width, height, fill=False))\r\n\r\n plt.show()\r\n\r\n # ----------------------------------------------------------------------------------------------------------------- #\r\n def shapeToDF(self, switchClicking):\r\n \"\"\"\r\n\r\n :return:\r\n \"\"\"\r\n\r\n #manual to avoid the clicking .. \r\n if (switchClicking == False):\r\n self.coords = []\r\n self.coords.append((80136.2903226, 229159.375))\r\n self.coords.append((80449.5967742, 229340.625))\r\n\r\n # make DataFrame of x, y, z\r\n shapeRecs = self.sf.shapeRecords()\r\n listShape = ['x-coor', 'y-coor', 'z-coor']\r\n coordinates = []\r\n for i in np.arange(len(shapeRecs)):\r\n x, y, z = shapeRecs[i].record[2], shapeRecs[i].record[3], shapeRecs[i].record[4]\r\n co = []\r\n co.append(x)\r\n co.append(y)\r\n co.append(z)\r\n\r\n co = np.array(co)\r\n dco = np.array([co])\r\n if i == 0:\r\n coordinates = dco\r\n else:\r\n coordinates = np.concatenate((coordinates, dco))\r\n\r\n dfCoor = pd.DataFrame(coordinates, columns=listShape)\r\n \r\n cornerx = float(self.coords[0][0])\r\n cornery = float(self.coords[0][1])\r\n width = np.abs(self.coords[0][0] - self.coords[1][0])\r\n height = np.abs(self.coords[0][1] - self.coords[1][1])\r\n\r\n self.dfTransect = dfCoor[(cornerx <= dfCoor['x-coor']) & (dfCoor['x-coor'] <= cornerx + width) &\r\n (cornery <= dfCoor['y-coor']) & (dfCoor['y-coor'] <= cornery + height)]\r\n\r\n\r\n # ----------------------------------------------------------------------------------------------------------------- #\r\n def projectTransectToLine(self):\r\n \"\"\"\r\n\r\n :return:\r\n \"\"\"\r\n\r\n # 1. use polyfit\r\n #p = np.poly1d(np.polyfit(dfInlaat['x-coor'], dfInlaat['y-coor'], 1))\r\n #lijn = p(dfInlaat['x-coor'])\r\n # ax.plot(dfInlaat['x-coor'], lijn)\r\n\r\n # 2. uses stats library\r\n self.slope, self.intercept, r_value, p_value, std_err = stats.linregress(self.dfTransect['x-coor'], self.dfTransect['y-coor'])\r\n\r\n # project points on to the line\r\n d = -1.0 / self.slope\r\n c = self.dfTransect['y-coor'].values - d * self.dfTransect['x-coor'].values\r\n self.xe = (self.intercept - c) / (d - self.slope)\r\n self.ye = self.slope * self.xe + self.intercept\r\n\r\n # ----------------------------------------------------------------------------------------------------------------- #\r\n def projectPointToLine(self, measuredLoc):\r\n \"\"\"\r\n\r\n :return:\r\n \"\"\"\r\n\r\n # project points on to the line\r\n d = -1.0 / self.slope\r\n xlo = np.array(measuredLoc['x'])\r\n ylo = np.array(measuredLoc['y'])\r\n c = ylo - d * xlo\r\n self.xp = (self.intercept - c) / (d - self.slope)\r\n self.yp = self.slope * self.xp + self.intercept\r\n\r\n # ----------------------------------------------------------------------------------------------------------------- #\r\n def dmsloc2dd(self, s):\r\n s1, s2 = s.split(',')\r\n lat, long = parse_dms(s1), parse_dms(s2)\r\n return lat, long\r\n\r\n\r\n # ----------------------------------------------------------------------------------------------------------------- #\r\n def plotGPSCoor(self, gpsC, wgs84, lamb72New):\r\n\r\n z = 0\r\n fig, ax = plt.subplots()\r\n colors = itertools.cycle([\"r\", \"c\"])\r\n markers = itertools.cycle([\">\", \"+\"])\r\n\r\n for gpsCoor in gpsC:\r\n\r\n xList = []\r\n yList = []\r\n zList = []\r\n latList = []\r\n longList = []\r\n\r\n for s in gpsCoor:\r\n # 0. convert dms to dd\r\n lat, long = self.dmsloc2dd(s)\r\n\r\n # 1. transform coordinates\r\n # from WGS84 to lamb72New \r\n x, y, zt = pyproj.transform(wgs84, lamb72New, long, lat, z)\r\n\r\n latList.append(lat)\r\n longList.append(long)\r\n xList.append(x)\r\n yList.append(y)\r\n zList.append(z)\r\n\r\n measureloc = {'location': ['loc 2', 'loc 1', 'loc 3'],\r\n 'x': xList,\r\n 'y': yList}\r\n\r\n # ----------------------------------------------------------------------------------------------------------------- #\r\n # project measurement point on line\r\n self.projectPointToLine(measureloc)\r\n\r\n x = np.array(xList)\r\n y = np.array(yList)\r\n ax.scatter(x, y, color=next(colors), marker=next(markers))\r\n\r\n ax.scatter(self.xp, self.yp, color='b')\r\n ax.scatter(self.dfTransect['x-coor'], self.dfTransect['y-coor'], color='red')\r\n ax.plot(self.xe, self.ye)\r\n ax.axis('equal')\r\n plt.show()\r\n\r\n # ----------------------------------------------------------------------------------------------------------------- #\r\n def computeAngleY(self):\r\n diffX = np.amax(self.xe) - np.amin(self.xe)\r\n idxmin = (self.xe).argmin()\r\n idxmax = (self.xe).argmax()\r\n \r\n diffY = self.ye[idxmax] - self.ye[idxmin]\r\n\r\n self.angle = np.arctan2(diffX,diffY)*360./(2.*np.pi)\r\n self.normal = self.angle + 90. \r\n\r\n # ----------------------------------------------------------------------------------------------------------------- #\r\n def probeVertValues(self):\r\n z = 0\r\n zl = self.dfTransect['z-coor'].values\r\n self.zp = []\r\n for xs in self.xp:\r\n # print xs\r\n ii = 0\r\n for x in self.xe:\r\n if xs > x:\r\n # print x, ii\r\n z = 0.5 * (zl[ii] + zl[ii - 1])\r\n self.zp.append(z)\r\n break\r\n ii += 1\r\n\r\n # ----------------------------------------------------------------------------------------------------------------- #\r\n def plotVertTrans(self):\r\n cornerx = np.amin(self.xe)\r\n cornery = np.amin(self.ye)\r\n\r\n self.xRelTransect = []\r\n for i in np.arange(len(self.xe)):\r\n xd = np.sqrt(np.power((self.xe[i] - cornerx), 2.) + np.power((self.ye[i] - cornery), 2.))\r\n self.xRelTransect.append(xd)\r\n\r\n fig, ax = plt.subplots()\r\n ax.plot(self.xRelTransect, self.dfTransect['z-coor'].values)\r\n\r\n self.xRelMeas = []\r\n for i in np.arange(len(self.xp)):\r\n xd = np.sqrt(np.power((self.xp[i] - cornerx), 2.) + np.power((self.yp[i] - cornery), 2.))\r\n self.xRelMeas.append(xd)\r\n\r\n ax.scatter(self.xRelMeas, self.zp, c='red')\r\n\r\n plt.show()\r\n\r\n # ----------------------------------------------------------------------------------------------------------------- #\r\n def adjustTrans(self, xL, xF): \r\n \r\n zT = self.dfTransect['z-coor'].values\r\n \r\n idx, xLast = find_nearest(self.xRelTransect, xL)\r\n zLast = zT[idx]\r\n\r\n idx, xFirst = find_nearest(self.xRelTransect, xF)\r\n zFirst = zT[idx]\r\n \r\n grad = (zLast - zFirst) / (xLast - xFirst)\r\n\r\n self.zAdj = []\r\n for i in np.arange(len(self.xRelTransect)):\r\n if (xLast > self.xRelTransect[i] and xFirst < self.xRelTransect[i]):\r\n zz = zFirst + grad*(self.xRelTransect[i] - xFirst)\r\n self.zAdj.append(zz)\r\n else: \r\n self.zAdj.append(zT[i])\r\n \r\n self.xRelTransect = np.array(self.xRelTransect)\r\n self.zAdj = np.array(self.zAdj)\r\n\r\n listShape = ['xRel']\r\n s = pd.Series(self.xRelTransect)\r\n self.dfAdjTrans = pd.DataFrame(s, columns=listShape)\r\n self.dfAdjTrans['zAdj'] = self.zAdj \r\n\r\n self.dfAdjTrans = self.dfAdjTrans.sort_values(by ='xRel', ascending=True)\r\n\r\n # ----------------------------------------------------------------------------------------------------------------- #\r\n def adjustMeas(self, x1, x2, x3):\r\n xA = self.dfAdjTrans['xRel'].values\r\n zA = self.dfAdjTrans['zAdj'].values\r\n \r\n self.xAdj = []\r\n self.zAdj = [] \r\n\r\n idx, xAdj1 = find_nearest(xA, x1)\r\n zAdj1 = zA[idx]\r\n\r\n self.xAdj.append(xAdj1)\r\n self.zAdj.append(zAdj1)\r\n\r\n idx, xAdj2 = find_nearest(xA, x2)\r\n zAdj2 = zA[idx]\r\n self.xAdj.append(xAdj2)\r\n self.zAdj.append(zAdj2)\r\n\r\n idx, xAdj3 = find_nearest(xA, x3)\r\n zAdj3 = zA[idx]\r\n self.xAdj.append(xAdj3)\r\n self.zAdj.append(zAdj3)\r\n \r\n listShape = ['xRelPoints']\r\n s = pd.Series(self.xAdj)\r\n self.dfAdjMeas = pd.DataFrame(s, columns=listShape)\r\n self.dfAdjMeas['zAdjPoints'] = self.zAdj \r\n\r\n \r\n\r\n\r\n \r\n\r\n\r\n\r\n\r\n\r\n", "sub_path": "areaTransect/analyzeShapeFile/analyzeShapeFileFunctions.py", "file_name": "analyzeShapeFileFunctions.py", "file_ext": "py", "file_size_in_byte": 13685, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "re.split", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "shapefile.Reader", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "numpy.abs", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.patches.Rectangle", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 146, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 148, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 179, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 186, "usage_type": "call"}, {"api_name": "scipy.stats.linregress", "line_number": 205, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 205, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 223, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 239, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 239, "usage_type": "name"}, {"api_name": "itertools.cycle", "line_number": 240, "usage_type": "call"}, {"api_name": "itertools.cycle", "line_number": 241, "usage_type": "call"}, {"api_name": "pyproj.transform", "line_number": 257, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 273, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 274, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 281, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 281, "usage_type": "name"}, {"api_name": "numpy.amax", "line_number": 285, "usage_type": "call"}, {"api_name": "numpy.amin", "line_number": 285, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 291, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 291, "usage_type": "attribute"}, {"api_name": "numpy.amin", "line_number": 312, "usage_type": "call"}, {"api_name": "numpy.amin", "line_number": 313, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 316, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 317, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 317, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 320, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 320, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 324, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 325, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 325, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 330, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 330, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 346, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 353, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 354, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 357, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 358, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 388, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 389, "usage_type": "call"}]} +{"seq_id": "458080307", "text": "#!/usr/bin/python3\nimport pandas as pd\nimport numpy as np\nimport xlsxwriter\nimport glob\nfrom functools import reduce\n\ndef get_col_widths(dataframe):\n # First we find the maximum length of the index column \n idx_max = max([len(str(s)) for s in dataframe.index.values] + [len(str(dataframe.index.name))])\n # Then, we concatenate this to the max of the lengths of column name and its values for each column, left to right\n return [idx_max] + [max([len(str(s)) for s in dataframe[col].values] + [len(col)]) for col in dataframe.columns]\n\n# importing sys module\nimport sys\n\n# Import first argument as IRFinder/Combined folder\nmyfolder = sys.argv[1]\n\n# Import third argument as condition string, gets converted to characters\ncond=sys.argv[3]\ncond=list(cond.split(\",\"))\n\n# reading second argument as final output file \nwith open(sys.argv[2], 'wb') as outf:\n\n # Define final data frame as reference to align all data to\n df_final = pd.DataFrame(columns=['uniqueID', 'gene_name', 'gene_id', 'altgenes'])\n \n # Get all conditions \n print('# of conditions:')\n print(len(cond))\n print('Conditions:')\n print(cond)\n\n # Loop over each condition in leafcutter/Combined folder\n for f in cond[1:]:\n myfilename=('{folder}/control_vs_{condition}_final.xlsx'.format(folder=myfolder, condition=f))\n df = pd.read_excel(myfilename)\n\n # Report data before filtering\n print('# rows before filtering:')\n print(len(df.index)) \n \n # Define uniqueID\n df[\"uniqueID\"] = df['chr'].map(str) + \":\" + df['start'].map(str) + \"-\" + df['end'].map(str) + \":\" + df['gene_id'].map(str)\n\n #drop unnecessary columns\n df = df.drop(columns=['Unnamed: 0', 'chr', 'start', 'end', 'logef', 'status', 'loglr', 'df', 'p'])\n condition=f\n # Reorder columns, split coordinates and give p.adjust and log2FoldChange condition names\n df = df[['uniqueID', 'gene_name', 'gene_id', 'altgenes', 'deltapsi', 'p.adjust', 'control', condition]]\n df.columns = ['uniqueID', 'gene_name', 'gene_id', 'altgenes', \"%s_dPSI\" % condition, \"%s_p.adjust\" % condition, \"PSI_control_%s\" % condition, \"PSI_%s\" % condition,]\n df_filtered = df[(df[\"%s_p.adjust\" % condition] < 0.05)]\n print('# rows after filtering')\n print(len(df_filtered.index))\n df_final = pd.merge(df_final,df_filtered,on=['uniqueID', 'gene_name', 'gene_id', 'altgenes'], how='outer')\n print('# rows of final df')\n print(len(df_final.index))\n print(\"Condition %s processed\" % condition)\n\n # Get proper coordinates\n IDs = df_final[\"uniqueID\"].str.split(\":\", n = 2, expand = True)\n Coords = IDs[1].str.split(\"-\", expand = True)\n df_final[\"coordinates\"] = IDs[0] +':'+ Coords[0] +'-'+ Coords[1]\n cols = df_final.columns.tolist()\n cols = cols[-1:] + cols[:-1]\n df_final = df_final[cols] \n\n # Get table range\n end_row = len(df_final.index)\n end_column = len(df_final.columns)-1\n cell_range = xlsxwriter.utility.xl_range(0, 0, end_row, end_column)\n\n #write to Excel file\n writer = pd.ExcelWriter(outf, engine='xlsxwriter')\n df_final.to_excel(writer, sheet_name='total', index=False)\n workbook = writer.book\n worksheet = writer.sheets['total']\n\n # Hack for preserving column headers when inserting table\n header = [{'header': di} for di in df_final.columns.tolist()]\n worksheet.add_table(cell_range,{'header_row': True,'columns':header})\n\n # Formating the output excel file\n worksheet.set_zoom(100)\n for i, width in enumerate(get_col_widths(df_final)):\n worksheet.set_column(i, i, width)\n worksheet.set_column(0, 0, 25)\n worksheet.set_column(1, 1, 10)\n worksheet.set_column(2, 2, 15)\n worksheet.set_column(3, 3, 20)\n worksheet.set_column(4, 4, 10)\n for f in range(5,end_column-1):\n worksheet.conditional_format(0, f, end_row-1, f, {'type':'3_color_scale', 'min_color': \"red\", 'mid_color': \"white\", 'max_color': \"green\"})\n writer.save()\n", "sub_path": "tools/leafcutter/xlsx_combine_leafcutter.py", "file_name": "xlsx_combine_leafcutter.py", "file_ext": "py", "file_size_in_byte": 4018, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "sys.argv", "line_number": 18, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 21, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 28, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 39, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 57, "usage_type": "call"}, {"api_name": "xlsxwriter.utility.xl_range", "line_number": 73, "usage_type": "call"}, {"api_name": "xlsxwriter.utility", "line_number": 73, "usage_type": "attribute"}, {"api_name": "pandas.ExcelWriter", "line_number": 76, "usage_type": "call"}]} +{"seq_id": "461391266", "text": "# -*- coding: utf-8 -*-\nimport numpy as np\nimport pandas as pd\n\nfrom patchwork._sample import find_unlabeled, find_fully_labeled\nfrom patchwork._sample import find_partially_labeled, find_subset\nfrom patchwork._sample import stratified_sample, unlabeled_sample\nfrom patchwork._sample import stratified_subset_sample\n\n\ntestdf = pd.DataFrame({\n \"filepath\":[\"a.jpg\", \"b.jpg\", \"c.jpg\", \"d.jpg\", \"e.jpg\"],\n \"exclude\":[True, False, False, False, False],\n \"validation\":[False, False, False, False, False],\n \"class1\":[None, 1, 0, 1, 1],\n \"class2\":[None, 0, 1, None, None],\n \"subset\":[0,0,1,1,1]\n })\n\n\ndef test_find_unlabeled():\n unlab = find_unlabeled(testdf)\n assert unlab.sum() == 1\n assert \"a.jpg\" in testdf[\"filepath\"][unlab].values\n\ndef test_find_fully_labeled():\n flab = find_fully_labeled(testdf)\n assert flab.sum() == 2\n assert \"b.jpg\" in testdf[\"filepath\"][flab].values\n\n\ndef test_partially_unlabeled():\n plab = find_partially_labeled(testdf)\n assert plab.sum() == 2\n assert \"d.jpg\" in testdf[\"filepath\"][plab].values\n\n\ndef test_find_subset_unlabeled_all():\n s0 = find_subset(testdf, \"unlabeled\", \"not excluded\", \"all\")\n assert s0.sum() == 0\n\n\ndef test_find_subset_labeled_all():\n s0 = find_subset(testdf, \"labeled\", \"not excluded\", \"all\")\n assert s0.sum() == 2\n \n \n\ndef test_find_subset_unlabeled_excluded_all():\n s0 = find_subset(testdf, \"unlabeled\", \"excluded\", \"all\")\n assert s0.sum() == 1\n assert \"a.jpg\" in testdf[s0].filepath.values\n \n\ndef test_find_subset_partial_all():\n s0 = find_subset(testdf, \"partial\", \"not excluded\", \"all\")\n assert s0.sum() == 2\n for f in [\"d.jpg\", \"e.jpg\"]:\n assert f in testdf[s0].filepath.values\n\ndef test_find_subset_labeled_subset():\n s0 = find_subset(testdf, \"labeled\", \"not excluded\", \"subset: 0\")\n assert s0.sum() == 1\n assert \"b.jpg\" in testdf[s0].filepath.values\n \n\ndef test_find_subset_partial_subset():\n s0 = find_subset(testdf, \"partial\", \"not excluded\", \"subset: 1\")\n assert s0.sum() == 2\n assert \"d.jpg\" in testdf[s0].filepath.values\n assert \"e.jpg\" in testdf[s0].filepath.values\n \n\ndef test_find_subset_partial_contains():\n s0 = find_subset(testdf, \"partial\", \"not excluded\", \"contains: class1\")\n assert s0.sum() == 2\n assert \"d.jpg\" in testdf[s0].filepath.values\n assert \"e.jpg\" in testdf[s0].filepath.values\n \n \ndef test_find_subset_labeled_doesnt_contain():\n s0 = find_subset(testdf, \"labeled\", \"not excluded\", \"doesn't contain: class1\")\n assert s0.sum() == 1\n assert \"c.jpg\" in testdf[s0].filepath.values\n \n \n\n\ndef test_stratified_sampler():\n N = 100\n outlist, ys = stratified_sample(testdf, N=N, return_indices=False)\n \n assert len(outlist) == N\n assert ys.shape[0] == N\n assert ys.shape[1] == 2\n #assert isinstance(outlist[0], str)\n #assert False, \"this should definitely be tested\"\n \n\ndef test_stratified_sampler_instance_sampling():\n N = 100\n outlist, ys = stratified_sample(testdf, N=N, return_indices=False,\n sampling=\"instance\")\n \n assert len(outlist) == N\n assert ys.shape[0] == N\n assert ys.shape[1] == 2\n \n\ndef test_stratified_sampler_sqrt_sampling():\n N = 100\n outlist, ys = stratified_sample(testdf, N=N, return_indices=False,\n sampling=\"squareroot\")\n \n assert len(outlist) == N\n assert ys.shape[0] == N\n assert ys.shape[1] == 2\n \n \ndef test_stratified_subset_sample():\n N = 100\n num_domains = 5\n df = pd.DataFrame({\"filepath\":[\"%s.jpg\"%i for i in range(N)],\n \"subset\":np.random.choice([\"domain_%s\"%i for i in range(num_domains)], size=N),\n \"foo\":np.random.choice([1,0,np.nan], size=N),\n \"bar\":np.random.choice([1,0,np.nan], size=N)})\n df[\"exclude\"] = False\n df[\"validation\"] = False\n \n num_samples = 101\n s,y = stratified_subset_sample(df, num_samples)\n \n assert len(s) == num_samples\n assert len(y) == num_samples\n ", "sub_path": "patchwork/tests/test_sample.py", "file_name": "test_sample.py", "file_ext": "py", "file_size_in_byte": 4103, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "pandas.DataFrame", "line_number": 11, "usage_type": "call"}, {"api_name": "patchwork._sample.find_unlabeled", "line_number": 22, "usage_type": "call"}, {"api_name": "patchwork._sample.find_fully_labeled", "line_number": 27, "usage_type": "call"}, {"api_name": "patchwork._sample.find_partially_labeled", "line_number": 33, "usage_type": "call"}, {"api_name": "patchwork._sample.find_subset", "line_number": 39, "usage_type": "call"}, {"api_name": "patchwork._sample.find_subset", "line_number": 44, "usage_type": "call"}, {"api_name": "patchwork._sample.find_subset", "line_number": 50, "usage_type": "call"}, {"api_name": "patchwork._sample.find_subset", "line_number": 56, "usage_type": "call"}, {"api_name": "patchwork._sample.find_subset", "line_number": 62, "usage_type": "call"}, {"api_name": "patchwork._sample.find_subset", "line_number": 68, "usage_type": "call"}, {"api_name": "patchwork._sample.find_subset", "line_number": 75, "usage_type": "call"}, {"api_name": "patchwork._sample.find_subset", "line_number": 82, "usage_type": "call"}, {"api_name": "patchwork._sample.stratified_sample", "line_number": 91, "usage_type": "call"}, {"api_name": "patchwork._sample.stratified_sample", "line_number": 102, "usage_type": "call"}, {"api_name": "patchwork._sample.stratified_sample", "line_number": 112, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 124, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 125, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 125, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 126, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 126, "usage_type": "attribute"}, {"api_name": "patchwork._sample.stratified_subset_sample", "line_number": 131, "usage_type": "call"}]} +{"seq_id": "148193296", "text": "from __future__ import absolute_import\n\nimport socket\nimport celery.states\nimport celery.worker.state\n\nfrom ixiacr.lib.utils import admin_helper\nfrom ixiacr.tasks.router import IxiaTaskRouter\nfrom ixiacr.tasks.celery import IxiaCelery\nfrom celery.task.control import inspect\nfrom ixiacr.lib import IxiaLogger\n\nixiacrlogger = IxiaLogger(__name__)\n\n\ndef queue_task_if_possible(task):\n \"\"\"\n Check celery queue status for task. If the queue is available, send\n the task to the queue, otherwise run the task in the current context\n \"\"\"\n\n def do_queue_task_if_possible(*args, **kwargs):\n # Get the queue for our task\n queue = IxiaTaskRouter().route_for_task(task.name)['routing_key']\n inspector = IxiaCelery.control.inspect()\n\n # Check to see if we have a queue\n have_available_queue = False\n stats = None\n\n try:\n stats = inspector.stats()\n except socket.error:\n # Unable to connect to our backend to collect stats,\n # so we definitely don't have any queues...\n pass\n\n if stats:\n for worker in stats.keys():\n if worker.startswith(queue):\n # We do; check to see if it's available\n if 'max-concurrency' in stats[worker]['pool']:\n num_workers = stats[worker]['pool']['max-concurrency']\n else:\n num_workers = 0 # threading module doesn't give\n # us this info\n queue_is_free = (len(inspector.active()[worker]) == 0)\n\n if queue_is_free or num_workers > 1:\n have_available_queue = True\n\n # Now run the task\n if have_available_queue:\n return task.delay(*args, **kwargs).get()\n else:\n return task(*args, **kwargs)\n\n return do_queue_task_if_possible\n\n\ndef get_worker_task_count(name=None):\n \"\"\"\n Count the number of tasks in the current worker that match 'name'.\n If name is blank, return the total number of tasks in the worker\n \"\"\"\n ntasks = 0\n for request in celery.worker.state.active_requests:\n if name is None or request.task_name.find(name) >= 0:\n ntasks += 1\n\n return ntasks\n\n\n@IxiaCelery.task\ndef queue_admin_helper(command, data):\n \"\"\"\n Run admin helper in task queue\n \"\"\"\n return admin_helper(command, data)\n\n\ndef get_task_running_count(task):\n \"\"\" Determine number of running tasks matching task name\n\n :param task: celery Task object or Name of task string\n\n :returns: number of instances running\n \"\"\"\n inspector = inspect()\n active = inspector.active()\n\n if isinstance(task, basestring):\n task_name = task\n else:\n task_name = task.name\n\n found = 0\n for host_tasks in active.values():\n for cur_task in host_tasks:\n ixiacrlogger.debug('Found running task: {0}'.\n format(cur_task['name']))\n if cur_task['name'] == task_name:\n found += 1\n\n ixiacrlogger.debug('Found {0} matching tasks running for task name={1}'.\n format(found, task_name))\n return found\n\n\ndef is_task_already_running(task):\n \"\"\" Determine if an instance of task is already running more than\n once by checking the name of active tasks\n\n Intended to be called from within an executing task.\n\n :param task: celery Task object\n\n :returns: True if the task is running more than 1 instance\n False if one or zero instances are running\n \"\"\"\n found = get_task_running_count(task)\n return True if found > 1 else False\n\n\n# ##\n# various tasks to check the status of celery task chains\n###\n\ndef task_chain_is_running(result):\n \"\"\"\n Check a celery result chain. Return true if any tasks in the chain\n are still running; false otherwise.\n \"\"\"\n customCount = readyCount = unreadyCount = failureCount = 0\n\n curResult = result\n while curResult is not None:\n status = curResult.status\n\n if status in celery.states.UNREADY_STATES:\n unreadyCount += 1\n elif status in celery.states.PROPAGATE_STATES:\n failureCount += 1\n elif status in celery.states.READY_STATES:\n readyCount += 1\n elif status not in celery.states.ALL_STATES:\n customCount += 1\n\n # Get next result in the chain\n curResult = curResult.parent\n\n # We consider a chain running if\n # 1) there are no failures\n # 2) At least one task has a result\n # 3) and 1 or more tasks are ready to run OR we have a\n # task in a custom state\n return (failureCount == 0 and\n readyCount > 0 and\n (unreadyCount > 0 or customCount > 0))\n\n\ndef task_chain_has_passed(result):\n \"\"\"\n Check a celery result chain. Return true if all tasks have succeeded;\n false otherwise\n \"\"\"\n resultCount = 0\n successCount = 0\n\n curResult = result\n while curResult is not None:\n resultCount += 1\n\n if curResult.ready() and curResult.status == 'SUCCESS':\n successCount += 1\n\n # Get next result in the chain\n curResult = curResult.parent\n\n return (resultCount == successCount)\n\n\ndef task_chain_has_failed(result):\n \"\"\"\n Check a celery result chain. Return true if any tasks have failed.\n Return false otherwise.\n \"\"\"\n failureCount = 0\n\n curResult = result\n while curResult is not None:\n if curResult.ready():\n # Check for failure\n if curResult.status in celery.states.PROPAGATE_STATES:\n failureCount += 1\n\n # Get next result in the chain\n curResult = curResult.parent\n\n return (failureCount > 0)\n\n\ndef get_running_task_result(result):\n \"\"\"\n Return the result object that corresponds to the currenly running\n task in the result chain\n \"\"\"\n curResult = result\n readyResult = pendingResult = customResult = None\n\n while curResult is not None:\n status = curResult.status\n if status in celery.states.READY_STATES:\n readyResult = curResult\n elif status not in celery.states.ALL_STATES:\n customResult = curResult\n elif status in celery.states.UNREADY_STATES:\n pendingResult = curResult\n\n # Get next result in the chain\n curResult = curResult.parent\n\n return (customResult or pendingResult or readyResult)\n\n\ndef get_task_from_chain(last_task, task_name):\n \"\"\" Return a task by name from a task chain\n\n :param last_task: where to start walking the chain by parent\n :param task_name: name of task we are searching for\n\n :returns: Task object matching task_name or exception\n \"\"\"\n ixiacrlogger.debug('checking chain for task_name={0}'.format(task_name))\n cur_task = last_task\n while cur_task:\n if cur_task.task_name == task_name:\n ixiacrlogger.debug(\n 'Located task_name={0} in task_chain; '\n 'task_id={1}; last_task.task_id={2}'.format(\n task_name, cur_task.task_id, last_task.task_id))\n\n return cur_task\n\n cur_task = cur_task.parent\n\n raise Exception('Could not locate task_name={0} in task_chain; '\n 'last_task.task_id={1}'.format(\n task_name, last_task.task_id))\n\n\ndef dump_task_chain_status(result):\n \"\"\"\n Log all tasks and status contained in result\n \"\"\"\n curResult = result\n ixiacrlogger.debug('TASK CHAIN DUMP: BEGIN')\n while curResult is not None:\n msg = ('{0} ({1}): state = {2}, result = {3}'.format(\n curResult.task_name, curResult.task_id,\n curResult.state, curResult.result))\n ixiacrlogger.debug(msg)\n\n curResult = curResult.parent\n\n ixiacrlogger.debug('TASK_CHAIN_DUMP: END')\n\n\ndef is_any_task_running(queue_names, task_names):\n \"\"\" Determine if any of the task names in the provided list are running\n\n :param task_names: Names of tasks\n\n :returns: True if task running\n \"\"\"\n inspector = inspect(queue_names)\n\n active = inspector.active()\n if active:\n for host_tasks in active.values():\n for cur_task in host_tasks:\n if 'name' in cur_task and cur_task['name'] in task_names:\n return True\n\n return False\n", "sub_path": "IxiaCR/ixiacr/tasks/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 8400, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "ixiacr.lib.IxiaLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "ixiacr.tasks.router.IxiaTaskRouter", "line_number": 24, "usage_type": "call"}, {"api_name": "ixiacr.tasks.celery.IxiaCelery.control.inspect", "line_number": 25, "usage_type": "call"}, {"api_name": "ixiacr.tasks.celery.IxiaCelery.control", "line_number": 25, "usage_type": "attribute"}, {"api_name": "ixiacr.tasks.celery.IxiaCelery", "line_number": 25, "usage_type": "name"}, {"api_name": "socket.error", "line_number": 33, "usage_type": "attribute"}, {"api_name": "celery.states.worker", "line_number": 67, "usage_type": "attribute"}, {"api_name": "celery.states", "line_number": 67, "usage_type": "name"}, {"api_name": "ixiacr.lib.utils.admin_helper", "line_number": 79, "usage_type": "call"}, {"api_name": "ixiacr.tasks.celery.IxiaCelery.task", "line_number": 74, "usage_type": "attribute"}, {"api_name": "ixiacr.tasks.celery.IxiaCelery", "line_number": 74, "usage_type": "name"}, {"api_name": "celery.task.control.inspect", "line_number": 89, "usage_type": "call"}, {"api_name": "celery.states.states", "line_number": 140, "usage_type": "attribute"}, {"api_name": "celery.states", "line_number": 140, "usage_type": "name"}, {"api_name": "celery.states.states", "line_number": 142, "usage_type": "attribute"}, {"api_name": "celery.states", "line_number": 142, "usage_type": "name"}, {"api_name": "celery.states.states", "line_number": 144, "usage_type": "attribute"}, {"api_name": "celery.states", "line_number": 144, "usage_type": "name"}, {"api_name": "celery.states.states", "line_number": 146, "usage_type": "attribute"}, {"api_name": "celery.states", "line_number": 146, "usage_type": "name"}, {"api_name": "celery.states.states", "line_number": 194, "usage_type": "attribute"}, {"api_name": "celery.states", "line_number": 194, "usage_type": "name"}, {"api_name": "celery.states.states", "line_number": 213, "usage_type": "attribute"}, {"api_name": "celery.states", "line_number": 213, "usage_type": "name"}, {"api_name": "celery.states.states", "line_number": 215, "usage_type": "attribute"}, {"api_name": "celery.states", "line_number": 215, "usage_type": "name"}, {"api_name": "celery.states.states", "line_number": 217, "usage_type": "attribute"}, {"api_name": "celery.states", "line_number": 217, "usage_type": "name"}, {"api_name": "celery.task.control.inspect", "line_number": 276, "usage_type": "call"}]} +{"seq_id": "108582255", "text": "import json\nfrom pyspark import SparkContext\nsc = SparkContext()\n\nbusiness = sc.textFile('hdfs:///var/si618w17/yelp_academic_dataset_business_updated.json')\nreview = sc.textFile('hdfs:///var/si618w17/yelp_academic_dataset_review_updated.json')\n\n\ndef city(data):\n\tyelp_list = []\n\tcity = data.get('city', None)\n\tbusiness_id = data.get('business_id', None)\n\tyelp_list.append((business_id, city.encode('utf-8')))\n\treturn yelp_list\n\ndef reviews(data):\n\tyelp_list = []\n\tbusiness_id = data.get('business_id', None)\n\tuser_id = data.get('user_id', None)\n\tyelp_list.append((business_id, user_id))\n\treturn yelp_list\n\ndef good_review(data):\n\tyelp_list = []\n\tbusiness_id = data.get('business_id', None)\n\tuser_id = data.get('user_id', None)\n\tstars = data.get('stars', None)\n\tif stars > 3:\n\t\tyelp_list.append((business_id, user_id))\n\treturn yelp_list\n\ndef bad_review(data):\n\tyelp_list = []\n\tbusiness_id = data.get('business_id', None)\n\tuser_id = data.get('user_id', None)\n\tstars = data.get('stars', None)\n\tif stars < 3:\n\t\tyelp_list.append((business_id, user_id))\n\treturn yelp_list\n\ndef unique(data):\n\tnew_list = []\n\tfor x in data:\n\t\tif x in new_list:\n\t\t\tpass\n\t\telse:\n\t\t\tnew_list.append(x)\n\treturn new_list\n\ndef toCSV(data):\n\treturn ','.join(str(x) for x in data)\n\n\nbusiness_data = business.map(lambda line: json.loads(line)) \\\n\t\t\t\t\t.flatMap(city)\n\nreviews_data = review.map(lambda line: json.loads(line))\nreview_data = reviews_data.flatMap(reviews)\npositive_data = reviews_data.flatMap(good_review)\nnegative_data = reviews_data.flatMap(bad_review)\n\nclean_data = business_data.join(review_data) \\\n\t\t\t\t\t.map(lambda x: (x[1][1], [x[1][0]])) \\\n\t\t\t\t\t.reduceByKey(lambda x, y: x + y) \\\n\t\t\t\t\t.map(lambda x: tuple(x[1])) \\\n\t\t\t\t\t.map(unique) \\\n\t\t\t\t\t.map(lambda x: (len(x), 1)) \\\n\t\t\t\t\t.reduceByKey(lambda x, y: x + y) \\\n\t\t\t\t\t.sortByKey(lambda x: x) \\\n\t\t\t\t\t.map(toCSV)\n\n#JOIN: (business, (city, user))\n#MAP: (user, [city])\n#REDUCEBYKEY: (user, [city1, city2, city2, city3])\n#MAP: ([city1, city2, city3])\n#MAP: (3,1)\n#HISTOGRAM: .histogram(30) -->\n#REDUCEBYKEY: # of cities per user\n#SORTBYKEY: sort, in ascending order, # of cities\n#MAP: to CSV\n\nclean_data.collect()\nclean_data.saveAsTextFile('si618_w17_hw5_part2_output_gracfu')\n\npos_data = business_data.join(positive_data) \\\n\t\t\t\t\t\t.map(lambda x: (x[1][1], [x[1][0]])) \\\n\t\t\t\t\t\t.reduceByKey(lambda x, y: x + y) \\\n\t\t\t\t\t\t.map(lambda x: tuple(x[1])) \\\n\t\t\t\t\t\t.map(unique) \\\n\t\t\t\t\t\t.map(lambda x: (len(x), 1)) \\\n\t\t\t\t\t\t.reduceByKey(lambda x, y: x + y) \\\n\t\t\t\t\t\t.sortByKey(lambda x: x) \\\n\t\t\t\t\t\t.map(toCSV)\npos_data.collect()\npos_data.saveAsTextFile('si618_w17_hw5_part2_goodreview_gracfu')\n\nneg_data = business_data.join(negative_data) \\\n\t\t\t\t\t\t.map(lambda x: (x[1][1], [x[1][0]])) \\\n\t\t\t\t\t\t.reduceByKey(lambda x, y: x + y) \\\n\t\t\t\t\t\t.map(lambda x: tuple(x[1])) \\\n\t\t\t\t\t\t.map(unique) \\\n\t\t\t\t\t\t.map(lambda x: (len(x), 1)) \\\n\t\t\t\t\t\t.reduceByKey(lambda x, y: x + y) \\\n\t\t\t\t\t\t.sortByKey(lambda x: x) \\\n\t\t\t\t\t\t.map(toCSV)\nneg_data.collect()\nneg_data.saveAsTextFile('si618_w17_hw5_part2_badreview_gracfu')\n\n", "sub_path": "si618_w17_hw5_part2_gracfu.py", "file_name": "si618_w17_hw5_part2_gracfu.py", "file_ext": "py", "file_size_in_byte": 3008, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "pyspark.SparkContext", "line_number": 3, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 54, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "303717968", "text": "import numpy as np\nimport pytest\n\nfrom climpred import HindcastEnsemble, PerfectModelEnsemble\nfrom climpred.bootstrap import bootstrap_perfect_model\nfrom climpred.checks import DimensionError\nfrom climpred.graphics import plot_bootstrapped_skill_over_leadyear\n\nITERATIONS = 3\n\n\ndef test_mpi_he_plot_bootstrapped_skill_over_leadyear_da(\n PM_da_initialized_1d, PM_da_control_1d\n):\n \"\"\"\n Checks plots from bootstrap_perfect_model works for xr.DataArray.\n \"\"\"\n res = bootstrap_perfect_model(\n PM_da_initialized_1d,\n PM_da_control_1d,\n metric=\"pearson_r\",\n iterations=ITERATIONS,\n reference=\"uninitialized\",\n )\n res_ax = plot_bootstrapped_skill_over_leadyear(res)\n assert res_ax is not None\n\n\ndef test_mpi_he_plot_bootstrapped_skill_over_leadyear_single_uninit_lead(\n PM_da_initialized_1d, PM_da_control_1d\n):\n \"\"\"\n Checks plots from bootstrap_perfect_model works for xr.DataArray.\n \"\"\"\n res = bootstrap_perfect_model(\n PM_da_initialized_1d,\n PM_da_control_1d,\n metric=\"pearson_r\",\n iterations=ITERATIONS,\n reference=[\"uninitialized\", \"persistence\"],\n )\n # set all but first uninit lead to nan\n res[:, 2, 1:] = [np.nan] * (res.lead.size - 1)\n res_ax = plot_bootstrapped_skill_over_leadyear(res)\n assert res_ax is not None\n\n\ndef test_mpi_he_plot_bootstrapped_skill_over_leadyear_ds(\n PM_ds_initialized_1d, PM_ds_control_1d\n):\n \"\"\"\n Checks plots from bootstrap_perfect_model works for xr.Dataset with one variable.\n \"\"\"\n res = bootstrap_perfect_model(\n PM_ds_initialized_1d,\n PM_ds_control_1d,\n metric=\"pearson_r\",\n iterations=ITERATIONS,\n reference=\"uninitialized\",\n )\n assert list(res.coords[\"skill\"]) == [\"initialized\", \"uninitialized\"]\n res_ax = plot_bootstrapped_skill_over_leadyear(res)\n assert res_ax is not None\n\n\n@pytest.mark.parametrize(\"cmap\", [\"tab10\", \"jet\"])\n@pytest.mark.parametrize(\"show_members\", [True, False])\n@pytest.mark.parametrize(\"variable\", [\"tos\", None])\ndef test_PerfectModelEnsemble_plot(\n PM_ds_initialized_1d, PM_ds_control_1d, variable, show_members, cmap\n):\n \"\"\"Test PredictionEnsemble.plot().\"\"\"\n pm = PerfectModelEnsemble(PM_ds_initialized_1d)\n kws = {\"cmap\": cmap, \"show_members\": show_members, \"variable\": variable}\n pm.plot(**kws)\n pm = pm.add_control(PM_ds_control_1d)\n pm.plot(**kws)\n pm = pm.generate_uninitialized()\n pm.plot(**kws)\n\n\ndef test_PerfectModelEnsemble_plot_fails_3d(PM_ds_initialized_3d):\n \"\"\"Test PredictionEnsemble.plot().\"\"\"\n pm = PerfectModelEnsemble(PM_ds_initialized_3d)\n with pytest.raises(DimensionError) as excinfo:\n pm.plot()\n assert \"does not allow dimensions other\" in str(excinfo.value)\n\n\n@pytest.mark.parametrize(\"x\", [\"time\", \"init\"])\n@pytest.mark.parametrize(\"show_members\", [True, False])\n@pytest.mark.parametrize(\"variable\", [\"SST\", None])\ndef test_PredictionEnsemble_plot(\n hind_ds_initialized_1d,\n hist_ds_uninitialized_1d,\n reconstruction_ds_1d,\n observations_ds_1d,\n variable,\n show_members,\n x,\n):\n \"\"\"Test PredictionEnsemble.plot().\"\"\"\n he = HindcastEnsemble(hind_ds_initialized_1d)\n kws = {\"show_members\": show_members, \"variable\": variable, \"x\": x}\n he.plot(**kws)\n he = he.add_uninitialized(hist_ds_uninitialized_1d)\n he.plot(**kws)\n he = he.add_observations(reconstruction_ds_1d)\n he.plot(**kws)\n he = he.add_observations(observations_ds_1d)\n he.plot(**kws)\n\n if x == \"time\":\n pm = PerfectModelEnsemble(hind_ds_initialized_1d)\n pm.plot(**kws)\n pm = pm.add_control(hist_ds_uninitialized_1d.isel(member=0, drop=True))\n pm.plot(**kws)\n", "sub_path": "climpred/tests/test_graphics.py", "file_name": "test_graphics.py", "file_ext": "py", "file_size_in_byte": 3720, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "climpred.bootstrap.bootstrap_perfect_model", "line_number": 18, "usage_type": "call"}, {"api_name": "climpred.graphics.plot_bootstrapped_skill_over_leadyear", "line_number": 25, "usage_type": "call"}, {"api_name": "climpred.bootstrap.bootstrap_perfect_model", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 43, "usage_type": "attribute"}, {"api_name": "climpred.graphics.plot_bootstrapped_skill_over_leadyear", "line_number": 44, "usage_type": "call"}, {"api_name": "climpred.bootstrap.bootstrap_perfect_model", "line_number": 54, "usage_type": "call"}, {"api_name": "climpred.graphics.plot_bootstrapped_skill_over_leadyear", "line_number": 62, "usage_type": "call"}, {"api_name": "climpred.PerfectModelEnsemble", "line_number": 73, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 66, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 66, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 67, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 67, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 68, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 68, "usage_type": "attribute"}, {"api_name": "climpred.PerfectModelEnsemble", "line_number": 84, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 85, "usage_type": "call"}, {"api_name": "climpred.checks.DimensionError", "line_number": 85, "usage_type": "argument"}, {"api_name": "climpred.HindcastEnsemble", "line_number": 103, "usage_type": "call"}, {"api_name": "climpred.PerfectModelEnsemble", "line_number": 114, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 90, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 90, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 91, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 91, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 92, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 92, "usage_type": "attribute"}]} +{"seq_id": "499106977", "text": "from moviepy.editor import VideoFileClip\nfrom IPython.display import HTML\n\nimport numpy as np\nimport cv2\nimport glob\nimport pickle\nimport matplotlib.pyplot as plt\nfrom tracker import tracker\n\n#Load previous camera calibration parameter\ndist_pickle = pickle.load( open(\"./camera_cal/calibration_pickle.p\",\"rb\"))\nmtx = dist_pickle[\"mtx\"]\ndist = dist_pickle[\"dist\"]\n\ndef window_mask(width, height, img_ref, center,level):\n output = np.zeros_like(img_ref)\n output[int(img_ref.shape[0]-(level+1)*height):int(img_ref.shape[0]-level*height),max(0,int(center-width/2)):min(int(center+width/2),img_ref.shape[1])] = 1\n return output\n\ndef abs_sobel_thresh(img, orient='x', sobel_kernel=3, thresh=(0,255)):\n # Convert to grayscale\n gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n # Apply x or y gradient with the OpenCV Sobel() function\n # and take the absolute value\n if orient == 'x':\n abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0))\n if orient == 'y':\n abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1))\n # Rescale back to 8 bit integer\n scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))\n # Create a copy and apply the threshold\n binary_output = np.zeros_like(scaled_sobel)\n # Here I'm using inclusive (>=, <=) thresholds, but exclusive is ok too\n binary_output[(scaled_sobel >= thresh[0]) & (scaled_sobel <= thresh[1])] = 1\n \n return binary_output\n\ndef color_threshold(image, sthresh=(0,255), vthresh=(0,255)):\n\thls = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)\n\ts_channel = hls[:,:,2]\n\ts_binary = np.zeros_like(s_channel)\n\ts_binary[(s_channel >= sthresh[0])&(s_channel <= sthresh[1])] = 1\n\t\n\thsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)\n\tv_channel = hls[:,:,2]\n\tv_binary = np.zeros_like(v_channel)\n\tv_binary[(v_channel >= vthresh[0])&(v_channel <= vthresh[1])] = 1\n\t\n\toutput = np.zeros_like(s_channel)\n\toutput[(s_binary == 1) & (v_binary == 1)] = 1\n\treturn output\n\t\n\t\nimages = glob.glob('./test_images/test*.jpg')\n\ndef process_image(img):\n\t#read and undistor images\n\t\n\timg = cv2.undistort(img,mtx,dist,None,mtx)\n\t\n\t\n\t# Process image and generate binary pixel of interests\n\tpreprocessImage = np.zeros_like(img[:,:,0])\n\tgradx = abs_sobel_thresh(img, orient='x', thresh=(12,255))\n\tgrady = abs_sobel_thresh(img, orient='y', thresh=(25,255))\n\tc_binary = color_threshold(img, sthresh=(100,255),vthresh=(50,255))\n\tpreprocessImage[((gradx == 1)&(grady == 1)|(c_binary == 1))] = 255\n\t\n\t\n\t\n\t# Defining perspective transformation area\n\theight = img.shape[0]\n\twidth = img.shape[1]\n\timg_size = (width, height)\n\n\t\n\ttop_left_src = (563, 470)\n\tbottom_left_src = (220, 700)\n\ttop_left_dst = (300,300)\n\tbottom_left_dst = (300,720)\n\t\n\tsrc = np.float32([[top_left_src[0],top_left_src[1]], \n\t[bottom_left_src[0],bottom_left_src[1]],\n\t[width - bottom_left_src[0],bottom_left_src[1]],\n\t[width - top_left_src[0],top_left_src[1]]]) \n\t\n\tdst = np.float32([[top_left_dst[0],top_left_dst[1]], \n\t[bottom_left_dst[0],bottom_left_dst[1]],\n\t[width - bottom_left_dst[0],bottom_left_dst[1]],\n\t[width - top_left_dst[0],top_left_dst[1]]])\n\t\n\t# Start applying perspective tranform\n\tM = cv2.getPerspectiveTransform(src,dst)\n\tMinv = cv2.getPerspectiveTransform(dst,src)\n\twarped = cv2.warpPerspective(preprocessImage,M,img_size,flags=cv2.INTER_LINEAR)\n\t\n\twarped_color = cv2.warpPerspective(img,M,img_size,flags=cv2.INTER_LINEAR)\n\t\n\t\t\n\t\n\t#Define the box size for fitting the curvature line\n\twindow_width = 25\n\twindow_height = 80\n\t\n\t#set up the overeall class to do the tracking\n\tcurve_centers = tracker(Mywindow_width = window_width, Mywindow_height = window_height, Mymargin =25, My_ym = 10/720, My_xm = 4/384, Mysmooth_factor=15)\n\t\n\twindow_centroids = curve_centers.find_window_centroids(warped)\n\t\n\t# Points used to draw all the left and right window\n\tl_points = np.zeros_like(warped)\n\tr_points = np.zeros_like(warped)\n\t\n\t# Points used to find the left and right lanes\n\tleftx = []\n\trightx = []\n\t\n\t# Go through each level and draw the windows \t\n\tfor level in range(0,len(window_centroids)):\n\t\t# Window_mask is a function to draw window areas\n\t\t# Add center value found in frame to the list of lane points per left,right\n\t\t\n\t\tleftx.append(window_centroids[level][0])\n\t\trightx.append(window_centroids[level][1])\n\t\t\n\t\tl_mask = window_mask(window_width,window_height,warped,window_centroids[level][0],level)\n\t\tr_mask = window_mask(window_width,window_height,warped,window_centroids[level][1],level)\n\t\t\n\t\t# Add graphic points from window mask here to total pixels found \n\t\tl_points[(l_points == 255) | ((l_mask == 1) ) ] = 255\n\t\tr_points[(r_points == 255) | ((r_mask == 1) ) ] = 255\n\t\n\t\n\t# Draw the results\n\t#template = np.array(r_points+l_points,np.uint8) # add both left and right window pixels together\n\t#zero_channel = np.zeros_like(template) # create a zero color channel\n\t#template = np.array(cv2.merge((zero_channel,template,zero_channel)),np.uint8) # make window pixels green\n\t#warpage = np.array(cv2.merge((warped,warped,warped)),np.uint8) # making the original road pixels 3 color channels\n\t#output = cv2.addWeighted(warpage, 1, template, 0.5, 0.0) # overlay the orignal road image with window results\n\t\n\t\n\t\n\t#Fit the lane boundaries to the left,right and center positions found\n\tyvals = range(0, warped.shape[0])\n\t\n\tres_yvals = np.arange(warped.shape[0]-(window_height/2),0,-window_height)\n\t\n\tleft_fit = np.polyfit(res_yvals, leftx, 2)\n\tleft_fitx = left_fit[0]*yvals*yvals + left_fit[1]*yvals + left_fit[2]\n\tleft_fitx = np.array(left_fitx,np.int32)\n\t\n\tright_fit = np.polyfit(res_yvals, rightx, 2)\n\tright_fitx = right_fit[0]*yvals*yvals + right_fit[1]*yvals + right_fit[2]\n\tright_fitx = np.array(right_fitx,np.int32)\n\t\n\tleft_lane = np.array(list(zip(np.concatenate((left_fitx-window_width/2, left_fitx[::-1]+window_width/2), axis=0),np.concatenate((yvals,yvals[::-1]),axis=0))),np.int32)\n\tright_lane = np.array(list(zip(np.concatenate((right_fitx-window_width/2, right_fitx[::-1]+window_width/2), axis=0),np.concatenate((yvals,yvals[::-1]),axis=0))),np.int32)\n\tinner_lane = np.array(list(zip(np.concatenate((left_fitx+window_width/2, right_fitx[::-1]-window_width/2), axis=0),np.concatenate((yvals,yvals[::-1]),axis=0))),np.int32)\n\t\n\troad = np.zeros_like(img)\n\troad_bkg = np.zeros_like(img)\n\tcv2.fillPoly(road,[left_lane],color=[255,0,0])\n\tcv2.fillPoly(road,[right_lane],color=[0,0,255])\n\tcv2.fillPoly(road,[inner_lane],color=[0,100,0])\n\t#cv2.fillPoly(road_bkg,[left_lane],color=[255,255,255])\n\t#cv2.fillPoly(road_bkg,[right_lane],color=[255,255,255])\n\t\n\troad_warped = cv2.warpPerspective(road,Minv,img_size,flags=cv2.INTER_LINEAR)\n\troad_warped_bkg = cv2.warpPerspective(road_bkg,Minv,img_size,flags=cv2.INTER_LINEAR)\n\t\n\tbase = cv2.addWeighted(img, 1.0, road_warped_bkg, -1.0, 0.0)\n\tfinal_line = cv2.addWeighted(base, 1.0, road_warped, 0.7, 0.0)\n\t\n\tym_per_pix = curve_centers.ym_per_pix\n\txm_per_pix = curve_centers.xm_per_pix\n\tcurve_fit_cr = np.polyfit(np.array(res_yvals,np.float32)*ym_per_pix, np.array(leftx,np.float32)*xm_per_pix,2)\n\tcurverad = ((1 + (2*curve_fit_cr[0]*yvals[-1]*ym_per_pix + curve_fit_cr[1])**2)**1.5) / np.absolute(2*curve_fit_cr[0])\n\t\n\t#Calculate the offset of the car on the road\n\tcamera_center = (left_fitx[-1]+right_fitx[-1])/2\n\tcenter_diff = (camera_center-warped.shape[1]/2)*xm_per_pix\n\tside_pos = 'left'\n\tif center_diff <= 0:\n\t\tside_pos = 'right'\n\t\n\tcv2.putText(final_line, 'Radius of Curvature = '+str(round(curverad,3))+'(m)', (50, 70), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (255, 255, 255), 3)\n\tcv2.putText(final_line, 'Vehicle is '+str(abs(round(center_diff,3)))+'m '+side_pos+' of center', (50, 140), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (255, 255, 255), 3)\n\t\n\treturn final_line\n\t\nOutput_video = 'output1_tracked.mp4'\nInput_video = 'project_video.mp4'\n\nclip1 = VideoFileClip(Input_video)\nvideo_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!\nvideo_clip.write_videofile(Output_video, audio=False)\t", "sub_path": "video_gen.py", "file_name": "video_gen.py", "file_ext": "py", "file_size_in_byte": 7891, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "pickle.load", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2GRAY", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.absolute", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.Sobel", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.CV_64F", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.absolute", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.Sobel", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.CV_64F", "line_number": 29, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2HLS", "line_number": 40, "usage_type": "attribute"}, {"api_name": "numpy.zeros_like", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2HSV", "line_number": 45, "usage_type": "attribute"}, {"api_name": "numpy.zeros_like", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 50, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.undistort", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 88, "usage_type": "call"}, {"api_name": "cv2.getPerspectiveTransform", "line_number": 94, "usage_type": "call"}, {"api_name": "cv2.getPerspectiveTransform", "line_number": 95, "usage_type": "call"}, {"api_name": "cv2.warpPerspective", "line_number": 96, "usage_type": "call"}, {"api_name": "cv2.INTER_LINEAR", "line_number": 96, "usage_type": "attribute"}, {"api_name": "cv2.warpPerspective", "line_number": 98, "usage_type": "call"}, {"api_name": "cv2.INTER_LINEAR", "line_number": 98, "usage_type": "attribute"}, {"api_name": "tracker.tracker", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.polyfit", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 151, "usage_type": "attribute"}, {"api_name": "numpy.polyfit", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 155, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 157, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 158, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 159, "usage_type": "attribute"}, {"api_name": "numpy.zeros_like", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 162, "usage_type": "call"}, {"api_name": "cv2.fillPoly", "line_number": 163, "usage_type": "call"}, {"api_name": "cv2.fillPoly", "line_number": 164, "usage_type": "call"}, {"api_name": "cv2.fillPoly", "line_number": 165, "usage_type": "call"}, {"api_name": "cv2.warpPerspective", "line_number": 169, "usage_type": "call"}, {"api_name": "cv2.INTER_LINEAR", "line_number": 169, "usage_type": "attribute"}, {"api_name": "cv2.warpPerspective", "line_number": 170, "usage_type": "call"}, {"api_name": "cv2.INTER_LINEAR", "line_number": 170, "usage_type": "attribute"}, {"api_name": "cv2.addWeighted", "line_number": 172, "usage_type": "call"}, {"api_name": "cv2.addWeighted", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.polyfit", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 177, "usage_type": "attribute"}, {"api_name": "numpy.absolute", "line_number": 178, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 187, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 187, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 188, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 188, "usage_type": "attribute"}, {"api_name": "moviepy.editor.VideoFileClip", "line_number": 195, "usage_type": "call"}]} +{"seq_id": "98880268", "text": "import logging\nimport ddl\nimport dml\nimport kronos\nimport facade_abc\nimport os\n\n\nclass TasksFacade(facade_abc.AbcFacade):\n __rows_in_table = 0\n\n def __init__(self, database):\n self.db = database\n self.ddl = ddl.DataDefinitionLanguage(database)\n self.dml = dml.DataManipulationLanguage(database)\n self.table_name = \"tasks\"\n TasksFacade.__rows_in_table = self.count_rows()\n try:\n self.schema = ddl.DataDefinitionLanguage.parse_json(\n os.path.join(\n os.path.dirname(__file__),\n \"table_schemas/\" + self.table_name + \".json\",\n )\n )\n except ValueError:\n logging.error(\"Unable to parse schema\")\n\n def count_rows(self):\n return len(self.get_rows())\n\n def get_rows(self):\n return self.dml.select_star_sql(self.table_name)\n\n def delete_history(self):\n self.ddl.drop_table(self.table_name)\n\n def disconnect(self):\n self.db.disconnect()\n\n def get_ids(self):\n ids = []\n for row in self.get_rows():\n ids.append(row[0])\n return ids\n\n def get_last_workday(self):\n rows = []\n for row in self.get_rows():\n if kronos.get_day_of_week(kronos.get_date_time()) == \"Monday\":\n if kronos.is_previous_friday(row[3]):\n rows.append(row)\n if kronos.is_yesterday(row[3]):\n rows.append(row)\n return rows\n\n def complete_task(self, row_id):\n now = kronos.get_date_time_as_string()\n self.db.get_cursor().execute(\n f\"UPDATE {self.table_name} SET is_complete = 'true', date_complete = '{now}' WHERE id = {row_id}\"\n )\n self.db.get_connection().commit()\n\n def void_task(self, row_id):\n now = kronos.get_date_time_as_string()\n self.db.get_cursor().execute(\n f\"UPDATE {self.table_name} SET is_void = 'true', date_complete = '{now}' WHERE id = {row_id}\"\n )\n self.db.get_connection().commit()\n\n def get_overdue_tasks(self):\n rows = []\n for row in self.get_rows():\n date_set = row[3]\n days_to_complete = row[5]\n is_complete = row[6]\n if is_complete == \"false\":\n if kronos.is_overdue(date_set, days_to_complete):\n rows.append(row)\n return rows\n\n def insert_task(self, task, days_to_complete):\n TasksFacade.increment_row_count()\n self.db.get_cursor().execute(\n \"INSERT INTO {} ({}, {}, {}, {}, {}, {}, {}, {}) VALUES (?, ?, ?, ?, ?, ?, ?, ?)\".format(\n self.table_name, *self.schema\n ),\n (\n TasksFacade.__rows_in_table,\n \"TASK\",\n task,\n kronos.get_date_time_as_string(),\n \"TBD****************\",\n days_to_complete,\n \"false\",\n \"false\",\n ),\n )\n self.db.get_connection().commit()\n\n def check_if_not_completed(self, task_id):\n cursor = self.db.get_cursor()\n cursor.execute(\n f\"SELECT is_complete from {self.table_name} WHERE id = {task_id}\"\n )\n record = cursor.fetchall()\n is_complete = record[0][0]\n\n if is_complete == \"true\":\n return 0\n else:\n return 1\n def check_if_not_voided(self, task_id):\n cursor = self.db.get_cursor()\n cursor.execute(\n f\"SELECT is_void from {self.table_name} WHERE id = {task_id}\"\n )\n record = cursor.fetchall()\n is_complete = record[0][0]\n\n if is_complete == \"true\":\n return 0\n else:\n return 1\n\n @classmethod\n def reset_row_count(cls):\n TasksFacade.__rows_in_table = 0\n\n @classmethod\n def increment_row_count(cls):\n TasksFacade.__rows_in_table = TasksFacade.__rows_in_table + 1\n", "sub_path": "time_management/facade_tasks.py", "file_name": "facade_tasks.py", "file_ext": "py", "file_size_in_byte": 3971, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "facade_abc.AbcFacade", "line_number": 9, "usage_type": "attribute"}, {"api_name": "ddl.DataDefinitionLanguage", "line_number": 14, "usage_type": "call"}, {"api_name": "dml.DataManipulationLanguage", "line_number": 15, "usage_type": "call"}, {"api_name": "ddl.DataDefinitionLanguage.parse_json", "line_number": 19, "usage_type": "call"}, {"api_name": "ddl.DataDefinitionLanguage", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 26, "usage_type": "call"}, {"api_name": "kronos.get_day_of_week", "line_number": 49, "usage_type": "call"}, {"api_name": "kronos.get_date_time", "line_number": 49, "usage_type": "call"}, {"api_name": "kronos.is_previous_friday", "line_number": 50, "usage_type": "call"}, {"api_name": "kronos.is_yesterday", "line_number": 52, "usage_type": "call"}, {"api_name": "kronos.get_date_time_as_string", "line_number": 57, "usage_type": "call"}, {"api_name": "kronos.get_date_time_as_string", "line_number": 64, "usage_type": "call"}, {"api_name": "kronos.is_overdue", "line_number": 77, "usage_type": "call"}, {"api_name": "kronos.get_date_time_as_string", "line_number": 91, "usage_type": "call"}]} +{"seq_id": "225665352", "text": "\nfrom django.shortcuts import render,get_object_or_404\nfrom django.views.generic import TemplateView, ListView # Import TemplateView.\nfrom django.http import HttpResponseRedirect\nfrom django.template import RequestContext\nfrom django.urls import reverse\nfrom django.shortcuts import render_to_response\n\nfrom votings.models import *\nfrom votings.forms import *\n#import cv2\n#from .model import bed\n#used for saving uploaded image to static folder.\nimport os\n#importing object detection code\nfrom votings.object import *\nfrom votings.processing import *\nfrom django.http import HttpResponse\nfrom .models import Category, Product \nfrom cart.forms import CartAddProductForm\n#from .model import bed\nimport json\n# to find substring in description of product to categorize by style\nimport re\nimport ntpath\n\n#shows all products by style and by category\ndef product_list(request, category_slug=None):\n \n print(\"Running...................product_list\") #20 dots\n currentRoom=request.session.get('currentRoom')\n currentStyle=request.session.get('currentStyle')\n roombreadth = request.session.get('roombreadth')\n roomlength = request.session.get('roomlength')\n category = None\n categories = Category.objects.all()\n \n if currentStyle!=None:\n #for All selected in category\n print(\"currentStyle not None\")\n products = Product.objects.filter(available=True,description__icontains=currentStyle)\n \n print(currentStyle,\" \",currentRoom)\n if category_slug:\n print(\"category_slug: \",category_slug)\n category = get_object_or_404(Category, slug=category_slug)\n #BEAUTIFUL\n products = Product.objects.filter(category=category,description__icontains=currentStyle)\n print(type(products))\n if category_slug==\"nostyle\":\n products = Product.objects.filter(available=True)\n \n\n else:\n print(\"currentStyle is None\")\n products = Product.objects.filter(available=True)\n \n print(currentStyle,\" \",currentRoom)\n if category_slug:\n print(\"category_slug: \",category_slug)\n category = get_object_or_404(Category, slug=category_slug)\n products = Product.objects.filter(category=category)\n print(type(products))\n\n user=request.user.username\n #extracting session variable of current logged in user.\n context = {\n 'category': category,\n 'categories': categories,\n 'products': products,\n 'user':user\n }\n\n return render(request, 'polls/product/list.html', context)\n\n\n\n#shows each product in detail\ndef product_detail(request, id, slug):\n print(\"I am in product_detail\")\n product = get_object_or_404(Product, id=id, slug=slug, available=True)\n cart_product_form = CartAddProductForm()\n user=request.user.username\n context = {\n 'product': product,\n 'cart_product_form': cart_product_form,\n 'user':user\n }\n return render(request, 'polls/product/detail.html', context)\n\nclass IndexPageView(TemplateView):\n\n template_name = \"polls/index.html\"\n\n\nclass CatPageView(TemplateView):\n template_name = \"polls/cat.html\"\n#To show all the uploaded image rooms\ndef AllImages(request):\n print(\"I am inside all images\")\n customer_id=request.user.id\n customer_name=request.user.username\n print(request.user.username)\n #print(request.user.email)\n #print(request.user.id)\n\n #g = get_object_or_404(Document,pk=customer_id)\n #i = get_object_or_404(Document, pk=1)\n i = Document.objects.all().order_by('-id')\n return render(request, 'polls/product/myimages.html', {'imagedata' :i })\n\n\ndef AllFurtherImages(request):\n print(\"I am inside all images\")\n customer_id=request.user.id\n customer_name=request.user.username\n print(request.user.username)\n #print(request.user.email)\n #print(request.user.id)\n\n #g = get_object_or_404(Document,pk=customer_id)\n #i = get_object_or_404(Document, pk=1)\n i = Document.objects.all().order_by('-id')\n return render(request, 'polls/product/yourimages.html', {'imagedata' :i })\n\ndef uploadRoom(request):\n\n # Handle file upload\n print(\"I am inside uploadRoom\")\n form = DocumentForm() # A empty, unbound form.\n print(\"Created a Document Form\")\n if(request.method == 'POST'):\n\n print(\"POST request received\")\n form = DocumentForm(request.POST, request.FILES)\n if form.is_valid():\n print(\"Form is valid\")\n # obtain session id of user.\n #customer_id=request.session.get('customer_id')\n customer_id=request.user.id\n customer_name=request.user.username\n # create a model object and save the file to database.\n brvalue = form.cleaned_data.get(\"breadth\")\n levalue = form.cleaned_data.get(\"length\")\n \n roomImage = Document(docfile = request.FILES['docfile'], breadth = brvalue, length=levalue, customer_id=customer_id)\n print(\"Room Image Path is: \", roomImage.docfile.path)\n roomImage.save()\n # change the location of file to a more secure location.\n print(\"Name of Docfile\",roomImage.docfile.path)\n print(\"Name of File \" , request.FILES['docfile'].name)\n print(\"BREADTH\", roomImage.breadth)\n oldName=roomImage.docfile.path\n newName=os.getcwd()+\"/static/users/\"+ roomImage.docfile.name\n os.rename(oldName,newName)\n \n # load the image of room and detect the objects.\n processRoom=ProcessRoomImage()\n detected_objs=processRoom.DetectObjects(newName)\n print(detected_objs)\n \n # extract the objects from room.\n objects=processRoom.ObjectExtraction(detected_objs,newName)\n\n # return style of the objects.\n styles=processRoom.LoadStylizer(objects)\n print(styles)\n # styles is a list of tuples\n # so we sort based on second value in each tuple so key =x[1]\n # and we want it to e in descending order\n styles=sorted(styles,key=lambda x: x[1], reverse=True)\n print(styles)\n # saving the style of user.\n style1=styles[0][0]\n style2=styles[1][0]\n \n roomImage.style1=processRoom.getStyleName(style1)\n roomImage.style2=processRoom.getStyleName(style2) \n roomImage.save()\n\n #save the room image as the current session image.\n \n file=roomImage.docfile.name \n br = roomImage.breadth\n le = roomImage.length\n request.session['roomlength']=le\n request.session['roombreadth']=br\n request.session['currentRoom']=file\n request.session['currentStyle']=roomImage.style1\n #used for debug. \n #print(file)'''\n print(\"New Path is: \"+newName)\n document = roomImage\n print(document)\n \n #=========== Send analysed images to template ===============\n #file=ntpath.basename(newName) \n li=sorted(os.listdir(os.getcwd()+\"/static/Analysis/\"))\n c1=li[0]\n c2=li[1]\n c3=li[2]\n c4=li[3]\n c5=li[4]\n c6=li[5]\n c7=li[6]\n c8=li[7]\n c9=li[8]\n c10=li[9]\n c11=li[10]\n c12=li[11]\n c13=li[12]\n c14=li[13]\n c15=li[14]\n c16=li[15]\n c17=li[16]\n c18=li[17]\n c19=li[18]\n c20=li[19]\n c21=li[20]\n c22=li[21]\n c23=li[22]\n c24=li[23]\n c25=li[24]\n c26=li[25]\n #l2=sorted(os.path(li))\n print(li)\n # Redirect to the document list after POST.\n #return HttpResponseRedirect(reverse('list1')).\n return render(request, 'polls/uploadRoom.html', {'document': document, \n 'form': form, 'list':li, 'file':file,'c1':c1,'c2':c2,'c3':c3,'c4':c4,'c5':c5,'c6':c6,'c7':c7,'c8':c8,'c9':c9,'c10':c10,'c11':c11,'c12':c12,'c13':c13,'c14':c14,'c15':c15,'c16':c16,'c17':c17,\n 'c18':c18,'c19':c19,'c20':c20,'c21':c21,'c22':c22,'c23':c23,'c24':c24,'c25':c25,'c26':c26},)\n # Load documents for the list page.\n\n \n #call object detector here\n #print(\"When inside list1 customer_id is: \", customer_id)\n #print(\"When inside list1: \", password)\n\n # print(documents)\n # Render list page with the documents and the form\n print(\"POST NOT received\")\n return render(request, 'polls/uploadRoom.html', { 'form': form},)\n\n\n'''#from django.shortcuts import render\n#from django.template.loader import get_template\n# Create your views here.\n#from django.http import HttpResponse\n\n\n#def index(request):\n # return HttpResponse(\"Hello, world. You're at the polls index.\")\n\n#def basefunc(request):\n#\tb = get_template('polls/login.html')\n#\thtml = b.render({'shubhangi':'hi'})\n#\treturn HttpResponse(html)\n #return HttpResponse(\"Hello, world. You're at the polls index.\")'''\n\n\n", "sub_path": "first_site/votings/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 9217, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "models.Category.objects.all", "line_number": 36, "usage_type": "call"}, {"api_name": "models.Category.objects", "line_number": 36, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 36, "usage_type": "name"}, {"api_name": "models.Product.objects.filter", "line_number": 41, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 41, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 41, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 46, "usage_type": "call"}, {"api_name": "models.Category", "line_number": 46, "usage_type": "argument"}, {"api_name": "models.Product.objects.filter", "line_number": 48, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 48, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 48, "usage_type": "name"}, {"api_name": "models.Product.objects.filter", "line_number": 51, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 51, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 51, "usage_type": "name"}, {"api_name": "models.Product.objects.filter", "line_number": 56, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 56, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 56, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 61, "usage_type": "call"}, {"api_name": "models.Category", "line_number": 61, "usage_type": "argument"}, {"api_name": "models.Product.objects.filter", "line_number": 62, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 62, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 62, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 74, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 81, "usage_type": "call"}, {"api_name": "models.Product", "line_number": 81, "usage_type": "argument"}, {"api_name": "cart.forms.CartAddProductForm", "line_number": 82, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 89, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView", "line_number": 91, "usage_type": "name"}, {"api_name": "django.views.generic.TemplateView", "line_number": 96, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 110, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 124, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 154, "usage_type": "call"}, {"api_name": "os.rename", "line_number": 155, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 198, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 198, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 229, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 242, "usage_type": "call"}]} +{"seq_id": "314378586", "text": "import json\nfrom collections import OrderedDict\nfrom django.http import JsonResponse, HttpResponse\nfrom django.shortcuts import render\n\nfrom django.contrib.auth.decorators import login_required\n\nfrom django.views.decorators.csrf import csrf_exempt\nfrom tworaven_apps.utils.view_helper import \\\n (get_request_body,\n get_json_error,\n get_json_success)\nfrom tworaven_apps.call_captures.models import ServiceCallEntry\nfrom tworaven_apps.utils.view_helper import \\\n (get_session_key, get_authenticated_user)\nfrom tworaven_apps.ta2_interfaces.models import \\\n (StoredRequest, StoredResponse)\nfrom tworaven_apps.ta2_interfaces.search_history_util import SearchHistoryUtil\nfrom tworaven_apps.ta2_interfaces.static_vals import \\\n (SEARCH_SOLUTIONS,\n GET_SEARCH_SOLUTIONS_RESULTS)\n\n\n@login_required\ndef view_clear_grpc_stored_history(request):\n \"\"\"For develop, clear GPRC stored history for a User\"\"\"\n user_info = get_authenticated_user(request)\n if not user_info.success:\n return JsonResponse(get_json_error(user_info.err_msg))\n\n clear_info = SearchHistoryUtil.clear_grpc_stored_history(user_info.result_obj)\n\n if not clear_info.success:\n return HttpResponse(clear_info.err_msg)\n\n return HttpResponse('
    '.join(clear_info.result_obj))\n\n\n@login_required\ndef view_grpc_search_history_json_no_id(request):\n \"\"\"Pick an existing search history, if it exists\"\"\"\n\n resp = SearchHistoryUtil.get_last_search_solutions_call()\n\n if not resp:\n err_info = get_json_error('No search_id was found')\n return JsonResponse(get_json_error(err_info))\n\n return view_grpc_search_history_json(request, resp.search_id)\n\n\n@login_required\ndef view_grpc_search_history_json(request, search_id):\n \"\"\"View stored request/responses based on search_id\"\"\"\n if not search_id:\n err_info = get_json_error('No search_id was found')\n return JsonResponse(get_json_error(err_info))\n\n search_history_util = SearchHistoryUtil(search_id=search_id)\n\n if search_history_util.has_error():\n err_info = f'Error found: {search_history_util.get_err_msg()}'\n #print(f'Error found: f{search_history_util.get_error()}')\n return JsonResponse(get_json_error(err_info))\n\n info_dict = dict(search_id=search_id,\n json_history=search_history_util.get_finalized_history())\n\n user_info = get_json_success('History found', data=info_dict)\n return JsonResponse(user_info)\n\n\n@login_required\ndef view_grpc_stored_history_no_id(request):\n \"\"\"Pick an existing search history, if it exists\"\"\"\n\n resp = SearchHistoryUtil.get_last_search_solutions_call()\n\n resp_id = resp.search_id if resp else None\n #if not resp:\n # err_info = get_json_error('No search_id was found')\n # return JsonResponse(get_json_error(err_info))\n\n return view_grpc_stored_history(request, resp_id)\n\n\ndef view_grpc_stored_history(request, search_id):\n \"\"\"View stored request/responses based on search_id\"\"\"\n\n info_dict = dict(search_id=search_id)\n\n if not search_id:\n info_dict['ERROR_MSG'] = 'No search_id was found'\n return render(request,\n 'grpc/view_grpc_stored_history.html',\n info_dict)\n\n search_history_util = SearchHistoryUtil(search_id=search_id)\n\n if search_history_util.has_error():\n err_msg = f'Error found: {search_history_util.get_err_msg()}'\n info_dict['ERROR_MSG'] = err_msg\n\n return render(request,\n 'grpc/view_grpc_stored_history.html',\n info_dict)\n\n info_dict = dict(search_id=search_id,\n json_history=search_history_util.get_finalized_history())\n\n return render(request,\n 'grpc/view_grpc_stored_history.html',\n info_dict)\n\n\n@csrf_exempt\ndef view_stored_request(request, hash_id):\n \"\"\"Return a StoredRequest object\"\"\"\n user_info = get_authenticated_user(request)\n #if not user_info.success:\n # return JsonResponse(get_json_error(user_info.err_msg))\n #user = user_info.result_obj\n\n try:\n req = StoredRequest.objects.get(\\\n hash_id=hash_id)\n #user=user)\n except StoredRequest.DoesNotExist:\n user_msg = 'StoredRequest not found.'\n return JsonResponse(get_json_error(user_msg))\n\n if 'pretty' in request.GET:\n json_str = '
    %s
    ' % \\\n                   (json.dumps(req.as_dict(), indent=4))\n        return HttpResponse(json_str)\n\n    resp_info = get_json_success('ok',\n                                 data=req.as_dict())\n    return JsonResponse(resp_info)\n\n\n\n@csrf_exempt\ndef view_stored_response(request, hash_id):\n    \"\"\"Return a StoredResponse object\"\"\"\n    user_info = get_authenticated_user(request)\n    #if not user_info.success:\n    #    return JsonResponse(get_json_error(user_info.err_msg))\n    #user = user_info.result_obj\n\n    try:\n        resp = StoredResponse.objects.get(\\\n                                hash_id=hash_id,)\n                                # stored_request__user=user)\n    except StoredResponse.DoesNotExist:\n        user_msg = 'StoredResponse not found.'\n        return JsonResponse(get_json_error(user_msg))\n\n    StoredResponse.mark_as_read(resp)\n\n    if 'pretty' in request.GET:\n        json_str = '
    %s
    ' % \\\n                   (json.dumps(resp.as_dict(), indent=4))\n        return HttpResponse(json_str)\n\n    resp_info = get_json_success('ok',\n                                 data=resp.as_dict())\n    return JsonResponse(resp_info)\n", "sub_path": "tworaven_apps/ta2_interfaces/views_saved_requests.py", "file_name": "views_saved_requests.py", "file_ext": "py", "file_size_in_byte": 5597, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "tworaven_apps.utils.view_helper.get_authenticated_user", "line_number": 27, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 29, "usage_type": "call"}, {"api_name": "tworaven_apps.utils.view_helper.get_json_error", "line_number": 29, "usage_type": "call"}, {"api_name": "tworaven_apps.ta2_interfaces.search_history_util.SearchHistoryUtil.clear_grpc_stored_history", "line_number": 31, "usage_type": "call"}, {"api_name": "tworaven_apps.ta2_interfaces.search_history_util.SearchHistoryUtil", "line_number": 31, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 34, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 36, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 24, "usage_type": "name"}, {"api_name": "tworaven_apps.ta2_interfaces.search_history_util.SearchHistoryUtil.get_last_search_solutions_call", "line_number": 43, "usage_type": "call"}, {"api_name": "tworaven_apps.ta2_interfaces.search_history_util.SearchHistoryUtil", "line_number": 43, "usage_type": "name"}, {"api_name": "tworaven_apps.utils.view_helper.get_json_error", "line_number": 46, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 47, "usage_type": "call"}, {"api_name": "tworaven_apps.utils.view_helper.get_json_error", "line_number": 47, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 39, "usage_type": "name"}, {"api_name": "tworaven_apps.utils.view_helper.get_json_error", "line_number": 56, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 57, "usage_type": "call"}, {"api_name": "tworaven_apps.utils.view_helper.get_json_error", "line_number": 57, "usage_type": "call"}, {"api_name": "tworaven_apps.ta2_interfaces.search_history_util.SearchHistoryUtil", "line_number": 59, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 64, "usage_type": "call"}, {"api_name": "tworaven_apps.utils.view_helper.get_json_error", "line_number": 64, "usage_type": "call"}, {"api_name": "tworaven_apps.utils.view_helper.get_json_success", "line_number": 69, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 70, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 52, "usage_type": "name"}, {"api_name": "tworaven_apps.ta2_interfaces.search_history_util.SearchHistoryUtil.get_last_search_solutions_call", "line_number": 77, "usage_type": "call"}, {"api_name": "tworaven_apps.ta2_interfaces.search_history_util.SearchHistoryUtil", "line_number": 77, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 73, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 94, "usage_type": "call"}, {"api_name": "tworaven_apps.ta2_interfaces.search_history_util.SearchHistoryUtil", "line_number": 98, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 104, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 111, "usage_type": "call"}, {"api_name": "tworaven_apps.utils.view_helper.get_authenticated_user", "line_number": 119, "usage_type": "call"}, {"api_name": "tworaven_apps.ta2_interfaces.models.StoredRequest.objects.get", "line_number": 125, "usage_type": "call"}, {"api_name": "tworaven_apps.ta2_interfaces.models.StoredRequest.objects", "line_number": 125, "usage_type": "attribute"}, {"api_name": "tworaven_apps.ta2_interfaces.models.StoredRequest", "line_number": 125, "usage_type": "name"}, {"api_name": "tworaven_apps.ta2_interfaces.models.StoredRequest.DoesNotExist", "line_number": 128, "usage_type": "attribute"}, {"api_name": "tworaven_apps.ta2_interfaces.models.StoredRequest", "line_number": 128, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 130, "usage_type": "call"}, {"api_name": "tworaven_apps.utils.view_helper.get_json_error", "line_number": 130, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 134, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 135, "usage_type": "call"}, {"api_name": "tworaven_apps.utils.view_helper.get_json_success", "line_number": 137, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 139, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 116, "usage_type": "name"}, {"api_name": "tworaven_apps.utils.view_helper.get_authenticated_user", "line_number": 146, "usage_type": "call"}, {"api_name": "tworaven_apps.ta2_interfaces.models.StoredResponse.objects.get", "line_number": 152, "usage_type": "call"}, {"api_name": "tworaven_apps.ta2_interfaces.models.StoredResponse.objects", "line_number": 152, "usage_type": "attribute"}, {"api_name": "tworaven_apps.ta2_interfaces.models.StoredResponse", "line_number": 152, "usage_type": "name"}, {"api_name": "tworaven_apps.ta2_interfaces.models.StoredResponse.DoesNotExist", "line_number": 155, "usage_type": "attribute"}, {"api_name": "tworaven_apps.ta2_interfaces.models.StoredResponse", "line_number": 155, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 157, "usage_type": "call"}, {"api_name": "tworaven_apps.utils.view_helper.get_json_error", "line_number": 157, "usage_type": "call"}, {"api_name": "tworaven_apps.ta2_interfaces.models.StoredResponse.mark_as_read", "line_number": 159, "usage_type": "call"}, {"api_name": "tworaven_apps.ta2_interfaces.models.StoredResponse", "line_number": 159, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 163, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 164, "usage_type": "call"}, {"api_name": "tworaven_apps.utils.view_helper.get_json_success", "line_number": 166, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 168, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 143, "usage_type": "name"}]}
+{"seq_id": "62135654", "text": "import os\nimport numpy as np\nimport torch\nimport math\n\nfrom torch.utils.data import DataLoader\nfrom model import BiSeNet\nfrom face_dataset import FaceMask\nfrom sklearn.metrics import f1_score\n\n\nif __name__ == '__main__':\n\n    model_dir = '/home/jihyun/workspace/face_parsing/face-parsing.PyTorch/res/exp'\n    n_classes = 19\n    net = BiSeNet(n_classes=n_classes)\n    net.cuda()\n    net.eval()\n\n    data_root = '/home/jihyun/workspace/face_parsing/dataset/CelebAMask-HQ/'\n    cropsize = [448, 448]\n    n_img_per_gpu = 16\n    ds = FaceMask(data_root, cropsize=cropsize, mode='val')\n    dl = DataLoader(ds, batch_size=16, shuffle=False, drop_last=True)\n\n    f = open(os.path.join(model_dir, 'f_measure.txt'), 'w+')\n\n    for filename in os.listdir(os.path.join(model_dir, 'cp')):\n\n        if filename.endswith('70000_iter.pth'):\n\n            net.load_state_dict(torch.load(os.path.join(model_dir, 'cp', filename)))\n\n            total_f_score = []\n            class_f_score = [[] for i in range(n_classes)]\n\n            with torch.no_grad():\n                        \n                    for i, sample in enumerate(dl):\n                        \n                        if i == 5: break\n                        if (i+1) % 5 == 0:\n                            print('processing {}-th batch...'.format(str(i+1)))\n\n                        im, lb = sample\n                        im = im.cuda()\n                        lb = lb.cuda()\n                        lb = torch.squeeze(lb, 1)\n                        out = net(im)[0]\n\n                        pred = out.squeeze(0).argmax(1)\n                        pred = pred.reshape(-1).cpu().numpy()\n                        lb = lb.reshape(-1).cpu().numpy()\n\n                        total_f_score.append(f1_score(lb, pred, average='weighted'))\n\n                        for c in range(n_classes):\n                            c_pred = pred == c\n                            c_lb = lb == c\n                            class_f_score[c].append(f1_score(c_lb, c_pred, average='binary'))\n                            print(class_f_score[c])\n                        \n                        '''\n                        # f-measure implementation\n                        fs = []\n                        for c in range(n_classes):\n                            pred_pos = (pred == c).cpu().numpy()\n                            true_pos = (lb == c).cpu().numpy()\n                            \n                            precision = np.sum(np.logical_and(pred_pos, true_pos)) / np.sum(pred_pos)\n                            recall = np.sum(np.logical_and(pred_pos, true_pos)) / np.sum(true_pos)\n                            \n                            f_measure = (2 * precision * recall) / (precision + recall)\n                            print(f_measure)\n\n                            if not math.isnan(f_measure):\n                                fs.append(np.sum(true_pos) * f_measure)\n\n                        print(sum(fs) / (n_img_per_gpu * cropsize[0] * cropsize[1]))\n                        '''\n\n                    f.write(filename + ' | ')\n                    f.write('total: ' + str(sum(total_f_score) / len(total_f_score)) + ' | ')\n\n                    # range()로 인덱싱하기\n                    for c in range(n_classes):\n                        score = np.take(class_f_score, np.arange(c, len(class_f_score), n_classes))\n                        print(np.arange(c, len(class_f_score), n_classes))\n\n                    print(len(class_f_score))\n\n            f.close()\n\n", "sub_path": "calculate_f_measure.py", "file_name": "calculate_f_measure.py", "file_ext": "py", "file_size_in_byte": 3500, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "model.BiSeNet", "line_number": 16, "usage_type": "call"}, {"api_name": "face_dataset.FaceMask", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.squeeze", "line_number": 48, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 55, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.take", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 88, "usage_type": "call"}]}
+{"seq_id": "534432266", "text": "import logging\nimport sys\nfrom datetime import datetime as carbon\n\ndef logger():\n    date = carbon.today().strftime('%Y-%m-%d')\n    logFileName = \"logs/{}.log\".format(date)\n    logging.basicConfig(filename=logFileName,\n                        format='%(asctime)s|%(levelname)s|%(filename)s:%(lineno)s|%(message)s',\n                        level=logging.DEBUG)\n    return logging", "sub_path": "logger.py", "file_name": "logger.py", "file_ext": "py", "file_size_in_byte": 378, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "datetime.datetime.today", "line_number": 6, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 6, "usage_type": "name"}, {"api_name": "logging.basicConfig", "line_number": 8, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 10, "usage_type": "attribute"}]}
+{"seq_id": "572989372", "text": "\"\"\"hotel_project URL Configuration\r\n\r\nThe `urlpatterns` list routes URLs to views. For more information please see:\r\n    https://docs.djangoproject.com/en/3.1/topics/http/urls/\r\nExamples:\r\nFunction views\r\n    1. Add an import:  from my_app import views\r\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\r\nClass-based views\r\n    1. Add an import:  from other_app.views import Home\r\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\r\nIncluding another URLconf\r\n    1. Import the include() function: from django.urls import include, path\r\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\r\n\"\"\"\r\nfrom django.contrib import admin\r\nfrom django.urls import path, include\r\nfrom django.contrib.auth import views as auth_views\r\n\r\nfrom booking import views\r\n\r\nurlpatterns = [\r\n    path('', views.main_page, name='main_page'),\r\n    path('hotels/', views.HotelsList.as_view(), name='hotels'),\r\n    path('rooms/', views.RoomsList.as_view(), name='rooms'),\r\n    path('hotels//', views.RoomsInHotelList.as_view()),\r\n    path('rooms//', views.RoomInfo.as_view()),\r\n    path('rooms//book', views.BookRoom.as_view(), name='booking'),\r\n    path('register/', views.register, name='register'),\r\n    path('profile/', views.profile, name='profile_render'),\r\n    path('profile/edit', views.profile_edit, name='profile_edit'),\r\n    path('profile/bookings', views.BookingsList.as_view(), name='bookings'),\r\n    path('profile/bookings/delete//', views.DeleteBooking.as_view(), name='delete_booking'),\r\n    path('reviews/', views.ReviewsList.as_view(), name='review_list'),\r\n    path('rooms//add_review', views.ReviewCreation.as_view(), name='add_review'),\r\n    path('last_guests/', views.GuestsList.as_view(), name='last_guests'),\r\n    path('admin/', admin.site.urls),\r\n    path('accounts/login/', auth_views.LoginView.as_view(template_name='login.html')),\r\n    path('accounts/', include('django.contrib.auth.urls')),\r\n\r\n]\r\n", "sub_path": "students/k33402/Sholomov_Dan/hotel_project/hotel_project/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1996, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "booking.views.main_page", "line_number": 23, "usage_type": "attribute"}, {"api_name": "booking.views", "line_number": 23, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "booking.views.HotelsList.as_view", "line_number": 24, "usage_type": "call"}, {"api_name": "booking.views.HotelsList", "line_number": 24, "usage_type": "attribute"}, {"api_name": "booking.views", "line_number": 24, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "booking.views.RoomsList.as_view", "line_number": 25, "usage_type": "call"}, {"api_name": "booking.views.RoomsList", "line_number": 25, "usage_type": "attribute"}, {"api_name": "booking.views", "line_number": 25, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "booking.views.RoomsInHotelList.as_view", "line_number": 26, "usage_type": "call"}, {"api_name": "booking.views.RoomsInHotelList", "line_number": 26, "usage_type": "attribute"}, {"api_name": "booking.views", "line_number": 26, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 27, "usage_type": "call"}, {"api_name": "booking.views.RoomInfo.as_view", "line_number": 27, "usage_type": "call"}, {"api_name": "booking.views.RoomInfo", "line_number": 27, "usage_type": "attribute"}, {"api_name": "booking.views", "line_number": 27, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 28, "usage_type": "call"}, {"api_name": "booking.views.BookRoom.as_view", "line_number": 28, "usage_type": "call"}, {"api_name": "booking.views.BookRoom", "line_number": 28, "usage_type": "attribute"}, {"api_name": "booking.views", "line_number": 28, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 29, "usage_type": "call"}, {"api_name": "booking.views.register", "line_number": 29, "usage_type": "attribute"}, {"api_name": "booking.views", "line_number": 29, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 30, "usage_type": "call"}, {"api_name": "booking.views.profile", "line_number": 30, "usage_type": "attribute"}, {"api_name": "booking.views", "line_number": 30, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 31, "usage_type": "call"}, {"api_name": "booking.views.profile_edit", "line_number": 31, "usage_type": "attribute"}, {"api_name": "booking.views", "line_number": 31, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 32, "usage_type": "call"}, {"api_name": "booking.views.BookingsList.as_view", "line_number": 32, "usage_type": "call"}, {"api_name": "booking.views.BookingsList", "line_number": 32, "usage_type": "attribute"}, {"api_name": "booking.views", "line_number": 32, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 33, "usage_type": "call"}, {"api_name": "booking.views.DeleteBooking.as_view", "line_number": 33, "usage_type": "call"}, {"api_name": "booking.views.DeleteBooking", "line_number": 33, "usage_type": "attribute"}, {"api_name": "booking.views", "line_number": 33, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 34, "usage_type": "call"}, {"api_name": "booking.views.ReviewsList.as_view", "line_number": 34, "usage_type": "call"}, {"api_name": "booking.views.ReviewsList", "line_number": 34, "usage_type": "attribute"}, {"api_name": "booking.views", "line_number": 34, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 35, "usage_type": "call"}, {"api_name": "booking.views.ReviewCreation.as_view", "line_number": 35, "usage_type": "call"}, {"api_name": "booking.views.ReviewCreation", "line_number": 35, "usage_type": "attribute"}, {"api_name": "booking.views", "line_number": 35, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 36, "usage_type": "call"}, {"api_name": "booking.views.GuestsList.as_view", "line_number": 36, "usage_type": "call"}, {"api_name": "booking.views.GuestsList", "line_number": 36, "usage_type": "attribute"}, {"api_name": "booking.views", "line_number": 36, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 37, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 37, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 37, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 38, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.LoginView.as_view", "line_number": 38, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.LoginView", "line_number": 38, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.views", "line_number": 38, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 39, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 39, "usage_type": "call"}]}
+{"seq_id": "274662914", "text": "from discord.ext import commands\nimport discord\nimport config\n\n# authors: stickyee and joshuathooyavan\n\n# DISCORD BOT\nstartup_extensions = [\"Commands.event_commands\", \"Commands.helpme\"]\nbot = commands.Bot(command_prefix=config.bot_prefix)\n\n\n# errors can be specified here.\n@bot.event\nasync def on_command_error(ctx, error):\n    # if there is a missing argument for any command the below statement is run.\n    if isinstance(error, commands.MissingRequiredArgument):\n        embed = discord.Embed(color=discord.Color.from_rgb(128, 0, 0), title=' ')\n        embed.add_field(name=f'You are missing an argument!', value='Please try again, but with something after the command :sunglasses:',\n                        inline=False)\n        await ctx.send(embed=embed)\n    if isinstance(error, commands.errors.CommandInvokeError):\n        embed = discord.Embed(color=discord.Color.from_rgb(128, 0, 0), title=' ')\n        embed.add_field(name=f'Oh no!', value='Something went wrong, please try again.\\nPossible issue: Invalid argument or no event code set..',\n                        inline=False)\n        await ctx.send(embed=embed)\n    # more errors can be added below.\n\n\n@bot.event\nasync def on_ready():\n    print('Bot is Online:', bot.user.name)\n    for extension in startup_extensions:\n        bot.load_extension(extension)\n    await bot.change_presence(activity=discord.Activity(type=discord.ActivityType.listening, name=\"-helpme\"), status=discord.Status.dnd)\n\n\nbot.run(config.bot_key)\n", "sub_path": "bot.py", "file_name": "bot.py", "file_ext": "py", "file_size_in_byte": 1482, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "discord.ext.commands.Bot", "line_number": 9, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 9, "usage_type": "name"}, {"api_name": "config.bot_prefix", "line_number": 9, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.MissingRequiredArgument", "line_number": 16, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 16, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 17, "usage_type": "call"}, {"api_name": "discord.Color.from_rgb", "line_number": 17, "usage_type": "call"}, {"api_name": "discord.Color", "line_number": 17, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.errors", "line_number": 21, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 21, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 22, "usage_type": "call"}, {"api_name": "discord.Color.from_rgb", "line_number": 22, "usage_type": "call"}, {"api_name": "discord.Color", "line_number": 22, "usage_type": "attribute"}, {"api_name": "discord.Activity", "line_number": 34, "usage_type": "call"}, {"api_name": "discord.ActivityType", "line_number": 34, "usage_type": "attribute"}, {"api_name": "discord.Status", "line_number": 34, "usage_type": "attribute"}, {"api_name": "config.bot_key", "line_number": 37, "usage_type": "attribute"}]}
+{"seq_id": "291744809", "text": "from flask import Flask , request , render_template\r\nimport pickle\r\nimport sklearn\r\nfrom sklearn.linear_model import LogisticRegression\r\nimport numpy as np\r\nfrom sklearn.feature_extraction.text import CountVectorizer\r\n\r\napp = Flask(__name__)\r\n\r\n@app.route('//')\r\ndef fun1():\r\n    return render_template('index.html')\r\nco = pickle.load(open('count.pkl','rb'))\r\nsol = pickle.load(open('spam.pkl','rb'))\r\n@app.route('/predict',methods = ['GET','POST'])\r\ndef fun2():\r\n    if request.method == 'POST':\r\n        text = request.form[\"message\"]\r\n        d = [text]\r\n        vectors = co.transform(d)\r\n        vectors = vectors.toarray()\r\n        final = sol.predict(vectors)\r\n        final = final[0]\r\n        if final == 0:\r\n            return render_template('index.html',prediction_text = \"Not Spam\")\r\n        else:\r\n            return render_template('index.html',prediction_text = \"Spam Mail\")\r\n\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n    app.run(debug=True)", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 951, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 12, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 13, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 17, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 18, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 18, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 27, "usage_type": "call"}]}
+{"seq_id": "235767015", "text": "#>_> coding:utf-8 >_>\n\nimport six\nimport json\nimport teca.generation as tecagen\n\ndef loadShortUrls(config):\n\ttry:\n\t\twith open(config.short_links.links_database) as links_file:\n\t\t\treturn json.load(links_file)\n\texcept IOError:\n\t\treturn dict()\n\ndef dumpShortUrls(urls, config):\n\ttry:\n\t\twith open(config.short_links.links_database) as links_file:\n\t\t\tjson.dump(urls, links_file)\n\texcept IOError as e:\n\t\traise IOError(\"could not save the new short URLs to disk: {0}\".format(e))\n\t\t\n", "sub_path": "teca/shorturls.py", "file_name": "shorturls.py", "file_ext": "py", "file_size_in_byte": 475, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "json.load", "line_number": 10, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 17, "usage_type": "call"}]}
+{"seq_id": "313043083", "text": "# Bibliotecas importadas\r\n# region\r\nimport os\r\nimport shutil\r\nimport pathlib\r\nimport sys\r\nimport subprocess\r\nimport msvcrt\r\nfrom subprocess import Popen\r\nimport tkinter\r\nfrom tkinter import *\r\nimport tkinter.messagebox\r\nimport time\r\n# endregion\r\n\r\n# Variáveis\r\n# region\r\nARQUIVO_BAT = 'teste.bat'\r\n\r\nUSERLOCAL = ''\r\nPASSWORDLOCAL = ''\r\nUSERSERVER = ''\r\nPASSWORDSERVER = ''\r\n\r\nCAMINHO_ÚNICO = ''\r\nCAMINHO = ''\r\n\r\nODBC_SERVER = ''\r\nODBC_LOCAL = ''\r\nCONNECTION_SERVER = ''\r\nCONNECTION_LOCAL = ''\r\n\r\narquivo = ''\r\nbackup122 = ''\r\nbackup121 = ''\r\nbackup88 = ''\r\n\r\ntexto = ''\r\ncaminho = ''\r\nescolha = ''\r\npressionou_primeira_config = False\r\ndict = {}\r\n# endregion\r\n\r\n# Funções\r\n# region\r\n\r\n\r\ndef ConfigurarArquivos(escolha):\r\n    dict = LerCFG(escolha)\r\n    global pressionou_primeira_config \r\n    pressionou_primeira_config = True\r\n    DuplicaArquivo(escolha, dict['arquivo'], dict['backup122'],\r\n                   dict['backup121'],  dict['backup88'])\r\n    RenomeiaArquivo(\r\n        escolha, dict['arquivo'], dict['backup122'],  dict['backup121'],  dict['backup88'])\r\n    tkinter.messagebox.showinfo(\r\n        title='Concluído', message='Ambiente configurado!')\r\n\r\n        \r\n\r\n\r\ndef AbrirHelp():\r\n    janela2 = Tk()\r\n    janela2.title('Help')\r\n    Label(janela2, text='*********************** Primeira Configuração ***********************\\n').grid(row=0)\r\n    Label(janela2, text='- Configure o arquivo .cfg, e mantenha-o no mesmo diretório deste executável.').grid(row=1, sticky=W)\r\n    Label(janela2, text='- Abra o Tools local do ambiente a ser configurado').grid(row=2, sticky=W)\r\n    Label(janela2, text='- Altere o nome do ODBC Server, para o correspondente do mesmo').grid(row=3, sticky=W)\r\n    Label(janela2, text='     em View -> Options... -> Check In/ Out -> Data Sources').grid(row=4, sticky=W)\r\n    Label(janela2, text='- Clique em '\"OK\"' e feche o Tools em seguida.').grid(row=5, sticky=W)\r\n    Label(janela2, text='- Abra o menu Config deste programa').grid(row=6, sticky=W)\r\n    Label(janela2, text='- Escolha a config para o ambiente que os parâmetros de Check In/Out foram alterados.').grid(row=7, sticky=W)\r\n    Label(janela2, text='- O mesmo processo deve ser repetido para os outros ambientes.').grid(row=8, sticky=W)\r\n    janela2.mainloop()\r\n\r\n\r\ndef ChecaPrimeiraConfiguracao(escolha, backup122, backup121, backup88):\r\n    return switch_case_backup(escolha, backup122, backup121, backup88).is_file()\r\n\r\n\r\ndef DuplicaArquivo(escolha, arquivo, backup122, backup121, backup88):\r\n    if(arquivo.is_file()):\r\n        if(escolha == 1 or escolha == 2):\r\n            shutil.copy2(arquivo, backup122)\r\n\r\n        if(escolha == 3 or escolha == 4):\r\n            shutil.copy2(arquivo, backup121)\r\n\r\n        if(escolha == 5 or escolha == 6):\r\n            shutil.copy2(arquivo, backup88)\r\n\r\n\r\ndef RenomeiaArquivo(escolha, arquivo, backup122, backup121, backup88):\r\n    if((escolha == 1 or escolha == 2) and backup122.is_file() == True):\r\n        shutil.copy2(backup122, backup122.name + ' - Copia')\r\n        shutil.move(backup122, arquivo)\r\n        shutil.move(backup122.name + ' - Copia', backup122)\r\n\r\n    if((escolha == 3 or escolha == 4) and backup121.is_file() == True):\r\n        shutil.copy2(backup121, backup121.name + ' - Copia')\r\n        shutil.move(backup121, arquivo)\r\n        shutil.move(backup121.name + ' - Copia', backup121)\r\n\r\n    if((escolha == 5 or escolha == 6) and backup88.is_file() == True):\r\n        shutil.copy2(backup88, backup88.name + ' - Copia')\r\n        shutil.move(backup88, arquivo)\r\n        shutil.move(backup88.name + ' - Copia', backup88)\r\n\r\n\r\ndef validacao(string, variavel, ambiente, linha_split):\r\n    if (ambiente == string):\r\n        variavel = linha_split[1]\r\n        variavel = variavel[0: len(variavel)].strip(\r\n            ' ').strip('\\n').strip('\\t')\r\n        return variavel\r\n    else:\r\n        return \"\"\r\n\r\n\r\ndef switch_case(argument):\r\n    switcher = {\r\n        1: '[122]',\r\n        2: '[122]',\r\n        3: '[121]',\r\n        4: '[121]',\r\n        5: '[88]',\r\n        6: '[88]'}\r\n    return (switcher.get(argument, \"Número inválido\"))\r\n\r\n\r\ndef switch_case_backup(argument, backup122, backup121, backup88):\r\n    switcher = {\r\n        1: backup122,\r\n        2: backup122,\r\n        3: backup121,\r\n        4: backup121,\r\n        5: backup88,\r\n        6: backup88}\r\n    return (switcher[argument])\r\n\r\n\r\ndef switch_case_2(argument, CONNECTION_SERVER, CONNECTION_LOCAL):\r\n    if(argument % 2 == 0):\r\n        return CONNECTION_SERVER\r\n    else:\r\n        return CONNECTION_LOCAL\r\n\r\n\r\ndef LerCFG(escolha):\r\n    with open('AbrirTools.cfg', 'r+', encoding=\"utf8\") as file:\r\n        texto = file.readlines()\r\n        section = ''\r\n\r\n        # Variáveis\r\n        # region\r\n\r\n        USERLOCAL = ''\r\n        PASSWORDLOCAL = ''\r\n        USERSERVER = ''\r\n        PASSWORDSERVER = ''\r\n\r\n        ODBC_SERVER = ''\r\n        ODBC_LOCAL = ''\r\n        CONNECTION_SERVER = ''\r\n        CONNECTION_LOCAL = ''\r\n\r\n        caminho = ''\r\n        # endregion\r\n        for linha in texto:\r\n            linha_split = linha.split(\"=\")\r\n\r\n            if(len(linha_split) > 1):\r\n                ambiente = str(linha_split[0]).strip(\r\n                    ' ').strip('\\n').strip('\\t')\r\n                variavel = str(linha_split[1]).strip(\r\n                    ' ').strip('\\n').strip('\\t')\r\n\r\n            else:\r\n                ambiente = str(linha_split[0]).strip(\r\n                    ' ').strip('\\n').strip('\\t')\r\n                if('[CAMINHO CFG ÚNICO]' in ambiente):\r\n                    ambiente = '[CAMINHO CFG ÚNICO]'\r\n                    try:\r\n                        variavel = str(linha_split[1]).strip(\r\n                            ' ').strip('\\n').strip('\\t')\r\n                    except:\r\n                        variavel = ''\r\n\r\n                if('[' in ambiente and ']' in ambiente):\r\n                    section = ambiente\r\n\r\n            if(section == switch_case(escolha)):\r\n                ODBC_SERVER += validacao('ODBC SERVER',\r\n                                         variavel, ambiente, linha_split)\r\n                ODBC_LOCAL += validacao('ODBC LOCAL',\r\n                                        variavel, ambiente, linha_split)\r\n\r\n                CONNECTION_SERVER += validacao('SERVER',\r\n                                               variavel, ambiente, linha_split)\r\n                CONNECTION_LOCAL += validacao('LOCAL',\r\n                                              variavel, ambiente, linha_split)\r\n\r\n                USERLOCAL += validacao('USER LOCAL',\r\n                                       variavel, ambiente, linha_split)\r\n                PASSWORDLOCAL += validacao('PASSWORD LOCAL',\r\n                                           variavel, ambiente, linha_split)\r\n\r\n                USERSERVER += validacao('USER SERVER',\r\n                                        variavel, ambiente, linha_split)\r\n                PASSWORDSERVER += validacao('PASSWORD SERVER',\r\n                                            variavel, ambiente, linha_split)\r\n\r\n                caminho += validacao('PATH CFG LOCAL',\r\n                                     variavel, ambiente, linha_split)\r\n\r\n            if(variavel != '' and section == '[CAMINHO CFG ÚNICO]'):\r\n                caminho += variavel\r\n\r\n    arquivo = pathlib.Path(f'C:\\Siebel\\8.1\\Tools_1\\BIN\\{USERLOCAL}&Siebel Tools.spf')\r\n    backup122 = pathlib.Path(f'C:\\Siebel\\8.1\\Tools_1\\BIN\\{USERLOCAL}&Siebel ToolsBackup122.spf')\r\n    backup121 = pathlib.Path(f'C:\\Siebel\\8.1\\Tools_1\\BIN\\{USERLOCAL}&Siebel ToolsBackup121.spf')\r\n    backup88 = pathlib.Path(f'C:\\Siebel\\8.1\\Tools_1\\BIN\\{USERLOCAL}&Siebel ToolsBackup88.spf')\r\n    retorno = {\r\n        'ODBC_SERVER': ODBC_SERVER,\r\n        'ODBC_LOCAL': ODBC_LOCAL,\r\n        'CONNECTION_SERVER': CONNECTION_SERVER,\r\n        'CONNECTION_LOCAL': CONNECTION_LOCAL,\r\n        'USERLOCAL': USERLOCAL,\r\n        'PASSWORDLOCAL': PASSWORDLOCAL,\r\n        'USERSERVER': USERSERVER,\r\n        'PASSWORDSERVER': PASSWORDSERVER,\r\n        'caminho': caminho,\r\n        'arquivo': arquivo,\r\n        'backup122': backup122,\r\n        'backup121': backup121,\r\n        'backup88': backup88}\r\n    return retorno\r\n\r\n# Abrir o Tools\r\n\r\n\r\ndef AbrirTools(escolha):\r\n    dict = LerCFG(escolha)\r\n    while True:\r\n        CONNECT_TO = switch_case_2(\r\n            escolha, dict['CONNECTION_SERVER'], dict['CONNECTION_LOCAL'])\r\n        if(ChecaPrimeiraConfiguracao(escolha, dict['backup122'],  dict['backup121'],  dict['backup88']) == False):\r\n            tkinter.messagebox.showwarning('AVISO!', 'Você ainda não fez a primeira configuração deste ambiente!\\n'\r\n                                           + '------Clique no menu \"Help\" para instruções------')\r\n            break\r\n        if((dict['caminho'] == '')):\r\n            tkinter.messagebox.showwarning(\r\n                'AVISO!', '------Escolha um ODBC configurado------')\r\n            break\r\n        else:\r\n            if(escolha % 2 == 0):\r\n                filepath = \"C:\\Siebel\\8.1\\Tools_1\\BIN\\siebdev.exe\" + \" /c \" + \\\r\n                    dict['caminho'] + \" /u \" + dict['USERLOCAL'] + \\\r\n                    \" /p \" + dict['PASSWORDLOCAL'] + \" /d \" + CONNECT_TO\r\n            else:\r\n                filepath = \"C:\\Siebel\\8.1\\Tools_1\\BIN\\siebdev.exe\" + \" /c \" + \\\r\n                    dict['caminho'] + \" /u \" + dict['USERSERVER'] + \\\r\n                    \" /p \" + dict['PASSWORDSERVER'] + \" /d \" + CONNECT_TO\r\n\r\n            RenomeiaArquivo(\r\n                escolha, dict['arquivo'], dict['backup122'],  dict['backup121'],  dict['backup88'])\r\n\r\n            if(pressionou_primeira_config == True):\r\n                time.sleep(3)\r\n                pressionou_primeira_config == False\r\n\r\n            subprocess.Popen(filepath, stdout=subprocess.PIPE)\r\n            break\r\n# endregion\r\n\r\n\r\n# Tela\r\n# region\r\njanela = Tk()\r\njanela.title(\"Abrir Tools\")\r\nbotao1 = Menubutton(janela, text=\"CONVERGENCIA\", bd=4,\r\n                    relief=RAISED, padx=40, pady=20)\r\nbotao1.menu = Menu(botao1, tearoff=0)\r\nbotao1['menu'] = botao1.menu\r\nbotao1.menu.add_command(label='Server', command=lambda: AbrirTools(1))\r\nbotao1.menu.add_command(label='Local', command=lambda: AbrirTools(2))\r\n\r\nbotao2 = Menubutton(janela, text=\"PROJ\", bd=4, relief=RAISED, padx=71, pady=20)\r\nbotao2.menu = Menu(botao2, tearoff=0)\r\nbotao2['menu'] = botao2.menu\r\nbotao2.menu.add_command(label='Server', command=lambda: AbrirTools(3))\r\nbotao2.menu.add_command(label='Local', command=lambda: AbrirTools(4))\r\n\r\nbotao3 = Menubutton(janela, text=\"FIBER PROJ\", bd=4,\r\n                    relief=RAISED, padx=55, pady=20)\r\nbotao3.menu = Menu(botao3, tearoff=0)\r\nbotao3['menu'] = botao3.menu\r\nbotao3.menu.add_command(label='Server', command=lambda: AbrirTools(5))\r\nbotao3.menu.add_command(label='Local', command=lambda: AbrirTools(6))\r\n\r\nbotao1.pack()\r\nbotao2.pack()\r\nbotao3.pack()\r\n\r\nmenu = Menu(janela)\r\njanela.config(menu=menu)\r\n\r\nsubMenu = Menu(menu, tearoff=0)\r\nmenu.add_cascade(label='Config', menu=subMenu)\r\nsubMenu.add_command(label='Primeira Config 122',\r\n                    command=lambda: ConfigurarArquivos(2))\r\nsubMenu.add_command(label='Primeira Config 121',\r\n                    command=lambda: ConfigurarArquivos(4))\r\nsubMenu.add_command(label='Primeira Config 88',\r\n                    command=lambda: ConfigurarArquivos(6))\r\n\r\nhelpMenu = Menu(menu, tearoff=0)\r\nmenu.add_command(label='Help', command=AbrirHelp)\r\n\r\njanela.geometry('250x210')\r\n\r\njanela.mainloop()\r\n# endregion\r\n", "sub_path": "AbrirToolsAutomacao/AbrirToolsAutomacao.py", "file_name": "AbrirToolsAutomacao.py", "file_ext": "py", "file_size_in_byte": 11421, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "tkinter.messagebox.showinfo", "line_number": 57, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 57, "usage_type": "attribute"}, {"api_name": "shutil.copy2", "line_number": 85, "usage_type": "call"}, {"api_name": "shutil.copy2", "line_number": 88, "usage_type": "call"}, {"api_name": "shutil.copy2", "line_number": 91, "usage_type": "call"}, {"api_name": "shutil.copy2", "line_number": 96, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 97, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 98, "usage_type": "call"}, {"api_name": "shutil.copy2", "line_number": 101, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 102, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 103, "usage_type": "call"}, {"api_name": "shutil.copy2", "line_number": 106, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 107, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 108, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 220, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 221, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 222, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 223, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showwarning", "line_number": 249, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 249, "usage_type": "attribute"}, {"api_name": "tkinter.messagebox.showwarning", "line_number": 253, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 253, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 270, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 273, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 273, "usage_type": "attribute"}]}
+{"seq_id": "628373861", "text": "from django import template\nfrom django.core.cache import cache\nfrom django.template import Node, TemplateSyntaxError, Variable\nfrom django.template import resolve_variable\n\nregister = template.Library()\n\nclass CacheNode(Node):\n    def __init__(self, nodelist, expire_time, key):\n        self.nodelist = nodelist\n        self.expire_time = Variable(expire_time)\n        self.key = key\n\n    def render(self, context):\n        key = resolve_variable(self.key, context)\n        expire_time = int(self.expire_time.resolve(context))\n\n        value = cache.get(key)\n        if value is None:\n            value = self.nodelist.render(context)\n            cache.set(key, value, expire_time)\n        return value\n\n@register.tag\ndef cachedeterministic(parser, token):\n    \"\"\"\n    This will cache the contents of a template fragment for a given amount of\n    time, just like {% cache .. %} except that the key is deterministic and not\n    mangled or run through MD5.\n\n    Usage::\n\n        {% cachedeterministic [expire_time] [key] %}\n            .. some expensive processing ..\n        {% endcachedeterministic %}\n\n    \"\"\"\n    nodelist = parser.parse(('endcachedeterministic',))\n    parser.delete_first_token()\n    tokens = token.contents.split()\n    if len(tokens) != 3:\n        raise TemplateSyntaxError(u\"'%r' tag requires 2 arguments.\" % tokens[0])\n    return CacheNode(nodelist, tokens[1], tokens[2])\n\nclass ShowIfCachedNode(Node):\n    def __init__(self, key):\n        self.key = key\n\n    def render(self, context):\n        key = resolve_variable(self.key, context)\n        return cache.get(key) or ''\n\n@register.tag\ndef showifcached(parser, token):\n    \"\"\"\n    Show content if it exists in the cache, otherwise display nothing.\n\n    The key is entirely deterministic and not mangled or run through MD5 (cf.\n    {% cache %})\n\n    Usage::\n\n        {% showifcached [key] %}\n\n    \"\"\"\n    tokens = token.contents.split()\n    if len(tokens) != 2:\n        raise TemplateSyntaxError(u\"'%r' tag requires 1 argument.\" % tokens[0])\n    return ShowIfCachedNode(tokens[1])\n", "sub_path": "cache_toolbox/templatetags/cache_toolbox.py", "file_name": "cache_toolbox.py", "file_ext": "py", "file_size_in_byte": 2055, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.template.Library", "line_number": 6, "usage_type": "call"}, {"api_name": "django.template", "line_number": 6, "usage_type": "name"}, {"api_name": "django.template.Node", "line_number": 8, "usage_type": "name"}, {"api_name": "django.template.Variable", "line_number": 11, "usage_type": "call"}, {"api_name": "django.template.resolve_variable", "line_number": 15, "usage_type": "call"}, {"api_name": "django.core.cache.cache.get", "line_number": 18, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 18, "usage_type": "name"}, {"api_name": "django.core.cache.cache.set", "line_number": 21, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 21, "usage_type": "name"}, {"api_name": "django.template.TemplateSyntaxError", "line_number": 42, "usage_type": "call"}, {"api_name": "django.template.Node", "line_number": 45, "usage_type": "name"}, {"api_name": "django.template.resolve_variable", "line_number": 50, "usage_type": "call"}, {"api_name": "django.core.cache.cache.get", "line_number": 51, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 51, "usage_type": "name"}, {"api_name": "django.template.TemplateSyntaxError", "line_number": 68, "usage_type": "call"}]}
+{"seq_id": "502662776", "text": "# Import statements\nimport psycopg2\nfrom Secret import * \nimport csv\nimport psycopg2.extras\n\n# Write code / functions to set up database connection and cursor here.\ntry:\n    conn = psycopg2.connect(\"dbname='{0}' user='{1}' password='{2}'\".format(db_name, db_user, db_password)) # No password on the databases yet -- wouldn't want to save that in plain text, anyway\n    print(\"Success connecting to database\")\nexcept:\n    print(\"Unable to connect to the database. Check server and credentials.\")\n    sys.exit(1) # Stop running program if there's no db connection.\ncur = conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor)\n\n# Write code / functions to create tables with the columns you want and all database setup here.\ncur.execute(\"\"\"DROP TABLE IF EXISTS \"Sites\" \"\"\")\ncur.execute(\"\"\"DROP TABLE IF EXISTS \"States\" \"\"\")\n\n\ncur.execute(\"\"\"CREATE TABLE IF NOT EXISTS \"States\"(\n\t\"ID\" SERIAL PRIMARY KEY,\n\t\"Name\" VARCHAR(40) NOT NULL UNIQUE)\"\"\")\n\ncur.execute(\"\"\"CREATE TABLE IF NOT EXISTS \"Sites\"(\n\t\"ID\" SERIAL PRIMARY KEY,\n\t\"Name\" VARCHAR(128) NOT NULL UNIQUE,\n\t\"Type\" VARCHAR(128),\n\t\"State_ID\" INTEGER REFERENCES \"States\"(\"ID\"),\n\t\"Location\" VARCHAR(255),\n\t\"Description\" TEXT)\"\"\")\n\n\n\n# Write code / functions to deal with CSV files and insert data into the database here.\n\n# Make sure to commit your database changes with .commit() on the database connection.\n\n# Write code to be invoked here (e.g. invoking any functions you wrote above)\n\n\ndef insert_States(states_name, conn, cur):\n    sql = \"\"\"INSERT INTO \"States\"(\"Name\") VALUES(%s) RETURNING  \"ID\" \"\"\"\n    cur.execute(sql,(states_name,)) # Must be tuple or list here, \",\" makes () be a tuple rather than list\n    # print(\"Artist name\", artist_name)\n    conn.commit()\n    rec = cur.fetchone()\n    #  print (rec)\n    return rec['ID']\n\n\ndef insert_Sites(sites_name, sites_type, sites_state_id, sites_location, sites_description, states_name, conn, cur):\n    \"\"\"Inserts an artist and returns name, None if unsuccessful\"\"\"\n    sql = \"\"\"INSERT INTO \"Sites\"(\"Name\", \"Type\", \"State_ID\", \"Location\", \"Description\") VALUES(%s, %s, %s, %s, %s)\"\"\"\n    cur.execute(sql,(sites_name, sites_type, sites_state_id, sites_location, sites_description))\n    # print(\"Artist name\", artist_name)\n    conn.commit()\n    return True\n\nstates_Name_list = [\"arkansas\",\"california\",\"michigan\"]\n\nfor n in range (len(states_Name_list)):\n\twith open(states_Name_list[n]+ '.csv', 'r', encoding = \"utf-8\") as f:\n\t\treader = csv.reader(f)\n\t\tnext(reader)\n\t\tstateID = insert_States(states_Name_list[n], conn, cur)\n\t\tfor row in reader:\n\t\t\t# print (row[0], row[2], stateID, row[1], row[4])\n\t\t\tinsert_Sites(row[0], row[2], stateID, row[1], row[4], states_Name_list[n], conn, cur)\n\n# Write code to make queries and save data in variables here.\n\ndef execute_return(query):\n\tcur.execute(query)\n\trec = cur.fetchall()\n\treturn rec\n\nall_locations = execute_return(\"\"\"SELECT \"Location\" FROM \"Sites\" \"\"\")\n# print (all_locations)\nbeautiful_sites = execute_return(\"\"\"SELECT \"Name\" FROM \"Sites\" WHERE \"Description\" ILIKE '%beautiful%' \"\"\")\n# print (beautiful_sites)\nnatl_lakeshores = execute_return(\"\"\"SELECT COUNT(*) FROM \"Sites\" WHERE \"Type\"='National Lakeshore' \"\"\")\n# print (natl_lakeshores)\nmichigan_names = execute_return(\"\"\"SELECT \"Sites\".\"Name\" FROM \"Sites\" INNER JOIN \"States\" ON \"Sites\".\"State_ID\" = \"States\".\"ID\" WHERE \"States\".\"Name\" = 'michigan'  \"\"\")\n# print (michigan_names)\ntotal_number_arkansas = execute_return(\"\"\"SELECT COUNT(*) FROM \"Sites\" INNER JOIN \"States\" ON \"Sites\".\"State_ID\" = \"States\".\"ID\" WHERE \"States\".\"Name\" = 'arkansas' \"\"\")\n# print (total_number_arkansas)\n# We have not provided any tests, but you could write your own in this file or another file, if you want.\n", "sub_path": "SI507_project6.py", "file_name": "SI507_project6.py", "file_ext": "py", "file_size_in_byte": 3698, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "psycopg2.connect", "line_number": 9, "usage_type": "call"}, {"api_name": "psycopg2.extras", "line_number": 14, "usage_type": "attribute"}, {"api_name": "csv.reader", "line_number": 64, "usage_type": "call"}]}
+{"seq_id": "11157034", "text": "from contextlib import redirect_stdout\n\nimport io\nimport time\nimport discord\nimport textwrap\nimport traceback\n\nfrom ..base import Admin\nfrom datetime import datetime\n\n\n# noinspection PyBroadException\nclass Evaluator(Admin):\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self._last_result = None\n\n    @staticmethod\n    def cleanup_code(content):\n        \"\"\"Automatically removes code blocks from the code.\"\"\"\n        # remove ```py\\n```\n        if content.startswith('```') and content.endswith('```'):\n            return '\\n'.join(content.split('\\n')[1:-1])\n\n        # remove `foo`\n        return content.strip('` \\n')\n\n    @Admin.command(name='eval', aliases=[\"py\", \"evaluate\"])\n    async def _eval(self, ctx, *, body: str):\n        \"\"\"Evaluates a code\"\"\"\n\n        try:\n            guild_profile = await self.bot.guild_cache.get_profile(ctx.guild.id)\n        except AttributeError:\n            guild_profile = None\n\n        env = {\n            'bot': self.bot,\n            'ctx': ctx,\n            'channel': ctx.channel,\n            'author': ctx.author,\n            'guild': ctx.guild,\n            'guild_profile': guild_profile,\n            'message': ctx.message,\n            '_': self._last_result\n        }\n\n        env.update(globals())\n\n        body = self.cleanup_code(body)\n        stdout = io.StringIO()\n\n        to_compile = f'async def func():\\n{textwrap.indent(body, \"  \")}'\n\n        async with ctx.loading():\n            try:\n                start_eval_at = time.time()\n                exec(to_compile, env)\n            except Exception as e:\n                embed = ctx.embed_line(f\"❗    Error while compiling.\")\n                embed.description = f'```py\\n{e.__class__.__name__}: {e}\\n```'\n                embed.timestamp = datetime.now()\n                return await ctx.send(embed=embed)\n\n            func = env['func']\n            try:\n                with redirect_stdout(stdout):\n                    ret = await func()\n                    evaluated_in = time.time() - start_eval_at\n            except Exception as _:\n                value = stdout.getvalue()\n                embed = ctx.embed_line(f\"⚠    An unexpected exception occurred.\")\n                embed.description = f'```py\\n{value}{traceback.format_exc()}\\n```'\n                embed.timestamp = datetime.now()\n                await ctx.send(embed=embed)\n            else:\n                value = stdout.getvalue()\n\n                self._last_result = ret\n\n                try:\n                    embed = ctx.embed_line(f\"{ctx.emotes.web_emotion.galka}    Evaluated in {round(evaluated_in, 3)} seconds.\")\n                    embed.description = f\"```py\\n{body}\\n```\"\n                    if value:\n                        embed.add_field(name=\"Standard Output\", value=f'```py\\n{value}\\n```')\n                    if ret:\n                        embed.add_field(name=\"Returned\", value=f'```py\\n{ret}\\n```')\n                    embed.timestamp = datetime.now()\n                    await ctx.send(embed=embed)\n                except discord.HTTPException:\n                    haste_url = await self.bot.utilities.haste(f\"{value}{ret}\")\n                    await ctx.send_line(f\"🔗    {haste_url.py}\")\n", "sub_path": "cosmos/core/plugins/admin/controller/evaluator.py", "file_name": "evaluator.py", "file_ext": "py", "file_size_in_byte": 3234, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "base.Admin", "line_number": 14, "usage_type": "name"}, {"api_name": "io.StringIO", "line_number": 53, "usage_type": "call"}, {"api_name": "textwrap.indent", "line_number": 55, "usage_type": "call"}, {"api_name": "time.time", "line_number": 59, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 64, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 64, "usage_type": "name"}, {"api_name": "contextlib.redirect_stdout", "line_number": 69, "usage_type": "call"}, {"api_name": "time.time", "line_number": 71, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 75, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 76, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 76, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 90, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 90, "usage_type": "name"}, {"api_name": "discord.HTTPException", "line_number": 92, "usage_type": "attribute"}, {"api_name": "base.Admin.command", "line_number": 30, "usage_type": "call"}, {"api_name": "base.Admin", "line_number": 30, "usage_type": "name"}]}
+{"seq_id": "516498495", "text": "\"\"\"\nDjime utility functions.\n\"\"\"\nfrom math import floor\nimport calendar\nfrom datetime import timedelta\n\ntry:\n    import json\nexcept ImportError:\n    from django.utils import simplejson as json\n\ndef delta_to_seconds(delta):\n    \"\"\"Convert a timedelta object into the equivalent number of seconds.\"\"\"\n    return (delta.days * 86400) + delta.seconds\n\ndef format_seconds(seconds):\n    \"\"\"\n    Format seconds for display as hours and minutes.\n\n    \"\"\"\n    duration = {\n        'hours': floor(seconds / 3600),\n        'minutes': floor((seconds % 3600) / 60),\n        'seconds': floor(seconds % 60),\n    }\n\n    return '%02i:%02i' % (duration['hours'], duration['minutes'])\n\n\ndef timesheet_timeslice_handler(timeslices):\n    if not timeslices:\n        return timeslices\n    timeslices = timeslices.exclude(duration=None).order_by('task', 'note')\n    result = []\n    test = []\n    # Create a timeslice like object, that is unique.\n    temp_slice =  type('temp_slice', (object,), {'note': 0, 'task':0})\n    for timeslice in timeslices:\n        if timeslice.note == temp_slice.note and timeslice.task == temp_slice.task:\n            temp_slice.duration += timeslice.duration\n        else:\n            result.append(temp_slice)\n            temp_slice = timeslice\n    result.append(temp_slice)\n    return result[1:]\n\ndef flot_timeslices(timeslices, start, end):\n    \"\"\"\n    Function to convert a Queryset of timselices into data that can be\n    used by flot in json format.\n    \"\"\"\n    timeslices.exclude(duration=None)\n    min_val = calendar.timegm(start.timetuple()) * 1000\n    vdict = {}\n    while start <= end:\n        vdict[start] = 0\n        start += timedelta(days=1)\n    for tslice in timeslices:\n        # Can timeslice doesn't have a duration (still is active) we can't\n        # add it to the total.\n        if tslice.duration:\n            vdict[tslice.begin.date()] += tslice.duration\n    vlist = []\n    keys = vdict.keys()\n    keys.sort()\n    for key in keys[:-1]:\n        # Only show entries where the days duration is above 10 mins.\n        if vdict[key] > 600:\n            vlist.append([calendar.timegm(key.timetuple()) * 1000,\n                                                        vdict[key] * 1000])\n    result = json.dumps({\n        'flot': vlist,\n        'min': min_val,\n        'max': calendar.timegm((end - timedelta(days=1)).timetuple()) * 1000,\n    })\n    return result\n", "sub_path": "djime/util.py", "file_name": "util.py", "file_ext": "py", "file_size_in_byte": 2383, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "math.floor", "line_number": 23, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 24, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 25, "usage_type": "call"}, {"api_name": "calendar.timegm", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 58, "usage_type": "call"}, {"api_name": "calendar.timegm", "line_number": 70, "usage_type": "call"}, {"api_name": "django.utils.simplejson.dumps", "line_number": 72, "usage_type": "call"}, {"api_name": "django.utils.simplejson", "line_number": 72, "usage_type": "name"}, {"api_name": "calendar.timegm", "line_number": 75, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 75, "usage_type": "call"}]}
+{"seq_id": "548577241", "text": "import numpy as np\nimport pandas as pd\nimport statsmodels.api as sm\nimport random\n\n# 데이터셋을 데이터프레임으로 읽음\niris = pd.read_csv('iris.csv', sep=',', header=0)\niris.columns = [heading.lower() for heading in iris.columns.str.replace('.','_')]\n\niris['YesorNo'] = np.where(iris['variety'] == 'Setosa', 1., 0.)\n\n\n## 1 로지스틱 회귀분석\ndependent_variable = iris['YesorNo']\nindependent_variables = iris[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']]\nindependent_variables_with_constant = sm.add_constant(independent_variables, prepend=True)\n\nlogit_model = sm.Logit(dependent_variable, independent_variables_with_constant).fit_regularized()\n\nprint(\"\\nQuantities you can extract from the result:\\n%s\" % dir(logit_model))\nprint(\"\\nCoefficients:\\n%s\" % logit_model.params)\nprint(\"\\nCoefficient Std Errors:\\n%s\" % logit_model.bse)\n\n\n## 3 예측하기\n# 기존 데이터셋의 첫 10개 값을 가지고 '새로운' 관측값 데이터셋을 만듦\n# sample_index_list=[2,5,7,9,40,55,78,99,101,102]\n# new_observations = iris.ix[iris.index.isin(sample_index_list), independent_variables.columns]\nnew_observations = iris.ix[iris.index.isin(range(100)), independent_variables.columns]\nnew_observations_with_constant = sm.add_constant(new_observations, prepend=True)\ny_predicted = logit_model.predict(new_observations_with_constant)\ny_predicted_rounded = [round(score, 2) for score in y_predicted]\nprint(y_predicted_rounded)", "sub_path": "03_Data_Science/3_Statistics/statistics_Chap07/Iris_Setosa.py", "file_name": "Iris_Setosa.py", "file_ext": "py", "file_size_in_byte": 1457, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pandas.read_csv", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 10, "usage_type": "call"}, {"api_name": "statsmodels.api.add_constant", "line_number": 16, "usage_type": "call"}, {"api_name": "statsmodels.api", "line_number": 16, "usage_type": "name"}, {"api_name": "statsmodels.api.Logit", "line_number": 18, "usage_type": "call"}, {"api_name": "statsmodels.api", "line_number": 18, "usage_type": "name"}, {"api_name": "statsmodels.api.add_constant", "line_number": 30, "usage_type": "call"}, {"api_name": "statsmodels.api", "line_number": 30, "usage_type": "name"}]}
+{"seq_id": "77414662", "text": "import os\nimport glob\nimport pandas as pd\n\nfrom argparse import ArgumentParser\n\ndef build_argparser():\n\n    parser = ArgumentParser()\n    \n    parser.add_argument(\"--data_path\", help=\"path to Mafauldda dataset folder\", required=True, type=str)\n    parser.add_argument(\"--output_dir\", help=\"path to output directory\", default=None, type=str)\n    parser.add_argument(\"--n_segments\", help=\"num of segments\", default=4096, type=int)\n    \n    return parser\n\nif __name__ == \"__main__\":\n    \n    args = build_argparser().parse_args()\n    \n    #data_dir = \"../../data/MAFAULDA\"\n    #output_dir = \"../../data/MAFAULDA_X\"\n    data_dir = args.data_dir\n    output_dir = args.output_dir\n    \n    _classes = [\"normal\",\n                \"imbalance\",\n                \"horizontal-misalignment\",\n                \"vertical-misalignment\",\n                \"underhang\",\n                \"overhang\"]\n    \n    #n_segments = 4096\n    n_segments = args.n_segments\n    \n    if not os.path.exists(output_dir):\n        os.makedirs(output_dir)\n    \n    for _class in _classes:\n        \n        filenames = glob.glob(os.path.join(data_dir, _class, \"*.csv\")) + \\\n                    glob.glob(os.path.join(data_dir, _class, \"*\", \"*.csv\")) + \\\n                    glob.glob(os.path.join(data_dir, _class, \"*\", \"*\", \"*.csv\"))\n        \n        print(\"{}: {} scenarios\".format(_class, len(filenames))) # check number of scenarios\n        \n        class_output_dir = os.path.join(output_dir, _class)\n        if not os.path.exists(class_output_dir):\n            os.makedirs(class_output_dir)\n        class_sample_count = 0\n        \n        for filename in filenames:\n            \n            df = pd.read_csv(filename)\n            for idx in range(0, df.shape[0], n_segments):\n                _df = df.iloc[idx:idx+n_segments, :]\n                if _df.shape[0] == n_segments:\n                    class_sample_count += 1\n                    _df.to_csv(os.path.join(class_output_dir, \"{:05d}.csv\".format(class_sample_count)))\n                \n            #break\n        #break\n        ", "sub_path": "src/SignalNet/preprocessing_1.py", "file_name": "preprocessing_1.py", "file_ext": "py", "file_size_in_byte": 2044, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 37, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 49, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}]}
+{"seq_id": "401256156", "text": "# -*- coding: utf-8 -*-\n# $Id: setup.py 225796 2010-10-31 17:46:44Z glenfant $\n\"\"\"The Products.SharkbiteSSOPlugin for PAS/PlonePAS\"\"\"\n\nfrom setuptools import setup, find_packages\nimport os\n\ndef read(*names):\n    here = os.path.dirname(os.path.abspath(__file__))\n    path = os.path.join(here, *names)\n    return open(path, 'r').read().strip()\n\nversion = read('Products', 'SharkbyteSSOPlugin', 'version.txt')\n\nsetup(name='Products.SharkbyteSSOPlugin',\n      version=version,\n      description=\"SSO Plugin for Zope 2 PAS and PlonePAS\",\n      long_description=read(\"README.txt\") + \"\\n\\n\" + read(\"docs\", \"HISTORY.txt\"),\n      # Get more strings from\n      # http://pypi.python.org/pypi?%3Aaction=list_classifiers\n      classifiers=[\n          \"Programming Language :: Python\",\n          \"Framework :: Zope2\",\n          \"Topic :: Security\"\n          ],\n      keywords='PAS SSO plugin',\n      author='Ben Mason',\n      author_email='ben@sharkbyte.co.uk',\n      url='http://plone.org/products/single-sign-on-plugin',\n      license='GPL',\n      packages=find_packages(exclude=['ez_setup']),\n      namespace_packages=['Products'],\n      include_package_data=True,\n      zip_safe=False,\n      install_requires=[\n          'setuptools',\n          # -*- Extra requirements: -*-\n          ],\n      entry_points=\"\"\"\n      # -*- Entry points: -*-\n      \"\"\",\n      )\n", "sub_path": "pypi_install_script/Products.SharkbyteSSOPlugin-0.6.0b1.tar/setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1350, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "os.path.dirname", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "setuptools.setup", "line_number": 15, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 31, "usage_type": "call"}]}
+{"seq_id": "45001406", "text": "import argparse\nimport socket\nimport time\nimport threading\nimport csv\nimport os\nimport datetime\nimport json\n\nfrom payloads import *\nimport xml.etree.cElementTree as ET\n\n###### Constant Configurations ######\ntask_wait_sleep_time = 10 # in sec\npackage_weight = 5 # in kg\ndefault_port = 33001\ndefault_dest1 = (53.309282, -6.223975)\ndefault_dest2 = (53.343473, -6.251387)\ndefault_dest3 = (53.385552, -6.256092)\nrecord_path = \"../record\"\nserver_config = None\n###### Constant Configurations ######\n\nclass TaskQueue:\n    #maintain task queue for each drone\n    def __init__(self):\n        self.queue = []\n        self.lock = threading.Lock()\n    \n    def add_task(self, base_task):\n        self.lock.acquire()\n        self.queue.append(base_task)\n        self.lock.release()\n    \n    def task_exists(self):\n        return len(self.queue) != 0\n    \n    def get_task(self):\n        self.lock.acquire()\n        return self.queue.pop(0)\n        self.lock.release()\n\nclass ConflictMgr:\n    #maintain task queue for each drone\n    def __init__(self):\n        self.drones = []\n        self.lock = threading.Lock()\n    \n    def add_alert(self, dest_loc, drone_id):\n        self.lock.acquire()\n        for drone in self.drones:\n            if drone['dest_loc'] == dest_loc:\n                drone['alert'] = True\n                drone['al_drone'] = drone_id\n        self.lock.release()\n    \n    def get_alert(self, drone_id):\n        for drone in self.drones:\n            if drone['id'] == drone_id:\n                if drone['alert']:\n                    al_drone = drone['al_drone']\n                    drone['alert'] = False\n                    drone['al_drone'] = 0\n                    return True, al_drone\n                return drone['alert'], drone['al_drone']\n    \n    def add_drone(self, drone_id, dest_loc):        \n        drone = {}\n        #init Drone Values\n        drone['id'] = drone_id\n        drone['dest_loc'] = dest_loc\n        drone['alert'] = False\n        drone['al_drone'] = 0\n\n        self.lock.acquire()\n        self.drones.append(drone)\n        self.lock.release()\n    \n    def remove_drone(self, drone_id):\n        self.lock.acquire()\n        for drone in self.drones:\n            if drone['id'] == drone_id:\n                self.drones.remove(drone)\n                break\n        self.lock.release()\n\n# check if exit flag set to any drone\ndef is_exit(drone_id):\n    return False\n\ndef writeGPXDom(rec_file, task_id, drone_id):\n    \n    gpx_root = ET.Element(\"gpx\",\n                        xmlns=\"http://www.topografix.com/GPX/1/1\",\n                        xsi=\"http://www.w3.org/2001/XMLSchema-instance\",\n                        schemaLocation=\"http://www.topografix.com/GPX/1/1 http://www.topografix.com/GPX/1/1/gpx.xsd\",\n                        version=\"1.1\", creator=\"OpenRouteService.org\")\n    trk = ET.SubElement(gpx_root, \"trk\")\n    ET.SubElement(trk, \"name\").text = \"Drone Track Trace\"\n    ET.SubElement(trk, \"desc\").text = \"Stores the path on which the drone tarvelled\"\n    trkseg= ET.SubElement(trk, \"trkseg\")\n    \n    with open(rec_file) as csv_file:\n        csv_reader = csv.reader(csv_file, delimiter=',')\n        line_count = 0\n        for row in csv_reader:\n            ET.SubElement(trkseg, \"trkpt\",lat = row[1], lon = row[2])\n    tree = ET.ElementTree(gpx_root)\n    tree.write(os.path.join(record_path, \"path_%s_%d.gpx\"%(task_id, drone_id)))\n\n\ndef record_update(task_id, drone_id, drone_update, record_file):\n    # record gps data in gpx format\n    with open(record_file, 'a', newline='') as rec:\n        c_writer = csv.writer(rec)\n        c_writer.writerow([drone_id, drone_update.lat, \n                        drone_update.lon, drone_update.height, \n                        drone_update.battery_power, drone_update.obstacle,\n                        drone_update.sig_str])\n\n    # print the parameters\n    print(\"Drone:{} -> latitude={}, longitude={}, height={}, battery-power={}, obstacle={}, signal_strength={}.\".format(drone_id, drone_update.lat, \n                                    drone_update.lon, drone_update.height, \n                                    drone_update.battery_power, drone_update.obstacle,\n                                    drone_update.sig_str))\n\ndef get_base_server_conf(server_config, server_id):\n    with open(server_config) as sc:\n        servers = json.load(sc)\n        for server in servers['base_servers']:\n            if server['id'] == server_id:\n                return server\n    return None\n\ndef get_server_for_loc(init_pos):\n    with open(server_config) as sc:\n        servers = json.load(sc)\n        for server in servers['base_servers']:\n            if abs(server['location']['lat'] - init_pos[0]) < 10e-6 and abs(server['location']['lon'] - init_pos[1]) < 10e-6:\n                return server\n    return None\n\ndef send_notification(dest_loc, curr_loc, drone_id):\n\n    try:        \n        server = get_server_for_loc(dest_loc)\n        bl_addr = (server['ip'], server['sync_port'])\n        # Create a socket\n        sync_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        sync_sock.connect(bl_addr)\n\n        print(\"Successfully connected to server %s:%d\"%(bl_addr[0], bl_addr[1]))\n\n        # send init message\n        drone_init = BaseSync(drone_id, curr_loc[0], curr_loc[1])\n        sync_sock.send(drone_init)\n\n        return True\n\n    except Exception as e:\n        print(\"Failed to notification!!\")\n        return False\n\ndef handle_client(conn, address, task_queue, conflict_mgr, server):\n    print(\"Connected to \", address)\n\n    ack_ok = Ack(True)\n    ack_fail = Ack(False)\n\n    # start communication protocol\n    try:\n        # receive first drone connection message\n        buff = conn.recv(sizeof(DroneConnect))\n        drone_init = DroneConnect.from_buffer_copy(buff)\n        \n        # send acknowledgement\n        conn.send(ack_ok)\n\n        print(\"<- Registered drone: %d ->\", drone_init.drone_id)\n\n        # Stay in loop till a task is accepted\n        while True:\n            if is_exit(drone_init.drone_id):\n                return\n\n            # if no task then wait\n            if not task_queue.task_exists():\n                time.sleep(task_wait_sleep_time)\n                continue\n            \n            # send task to initiate the task\n            task = task_queue.get_task()\n\n            conn.send(task)\n\n            # wait for acknowledgement\n            buff = conn.recv(sizeof(Ack))\n            ack_rec = Ack.from_buffer_copy(buff)\n\n            # if task assignment fails, release the task for other drones\n            if not ack_rec.is_accepted:\n                print(\"Drone:%d -> Task [%d] rejected.\"%(drone_init.drone_id, task.task_id))\n                task_queue.add_task(task)\n                continue\n            else:\n                print(\"Drone:%d -> Task [%d] accepted.\"%(drone_init.drone_id, task.task_id))\n                break\n            \n        # recieve live updates from client\n        record_file = os.path.join(record_path,\n                \"simulation_%s_%d_%s.csv\"%(task.task_id, \n                drone_init.drone_id, \n                datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")))\n        \n        # register for conflict alert\n        dest_loc = (task.dest_lat, task.dest_lon)\n        conflict_mgr.add_drone(drone_init.drone_id, dest_loc)\n        \n        #send sync notification to destination\n        send_notification(dest_loc, (server['location']['lat'], server['location']['lon']), drone_init.drone_id)\n        \n        while True:\n            buff = conn.recv(sizeof(DroneUpdate))\n            drone_update = DroneUpdate.from_buffer_copy(buff)\n            # task complete close connection\n            record_update(task.task_id, drone_init.drone_id, drone_update, record_file)\n            \n            # send alers if any\n            is_alert, drone_id = conflict_mgr.get_alert(drone_init.drone_id)\n            base_update = BaseUpdate(is_alert, drone_id)\n            conn.send(base_update)\n            \n            if drone_update.is_complete:\n                writeGPXDom(record_file, task.task_id, drone_init.drone_id)\n                print(\"Drone:%d -> Task completed successfully.\"%(drone_init.drone_id))\n                conflict_mgr.remove_drone(drone_init.drone_id)\n                return\n\n    except BlockingIOError:\n        print(\"socket is open and reading from it would block\")\n    except ConnectionResetError:\n        print(\"socket was closed\")\n    except Exception as e:\n        print(\"Unexpected Exception: Closing the connection to the Drone.\")\n    \n    return\n\n#start drone server\ndef start_drone_server(server, task_queue, conflict_mgr):\n    addr = (server['ip'], server['drone_port'])\n\n    serverSocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    serverSocket.bind(addr)\n\n    print(\"Starting base_loc server on [{}] at port {}\".format(server['ip'], server['drone_port']))\n    try:\n        serverSocket.listen()\n        while True:\n            conn, address = serverSocket.accept()\n            thread = threading.Thread(target=handle_client, args=(conn, address, task_queue, conflict_mgr, server))\n            thread.start()\n    except Exception as e:\n        serverSocket.close()\n\n#thread to listen to updates from base locs\ndef start_sync_server(server, port, conflict_mgr):\n    \n    addr = (server, port)\n\n    serverSocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    serverSocket.bind(addr)\n\n    print(\"Starting sync server on [{}] at port {}\".format(server, port))\n    try:\n        serverSocket.listen()\n        while True:\n            conn, address = serverSocket.accept()\n            buff = conn.recv(sizeof(BaseSync))\n            sync_msg = BaseSync.from_buffer_copy(buff)\n            conflict_mgr.add_alert((sync_msg.src_lat, sync_msg.src_lon), sync_msg.drone_id)\n            conn.close()\n    except Exception as e:\n        serverSocket.close()\n\ndef start_task_sim(task_queue, server_id):\n    while True:\n        if server_id == 1:\n            task_queue.add_task(BaseTask(1,5,default_dest1[0], default_dest1[1]))\n        elif server_id == 2:\n            task_queue.add_task(BaseTask(2,5,default_dest2[0], default_dest2[1]))\n        elif server_id == 3:\n            task_queue.add_task(BaseTask(3,5,default_dest2[0], default_dest2[1]))\n        time.sleep(310)\n\ndef main():\n    global record_path\n    global server_config\n\n    #define the argumes required to be passed for starting the server on base location\n    parser = argparse.ArgumentParser()\n    \n    parser.add_argument('--server-id', help='Assign a unique id for this drone', type=int)\n    parser.add_argument('--server-config', help='Server Configuration file', type=str)\n    parser.add_argument('--record-dir', help='Driectory to store the simulation records', type=str)\n    args = parser.parse_args()\n\n    if args.server_id is None:\n        print(\"--server-id  is necessary\")\n        exit(1)\n\n    if args.server_config is None:\n        print(\"--server-config  is necessary\")\n        exit(1)\n    else:\n        server_config = args.server_config\n\n    if args.record_dir is not None:\n        record_path = args.record_dir\n\n    if not os.path.exists(record_path):\n        os.mkdir(record_path)\n    \n    server = get_base_server_conf(server_config, args.server_id)\n\n    task_queue = TaskQueue()\n    conflict_mgr = ConflictMgr()\n\n    #start a task queue thread\n    thread_task_sim = threading.Thread(target=start_task_sim, args=(task_queue, args.server_id))\n    thread_task_sim.start()\n\n    # start drone server\n    thread_drone = threading.Thread(target=start_drone_server, args=(server, task_queue, conflict_mgr))\n    thread_drone.start()\n\n    # start sync server in the main thread\n    start_sync_server(server['ip'], server['sync_port'], conflict_mgr)\n\n    return\n\nif __name__ == \"__main__\":\n    main()", "sub_path": "integrated/base_loc.py", "file_name": "base_loc.py", "file_ext": "py", "file_size_in_byte": 11781, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "threading.Lock", "line_number": 28, "usage_type": "call"}, {"api_name": "threading.Lock", "line_number": 47, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree.Element", "line_number": 93, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree", "line_number": 93, "usage_type": "name"}, {"api_name": "xml.etree.cElementTree.SubElement", "line_number": 98, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree", "line_number": 98, "usage_type": "name"}, {"api_name": "xml.etree.cElementTree.SubElement", "line_number": 99, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree", "line_number": 99, "usage_type": "name"}, {"api_name": "xml.etree.cElementTree.SubElement", "line_number": 100, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree", "line_number": 100, "usage_type": "name"}, {"api_name": "xml.etree.cElementTree.SubElement", "line_number": 101, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree", "line_number": 101, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 104, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree.SubElement", "line_number": 107, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree", "line_number": 107, "usage_type": "name"}, {"api_name": "xml.etree.cElementTree.ElementTree", "line_number": 108, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree", "line_number": 108, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path", "line_number": 109, "usage_type": "attribute"}, {"api_name": "csv.writer", "line_number": 115, "usage_type": "call"}, {"api_name": "json.load", "line_number": 129, "usage_type": "call"}, {"api_name": "json.load", "line_number": 137, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 149, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 149, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 149, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 188, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 210, "usage_type": "call"}, {"api_name": "os.path", "line_number": 210, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 213, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 213, "usage_type": "attribute"}, {"api_name": "socket.socket", "line_number": 252, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 252, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 252, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 260, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 270, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 270, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 270, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 293, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 300, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 320, "usage_type": "call"}, {"api_name": "os.path", "line_number": 320, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 321, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 329, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 333, "usage_type": "call"}]}
+{"seq_id": "14351201", "text": "from collections import OrderedDict\nfrom itertools import zip_longest\nfrom typing import Iterable, Sequence\n\nfrom bs4 import Tag\nfrom requests import Response\n\nfrom rets.http.data import Metadata, Object, SearchResult, SystemMetadata\nfrom rets.http.parsers.http import parse_xml, parse_response\nfrom rets.errors import RetsParseError, RetsApiError\n\n\ndef parse_capability_urls(response: Response) -> dict:\n    \"\"\"\n    Parses the list of capability URLs from the response of a successful Login transaction.\n\n    The capability url list is the set of functions or URLs to which the Login grants access.\n    A capability consists of a key and a URL. The list returned from the server in the login\n    reply must include URLs for Search, Login, and GetMetadata, and optionally may include\n    URLs for Action, ChangePassword, GetObject, LoginComplete, Logout, ServerInformation,\n    and Update.\n\n    \n        \n            MemberName=member_name\n            User=user_id,user_level,user_class,agent_code\n            Broker=RETSOFFIC\n            MetadataVersion=01.09.02991\n            MetadataTimestamp=2016-11-24T05:24:06Z\n            MinMetadataTimestamp=2016-11-24T05:24:06Z\n            Login=/rets2_1/Login\n            Search=/rets2_1/Search\n            GetMetadata=/rets2_1/GetMetadata\n            X-SampleLinks=/rets2_1/Links\n            X-SupportSite=http://flexmls.com/rets/\n            X-NotificationFeed=http://retsgw.flexmls.com/atom/feed/private/atom.xml\n            GetObject=/rets2_1/GetObject\n            Logout=/rets2_1/Logout\n            X-ApiAccessSettings=/rets2_1/API\n        \n    \n    \"\"\"\n    tag = parse_xml(response)\n    response_tag = tag.find('RETS-RESPONSE')\n    if response_tag is None:\n        return {}\n    raw_arguments = response_tag.text.strip().split('\\n')\n    return dict((s.strip() for s in arg.split('=', 1)) for arg in raw_arguments)\n\n\ndef parse_metadata(response: Response) -> Sequence[Metadata]:\n    \"\"\"\n    Parse the information from a GetMetadata transaction.\n\n    \n        \tResourceID\tStandardName\t\n        \tActiveAgent\tActiveAgent\t\n        \tOffice\tOffice\t\n        \tOpenHouse\tOpenHouse\t\n        \tProperty\tProperty\t\n        \tRentalSchedule\tRentalSchedule\t\n    \n    \"\"\"\n    tag = parse_xml(response)\n    metadata_tags = tag.find_all(lambda t: t.name.startswith('METADATA-'))\n    if metadata_tags is None:\n        return ()\n\n    def parse_metadata_tag(tag: Tag) -> Metadata:\n        \"\"\" Parses a single  tag \"\"\"\n        return Metadata(\n            type_=tag.name.split('-', 1)[1],\n            resource=tag.attrs.get('Resource'),\n            class_=tag.attrs.get('Class'),\n            data=tuple(_parse_data(tag)),\n        )\n\n    return tuple(parse_metadata_tag(metadata_tag) for metadata_tag in metadata_tags)\n\n\ndef parse_system(response: Response) -> SystemMetadata:\n    \"\"\"\n    Parse the server system information from a SYSTEM GetMetadata transaction.\n\n    \n        \n            \n            \n        \n    \n    \"\"\"\n    tag = parse_xml(response)\n    metadata_system_tag = _find_or_raise(tag, 'METADATA-SYSTEM')\n    system_tag = _find_or_raise(tag, 'SYSTEM')\n    comments_tag = metadata_system_tag.find('COMMENTS')\n    return SystemMetadata(\n        system_id=system_tag.attrs['SystemID'],\n        system_description=system_tag.attrs['SystemDescription'],\n        system_date=metadata_system_tag.attrs['Date'],\n        system_version=metadata_system_tag.attrs['Version'],\n\n        # Optional fields\n        time_zone_offset=system_tag.attrs.get('TimeZoneOffset'),\n        comments=comments_tag and (comments_tag.text or None),\n    )\n\n\ndef parse_search(response: Response) -> SearchResult:\n    try:\n        tag = parse_xml(response)\n    except RetsApiError as e:\n        if e.reply_code == 20201:  # No records found\n            return SearchResult(0, False, ())\n        raise\n\n    count_tag = tag.find('COUNT')\n    try:\n        data = tuple(_parse_data(tag))\n    except RetsParseError:\n        data = None\n    return SearchResult(\n        count=count_tag and int(count_tag.attrs['Records']),\n        max_rows=bool(tag.find('MAXROWS')),\n        data=data,\n    )\n\n\ndef parse_object(response: Response) -> Sequence[Object]:\n    return parse_response(response)\n\n\ndef _parse_data(tag: Tag) -> Iterable[dict]:\n    \"\"\"\n    Parses a generic container tag enclosing a single COLUMNS and multiple DATA tags, and returns\n    a generator of dicts with keys given by the COLUMNS tag and values given by each DATA tag.\n    The container tag may optionally contain a DELIMITER tag to define the delimiter used,\n    otherwise a default of '\\t' is assumed.\n\n    \n        \n        \tLIST_87\tLIST_105\tLIST_1\t\n        \t2016-12-01T00:08:10\t5489015\t20160824051756837742000000\t\n        \t2016-12-01T00:10:02\t5497756\t20160915055426038684000000\t\n        \t2016-12-01T00:10:26\t5528935\t20161123230848928777000000\t\n        \t2016-12-01T00:10:52\t5528955\t20161123234916869427000000\t\n        \t2016-12-01T00:14:31\t5530021\t20161127221848669500000000\t\n    \n    \"\"\"\n    delimiter = _parse_delimiter(tag)\n\n    columns_tag = _find_or_raise(tag, 'COLUMNS')\n    columns = _parse_data_line(columns_tag, delimiter)\n\n    data_tags = tag.find_all('DATA')\n\n    return (OrderedDict(zip_longest(columns, _parse_data_line(data, delimiter)))\n            for data in data_tags)\n\n\ndef _find_or_raise(tag: Tag, child_tag_name: str) -> Tag:\n    child = tag.find(child_tag_name)\n    if child is None:\n        raise RetsParseError('Missing %s tag' % child_tag_name)\n    return child\n\n\ndef _parse_data_line(tag: Tag, delimiter: str = '\\t') -> Sequence[str]:\n    # DATA tags using the COMPACT format and COLUMN tags all start and end with delimiters\n    return tag.text.split(delimiter)[1:-1]\n\n\ndef _parse_delimiter(tag: Tag) -> str:\n    delimiter_tag = tag.find('DELIMITER')\n    if delimiter_tag is None:\n        return '\\t'\n    return chr(int(delimiter_tag.attrs['value']))\n", "sub_path": "rets/http/parsers/parse.py", "file_name": "parse.py", "file_ext": "py", "file_size_in_byte": 6484, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "requests.Response", "line_number": 13, "usage_type": "name"}, {"api_name": "rets.http.parsers.http.parse_xml", "line_number": 43, "usage_type": "call"}, {"api_name": "requests.Response", "line_number": 51, "usage_type": "name"}, {"api_name": "rets.http.parsers.http.parse_xml", "line_number": 64, "usage_type": "call"}, {"api_name": "bs4.Tag", "line_number": 69, "usage_type": "name"}, {"api_name": "rets.http.data.Metadata", "line_number": 71, "usage_type": "call"}, {"api_name": "rets.http.data.Metadata", "line_number": 69, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 51, "usage_type": "name"}, {"api_name": "rets.http.data.Metadata", "line_number": 51, "usage_type": "name"}, {"api_name": "requests.Response", "line_number": 81, "usage_type": "name"}, {"api_name": "rets.http.parsers.http.parse_xml", "line_number": 92, "usage_type": "call"}, {"api_name": "rets.http.data.SystemMetadata", "line_number": 96, "usage_type": "call"}, {"api_name": "rets.http.data.SystemMetadata", "line_number": 81, "usage_type": "name"}, {"api_name": "requests.Response", "line_number": 108, "usage_type": "name"}, {"api_name": "rets.http.parsers.http.parse_xml", "line_number": 110, "usage_type": "call"}, {"api_name": "rets.errors.RetsApiError", "line_number": 111, "usage_type": "name"}, {"api_name": "rets.http.data.SearchResult", "line_number": 113, "usage_type": "call"}, {"api_name": "rets.errors.RetsParseError", "line_number": 119, "usage_type": "name"}, {"api_name": "rets.http.data.SearchResult", "line_number": 121, "usage_type": "call"}, {"api_name": "rets.http.data.SearchResult", "line_number": 108, "usage_type": "name"}, {"api_name": "requests.Response", "line_number": 128, "usage_type": "name"}, {"api_name": "rets.http.parsers.http.parse_response", "line_number": 129, "usage_type": "call"}, {"api_name": "typing.Sequence", "line_number": 128, "usage_type": "name"}, {"api_name": "rets.http.data.Object", "line_number": 128, "usage_type": "name"}, {"api_name": "bs4.Tag", "line_number": 132, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 156, "usage_type": "call"}, {"api_name": "itertools.zip_longest", "line_number": 156, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 132, "usage_type": "name"}, {"api_name": "bs4.Tag", "line_number": 160, "usage_type": "name"}, {"api_name": "rets.errors.RetsParseError", "line_number": 163, "usage_type": "call"}, {"api_name": "bs4.Tag", "line_number": 167, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 167, "usage_type": "name"}, {"api_name": "bs4.Tag", "line_number": 172, "usage_type": "name"}]}
+{"seq_id": "418122514", "text": "import os\nimport json\nimport numpy as np\nfrom word2vec import WordVectors\nimport tensorflow as tf\nfrom nltk.tokenize import sent_tokenize\n\nTEXT_FILE = \"./text.txt\"\nMODEL_PATH = \"./nlp.model\"\n\nword_vec = WordVectors()\ndef main():\n    model = tf.keras.models.load_model(MODEL_PATH)\n\n    f = open(TEXT_FILE, \"r\")\n    text = f.read()\n    textVec = word_vec.vectorize_string(text)\n    sentences = sent_tokenize(text)\n    sentVec = word_vec.vectorize_strings(sentences)\n\n    while True:\n        print(\"Question:\")\n        question = input()\n        quesVec = word_vec.vectorize_string(question)\n\n        results = []\n        for i, sent in enumerate(sentVec):\n            res = model.predict(np.array([[quesVec, sent, textVec]]))[0]\n            results.append(res[1] - res[0])\n        results = np.array(results)\n        for i in range(3):\n            index = np.argmax(results)\n            results[index] = -1 \n            print(f\"{i}: {sentences[index]}\")\n\n\nif __name__ == \"__main__\":\n    main()", "sub_path": "demo/text_search.py", "file_name": "text_search.py", "file_ext": "py", "file_size_in_byte": 991, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "word2vec.WordVectors", "line_number": 11, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.load_model", "line_number": 13, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 13, "usage_type": "attribute"}, {"api_name": "nltk.tokenize.sent_tokenize", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 32, "usage_type": "call"}]}
+{"seq_id": "480446469", "text": "#!/usr/bin/env Python\n# coding=utf-8\n\nfrom selenium import webdriver\nfrom time import sleep\n\ndriver=webdriver.Firefox()\ndriver.get(\"http://www.51zxw.net/list.aspx?cid=615\")\n\n# 获取窗口句柄\nselenium_index=driver.current_window_handle\nsleep(2)\n\n# 点击2-1课程链接，进入课程详情页面\ndriver.find_element_by_partial_link_text('2-1').click()\nsleep(4)\n\n# 跳转到课程主页，点击3-1课程\ndriver.switch_to.window(selenium_index)\nsleep(4)\ndriver.find_element_by_partial_link_text('3-1').click()\nsleep(3)\n\ndriver.quit()", "sub_path": "webdriver/4-22~4-32/Multi-window switch.py", "file_name": "Multi-window switch.py", "file_ext": "py", "file_size_in_byte": 536, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "selenium.webdriver.Firefox", "line_number": 7, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 7, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 12, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 16, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 20, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 22, "usage_type": "call"}]}
+{"seq_id": "415618669", "text": "import json\nfrom flask import Blueprint, request\nfrom Controller.API import Controllers\nfrom Model.Gateway.ChartGateway import ChartGateway\nfrom Model.Object.DataTransferObject import DataTransferObject\n\nChartController = Blueprint('ChartController', __name__)\n\n#Get All Charts\n@ChartController.route('/api/Charts/', methods=['GET'])\ndef getCharts():\n    return json.dumps((ChartGateway().getAllCharts()), default=Controllers.default)\n\n#Get Chart by ID\n@ChartController.route('/api/Charts/View/', methods=['GET'])\ndef getChart(ID):\n    if ID is None:\n        abort(404)\n    else:\n        return json.dumps((ChartGateway().getChart(ID)), default=Controllers.default)\n\n#Create Chart\n@ChartController.route('/api/Charts/Create/', methods=['POST'])\ndef createChart():\n    ChartDTO = DataTransferObject()\n    ChartDTO.ID = 0;\n    ChartDTO.Name = request.form['name']\n    ChartDTO.Description = request.form['description']\n    ChartDTO.Server = request.form['server']\n    ChartDTO.Query = request.form['query']\n    ChartDTO.Dependent = request.form['dependent']\n    ChartDTO.Independent = request.form['independent']\n    return json.dumps(ChartGateway().addChart(ChartDTO), default=Controllers.default)\n", "sub_path": "src/Controller/API/ChartController.py", "file_name": "ChartController.py", "file_ext": "py", "file_size_in_byte": 1205, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "flask.Blueprint", "line_number": 7, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 12, "usage_type": "call"}, {"api_name": "Model.Gateway.ChartGateway.ChartGateway", "line_number": 12, "usage_type": "call"}, {"api_name": "Controller.API.Controllers.default", "line_number": 12, "usage_type": "attribute"}, {"api_name": "Controller.API.Controllers", "line_number": 12, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 20, "usage_type": "call"}, {"api_name": "Model.Gateway.ChartGateway.ChartGateway", "line_number": 20, "usage_type": "call"}, {"api_name": "Controller.API.Controllers.default", "line_number": 20, "usage_type": "attribute"}, {"api_name": "Controller.API.Controllers", "line_number": 20, "usage_type": "name"}, {"api_name": "Model.Object.DataTransferObject.DataTransferObject", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 28, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 30, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 31, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 31, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 32, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 32, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 33, "usage_type": "call"}, {"api_name": "Model.Gateway.ChartGateway.ChartGateway", "line_number": 33, "usage_type": "call"}, {"api_name": "Controller.API.Controllers.default", "line_number": 33, "usage_type": "attribute"}, {"api_name": "Controller.API.Controllers", "line_number": 33, "usage_type": "name"}]}
+{"seq_id": "400082805", "text": "import os, sys\n\nfrom bamboo.rdkit_utils.mol import check_pdb_readable\n\nfrom rdkit import Chem\n\ndef get_centroid_from_file(model):\n    \"\"\"Get the centroid of an isolated ligand\"\"\"\n    return get_centroid_from_mol(Chem.MolFromPDBFile(model))\n\ndef get_centroid_from_mol(mol):\n    \"\"\"Get the centroid of a mol\"\"\"\n\n    num_atm = mol.GetNumHeavyAtoms()\n    # Get the conformer (actual coords)\n    conf = mol.GetConformer()\n    # Get the atom objects\n    atoms = mol.GetAtoms()\n    # Zero the sums\n    sumx, sumy, sumz, count = (0, 0, 0, 0)\n\n    for a in atoms:\n        # Skip Hydrogens (should have been removed anyway, but...)\n        if a.GetAtomicNum() == 1:\n            continue\n        # Get the 3D coords of the atom\n        pos = conf.GetAtomPosition(a.GetIdx())\n        # Number of atoms used\n        count += 1\n        # Add the coords to the sum\n        sumx += pos.x\n        sumy += pos.y\n        sumz += pos.z\n\n    # Check we've got as many as we were expecting\n    assert count == num_atm, 'Number of atoms used to calculate centroid != number of heavy atoms'\n\n    return (sumx/count,sumy/count,sumz/count)\n\ndef calculate_coordinate_differences(model1, model2):\n    \"\"\"Calculate the differences between the atom coordinates of two identical structures\"\"\"\n\n    # Read in mols and check for validity\n    mol1 = check_pdb_readable(model1)\n    mol2 = check_pdb_readable(model2)\n    if (not mol1) or (not mol2):\n        return None\n\n    # Check that the mols are identical-ish\n    if mol1.GetNumHeavyAtoms() != mol2.GetNumHeavyAtoms():\n        raise EqualityError('Molecules are not identical (Num Atoms) {!s} != {!s}.\\n{!s}\\n{!s}'.format(mol1.GetNumHeavyAtoms(),mol2.GetNumHeavyAtoms(),Chem.MolToSmiles(mol1),Chem.MolToSmiles(mol2)))\n    if mol1.GetNumBonds() != mol2.GetNumBonds():\n        raise EqualityError('Molecules are not identical (Num Bonds) {!s} != {!s}:\\n{!s}\\n{!s}'.format(mol1.GetNumBonds(),mol2.GetNumBonds(),Chem.MolToSmiles(mol1), Chem.MolToSmiles(mol2)))\n\n    # Gets atoms in mol1 (e.g. 14,5,3...) that match mol2 (1,2,3...)\n    matchpatterns = mol1.GetSubstructMatches(mol2, uniquify=False)\n    # Check to see if the molecules actually DO contain common substructures\n    if not matchpatterns: return None\n\n    differences = []\n    # Get the conformers to access the coords\n    conf1 = mol1.GetConformer(0)\n    conf2 = mol2.GetConformer(0)\n    # May be more than one matching pattern. Calculate all of them.\n    for matchlist in matchpatterns:\n        # reset the vector of coord difference for each match pattern\n        match_diffs = []\n        # idx2 = 0,1,2... idx1 = 14,5,3...\n        for idx2, idx1 in enumerate(matchlist):\n            # Get the atom coords\n            atm1 = conf1.GetAtomPosition(idx1)\n            atm2 = conf2.GetAtomPosition(idx2)\n            # Append tuple of differences\n            match_diffs.append((atm1.x - atm2.x, atm1.y - atm2.y, atm1.z - atm2.z))\n        differences.append(match_diffs)\n    # Return the differences corresponding to all of the ways of matching the molecules\n    return differences\n\ndef get_atomic_equivalences(mol1, mol2):\n    \"\"\"Returns the list of atoms in mol1 that match with the atoms in mol2 => [pattern1, pattern2] ... pattern1 = [(mol1atm, mol2atm), ...]\"\"\"\n\n    # Get the match patterns between mol1, mol2\n    match_patterns = mol1.GetSubstructMatches(mol2, uniquify=False)\n\n    # List of paired atoms in mol1 -> mol2\n    atom_pairings = []\n\n    if mol1.GetNumHeavyAtoms() != mol2.GetNumHeavyAtoms():\n        raise EqualityError('Heavy Atom Numbers are not equal!')\n\n    for match_pattern in match_patterns:\n        atom_pairings.append(zip(match_pattern,range(0,mol1.GetNumHeavyAtoms())))\n\n    return atom_pairings\n\n", "sub_path": "build/lib/bamboo/rdkit_utils/coords/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 3702, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "rdkit.Chem.MolFromPDBFile", "line_number": 9, "usage_type": "call"}, {"api_name": "rdkit.Chem", "line_number": 9, "usage_type": "name"}, {"api_name": "bamboo.rdkit_utils.mol.check_pdb_readable", "line_number": 44, "usage_type": "call"}, {"api_name": "bamboo.rdkit_utils.mol.check_pdb_readable", "line_number": 45, "usage_type": "call"}, {"api_name": "rdkit.Chem.MolToSmiles", "line_number": 51, "usage_type": "call"}, {"api_name": "rdkit.Chem", "line_number": 51, "usage_type": "name"}, {"api_name": "rdkit.Chem.MolToSmiles", "line_number": 53, "usage_type": "call"}, {"api_name": "rdkit.Chem", "line_number": 53, "usage_type": "name"}]}
+{"seq_id": "234807045", "text": "#!/usr/bin/env python3\nimport collections\n\n\ndef main():\n    N = int(input())\n    S = input()\n    RGB = ('R', 'G', 'B')\n    cnt = collections.Counter(S)\n    ans = cnt['R'] * cnt['G'] * cnt['B']\n\n    for i in range(N - 2):\n        for j in range(i + 1, N - 1):\n            if S[i] != S[j]:\n                if 2 * j - i < N:\n                    if S[2 * j - i] == RGB[3 - RGB.index(S[i]) - RGB.index(S[j])]:\n                        ans -= 1\n\n    print(ans)\n\n\nif __name__ == \"__main__\":\n    main()\n", "sub_path": "Python_codes/p02714/s538666801.py", "file_name": "s538666801.py", "file_ext": "py", "file_size_in_byte": 494, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "collections.Counter", "line_number": 9, "usage_type": "call"}]}
+{"seq_id": "126050966", "text": "from django.urls import path\n\nimport run_make.views.views    as vs\nimport run_make.views.examples as vx\n\n\napp_name = 'run_make'\n\nurlpatterns = [\n    path( 'write_time',\n          vx.write_time,\n          name='write_time'),\n\n    path( 'ingest_full_spec',\n          vs.ingest_full_spec,\n          name='ingest_full_spec'),\n\n    path( 'thank-for-spec/',\n          vs.thank_for_spec,\n          name='thank-for-spec'),\n\n    path( 'download',\n          vx.download,\n          name='download'),\n\n    path( \"upload_multiple_with_logging\",\n          vx.upload_multiple_with_logging,\n          name=\"upload_multiple_with_logging\" ),\n\n    path( 'upload_and_show_url',\n           vx.upload_and_show_url,\n           name=\"upload_and_show_url\" ),\n\n    path( 'upload_multiple',\n           vx.upload_multiple,\n           name=\"upload_multiple\" )\n]\n", "sub_path": "django/run_make/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 845, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "run_make.views.examples.write_time", "line_number": 11, "usage_type": "attribute"}, {"api_name": "run_make.views.examples", "line_number": 11, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "run_make.views.views.ingest_full_spec", "line_number": 15, "usage_type": "attribute"}, {"api_name": "run_make.views.views", "line_number": 15, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "run_make.views.views.thank_for_spec", "line_number": 19, "usage_type": "attribute"}, {"api_name": "run_make.views.views", "line_number": 19, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "run_make.views.examples.download", "line_number": 23, "usage_type": "attribute"}, {"api_name": "run_make.views.examples", "line_number": 23, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "run_make.views.examples.upload_multiple_with_logging", "line_number": 27, "usage_type": "attribute"}, {"api_name": "run_make.views.examples", "line_number": 27, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 30, "usage_type": "call"}, {"api_name": "run_make.views.examples.upload_and_show_url", "line_number": 31, "usage_type": "attribute"}, {"api_name": "run_make.views.examples", "line_number": 31, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 34, "usage_type": "call"}, {"api_name": "run_make.views.examples.upload_multiple", "line_number": 35, "usage_type": "attribute"}, {"api_name": "run_make.views.examples", "line_number": 35, "usage_type": "name"}]}
+{"seq_id": "225897148", "text": "#将GA在自适应提取上复现\n#然后将select功能用RL方式复现\n\nimport numpy as np\nimport cv2\nimport targetTacing as TT \nimport matplotlib.pyplot as plt\nimport datetime\n\nDNA_SIZE = 6\nPOP_SIZE = 100\nN_GENERATION = 20\nCROSS_RATE = 0.8 \nMUTATION_RATE = 0.003\n\nDNA_BOUND1 = [0.01,0.1] # for qualityLevel\nDNA_BOUND2 = [0,100] # for minDistance,winSize,maxlevel,COUNT\nDNA_BOUND3 = [0,1] #for EPS\n\ndef get_fitness(pop):\n\t#store results from targetTracing\n\tfitness = np.empty(POP_SIZE)\n\t#get the fitness\n\tfor i in range(POP_SIZE):\n\t\t#translate the DNA into params\n\t\tfeature_params = dict(maxCorners = 1000,\n\t\t\tqualityLevel = pop[i][0],\n\t\t\tminDistance = pop[i][1],\n\t\t\tblockSize = 3\n\t\t\t)\n\t\tlk_params = dict(winSize = (int(pop[i][2]),int(pop[i][2])),\n\t\t\tmaxLevel = int(pop[i][3]),\n\t\t\tcriteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT,int(pop[i][4]),pop[i][5])\n\t\t\t)\n\t\t#get the tracing result from tragetTrace func as fitness\n\t\tmse = TT.targetTrace(feature_params,lk_params)\n\t\t#print(mse)\n\t\tfitness[i] = mse\n\treturn fitness\n\ndef select(pop,fitness):\n\t#Survival of the fittest \n\t#the param is True,so each time the selection is from the whole size\n\tidx = np.random.choice(POP_SIZE,POP_SIZE,True,fitness/fitness.sum())\n\treturn pop[idx]\n\ndef crossover(parent,pop):\n\tif np.random.rand() < CROSS_RATE:\n\t\t#random choose one pop to cross with the current \n\t\ti_ = np.random.randint(0,POP_SIZE,1)\n\t\tchoice =np.random.randint(0,2,size = DNA_SIZE).astype(np.bool)\n\t\tmom = pop[i_].flatten()\n\t\tparent[choice] = mom[choice]\n\treturn parent \n\ndef mutate(child):\n\t#even if the DNA has mutated,it still has its own bound\n\tfor i,dna in enumerate(child):\n\t\tif np.random.rand() < MUTATION_RATE:\n\t\t\t#so each one mutate on its own way\n\t\t\tif i == 0:\n\t\t\t\tdna = np.random.rand()/10\n\t\t\telif i == 2:\n\t\t\t\tdna = np.random.randint(3,100,1)\n\t\t\telif i == 5:\n\t\t\t\tdna = np.random.rand()\n\t\t\telse:\n\t\t\t\tdna = np.random.randint(0,100,1)\n\treturn child\n\ndef create():\n\t#create a module for DNA\n\tdna_mod = np.empty((POP_SIZE,DNA_SIZE))\n\t#each DNA has its own bound\n\tfor i in range(POP_SIZE):\n\t\t#qualityLevel [0.01,0.1]\n\t\tdna_mod[i][0] = np.random.rand()/10\n\t\t\n\t\t#mindistance  [0,100] \n\t\tdna_mod[i][1] = np.random.randint(0,100,1)\n\t\t\n\t\t#winSize  (2,100]\n\t\tdna_mod[i][2] = np.random.randint(3,100,1)\n\t\t\n\t\t#maxlevel,COUNT [0,100]\n\t\tdna_mod[i][3] = np.random.randint(0,100,1)\n\t\tdna_mod[i][4] = np.random.randint(0,100,1)\n\t\t\n\t\t#EPS [0,1]\n\t\tdna_mod[i][5] = np.random.rand()\n\t\n\treturn dna_mod\n\nif __name__ == '__main__':\n\tstarttime = datetime.datetime.now()\n\tnp.seterr(divide='ignore',invalid='ignore')\n\tpop = create()\n\tmax_found = -9999\n\tfor _ in range(N_GENERATION+1):\n\t\t\n\t\tfitness = get_fitness(pop)\n\t\tif _ % 10 == 0:\n\t\t\tlength = fitness.shape[0]\n\t\t\t#print(length)\n\t\t\tx = range(length)\n\t\t\tplt.plot(x,fitness)\n\t\t\tplt.show()\n\t\t\tif _ == N_GENERATION:\n\t\t\t\tbreak\n\t\t\n\t\t#find the maxMatch params\n\t\tidx = fitness.argmax()\n\t\tif(fitness[idx] > max_found):\n\t\t\tmax_found = fitness[idx]\n\t\t\tmax_param = pop[idx][:]\n\n\t\tprint(\"max match:\",max_found)\n\t\tprint(\"params:\",max_param)\n\n\t\tfor i,item in enumerate(fitness):\n\t\t\tif item < (-5000):\n\t\t\t\tfitness[i] = 0\n\n\t\tpop = select(pop,fitness)\n\n\t\tpop_copy = pop.copy()\n\n\t\tfor parent in pop:\n\t\t\tchild = crossover(parent,pop_copy)\n\t\t\tchild = mutate(child)\n\t\t\tparent = child\n\n\tendtime = datetime.datetime.now()\n\tprint('runtime:',(endtime - starttime).seconds)\n", "sub_path": "TEST/traditionalGA.py", "file_name": "traditionalGA.py", "file_ext": "py", "file_size_in_byte": 3345, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.empty", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.TERM_CRITERIA_EPS", "line_number": 33, "usage_type": "attribute"}, {"api_name": "cv2.TERM_CRITERIA_COUNT", "line_number": 33, "usage_type": "attribute"}, {"api_name": "targetTacing.targetTrace", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 48, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 50, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 51, "usage_type": "attribute"}, {"api_name": "numpy.bool", "line_number": 51, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 59, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 62, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 64, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 66, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 68, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 77, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 80, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 83, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 86, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 87, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 90, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 95, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 95, "usage_type": "attribute"}, {"api_name": "numpy.seterr", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 133, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 133, "usage_type": "attribute"}]}
+{"seq_id": "649853756", "text": "\"\"\"\nGiven a matrix of M x N elements (M rows, N columns), return all elements of the\nmatrix in diagonal order as shown in the below image.\n\"\"\"\nclass Solution(object):\n    def findDiagonalOrder(self, matrix):\n        \"\"\"\n        :type matrix: List[List[int]]\n        :rtype: List[int]\n        \"\"\"\n        if not matrix: return matrix\n        m, n = len(matrix), len(matrix[0])\n        def traverse(matrix, i, j, up): # up 1, down 0\n            self.res.append(matrix[i][j])\n            if i == m - 1 and j == n - 1:\n                return\n            if up:\n                if 0 <= i - 1 and j + 1 < n:\n                    traverse(matrix, i - 1, j + 1, 1)\n                elif j + 1 < n:\n                    traverse(matrix, i, j + 1, 0)\n                else:\n                    traverse(matrix, i + 1, j, 0)\n            else:\n                if i + 1 < m and 0 <= j - 1:\n                    traverse(matrix, i + 1, j - 1, 0)\n                elif i + 1 < m:\n                    traverse(matrix, i + 1, j, 1)\n                else:\n                    traverse(matrix, i, j + 1, 1)\n\n        self.res = []\n        traverse(matrix, 0, 0, 1)\n        return self.res\n\n    def findDiagonalOrder2(self, matrix):\n        if not matrix: return matrix\n        r, c = 0, 0\n        m, n  = len(matrix), len(matrix[0])\n        A = [0] * (m * n)\n        for i in range(len(A)):\n            A[i] = matrix[r][c]\n            if (r + c) % 2 == 0: # if even sum moving up\n                if c == n - 1:\n                    r += 1\n                elif r == 0:\n                    c += 1\n                else:\n                    r -= 1\n                    c += 1\n            else:\n                if r == m - 1:\n                    c += 1\n                elif c == 0:\n                    r += 1\n                else:\n                    r += 1\n                    c -= 1\n        return A\n\n\n\n    def findDiagonalOrder3(self, matrix):\n        # Assemble all the diagonal into a deque then merge them\n        from collections import deque, defaultdict\n        if matrix == []:\n            return []\n        M, N = len(matrix), len(matrix[0])\n        result = defaultdict(deque)\n        max_sum, top_down = M + N - 2, True\n        for i in range(M):\n            for j in range(N):\n                s = i + j\n                if s & 1:\n                    result[s].append(matrix[i][j])\n                else:\n                    result[s].appendleft(matrix[i][j])\n        output = []\n        for s in range(max_sum + 1):\n            output.extend(result[s])\n        return output\n\n\n# matrix = [\n#  [ 1, 2, 3 ],\n#  [ 4, 5, 6 ],\n#  [ 7, 8, 9 ]\n# ]\nmatrix = [[2,5],[8,4],[0,-1]]\nprint(Solution().findDiagonalOrder3(matrix))", "sub_path": "498DiagonalTraverse.py", "file_name": "498DiagonalTraverse.py", "file_ext": "py", "file_size_in_byte": 2693, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "collections.defaultdict", "line_number": 69, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 69, "usage_type": "name"}, {"api_name": "{'deque': 'collections.deque', 'defaultdict': 'collections.defaultdict'}", "line_number": 90, "usage_type": "call"}]}
+{"seq_id": "653367194", "text": "from django.conf.urls import url\nfrom django.urls import path\nfrom api.views import movie_views, rating_views, auth_views, clustering_views\n\nurlpatterns = [\n    # 초기 setup을 위한 url\n    url('auth/signup-many/$', auth_views.signup_many, name='sign_up_many'),\n    url('auth/signup/$', auth_views.signup, name='sign_up'),\n    url('rating_many/$', rating_views.rating_many, name='rating_list'),\n\n    url('cluster/user', clustering_views.cluster_user_method, name=\"cluster_user_method\"),\n    url('cluster/movie', clustering_views.cluster_movie_method, name=\"cluster_movie_method\"),\n    url('cluster/custom', clustering_views.user_customized_recommendation, name=\"user_customized_recommendation\"),\n\n    url('cluster/', clustering_views.setup, name=\"cluster_setup\"),\n\n    url('movies/$', movie_views.movies, name='movie_list'),\n\n    url('rating/$', rating_views.rating, name='rating'),\n\n    path('profile//newmovies', auth_views.profileUnRatedMovieSearch, name='profileUnRatedMovieSearch'),\n    path('profile/', auth_views.profile, name='profile'),\n    path('profile/', auth_views.profileSearch, name='profileSearch'),\n    url('auth/login', auth_views.userLogin, name='login'),\n    url('auth/logout', auth_views.userLogout, name='logout'),\n\n\n    path('subscription/', auth_views.subscription, name=\"subscription\"),\n]\n", "sub_path": "backend/api/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1357, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "api.views.auth_views.signup_many", "line_number": 7, "usage_type": "attribute"}, {"api_name": "api.views.auth_views", "line_number": 7, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "api.views.auth_views.signup", "line_number": 8, "usage_type": "attribute"}, {"api_name": "api.views.auth_views", "line_number": 8, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "api.views.rating_views.rating_many", "line_number": 9, "usage_type": "attribute"}, {"api_name": "api.views.rating_views", "line_number": 9, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "api.views.clustering_views.cluster_user_method", "line_number": 11, "usage_type": "attribute"}, {"api_name": "api.views.clustering_views", "line_number": 11, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "api.views.clustering_views.cluster_movie_method", "line_number": 12, "usage_type": "attribute"}, {"api_name": "api.views.clustering_views", "line_number": 12, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "api.views.clustering_views.user_customized_recommendation", "line_number": 13, "usage_type": "attribute"}, {"api_name": "api.views.clustering_views", "line_number": 13, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "api.views.clustering_views.setup", "line_number": 15, "usage_type": "attribute"}, {"api_name": "api.views.clustering_views", "line_number": 15, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "api.views.movie_views.movies", "line_number": 17, "usage_type": "attribute"}, {"api_name": "api.views.movie_views", "line_number": 17, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 19, "usage_type": "call"}, {"api_name": "api.views.rating_views.rating", "line_number": 19, "usage_type": "attribute"}, {"api_name": "api.views.rating_views", "line_number": 19, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "api.views.auth_views.profileUnRatedMovieSearch", "line_number": 21, "usage_type": "attribute"}, {"api_name": "api.views.auth_views", "line_number": 21, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "api.views.auth_views.profile", "line_number": 22, "usage_type": "attribute"}, {"api_name": "api.views.auth_views", "line_number": 22, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "api.views.auth_views.profileSearch", "line_number": 23, "usage_type": "attribute"}, {"api_name": "api.views.auth_views", "line_number": 23, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 24, "usage_type": "call"}, {"api_name": "api.views.auth_views.userLogin", "line_number": 24, "usage_type": "attribute"}, {"api_name": "api.views.auth_views", "line_number": 24, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 25, "usage_type": "call"}, {"api_name": "api.views.auth_views.userLogout", "line_number": 25, "usage_type": "attribute"}, {"api_name": "api.views.auth_views", "line_number": 25, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 28, "usage_type": "call"}, {"api_name": "api.views.auth_views.subscription", "line_number": 28, "usage_type": "attribute"}, {"api_name": "api.views.auth_views", "line_number": 28, "usage_type": "name"}]}
+{"seq_id": "32724431", "text": "import collections\nimport functools\nimport json\nimport logging\nfrom collections import OrderedDict\nfrom typing import Dict, List\n\nimport tensorflow as tf\n\nfrom seq2annotation.utils import class_from_module_path, load_hook\nfrom tokenizer_tools.tagset.converter.offset_to_biluo import offset_to_biluo\nfrom tokenizer_tools.tagset.NER.BILUO import BILUOEncoderDecoder\n\nlogger = logging.getLogger(__name__)\n\n\nclass Lookuper(object):\n    def __init__(self, index_table: Dict[str, int]):\n        # index_table: str -> int, ordered by key\n        self.index_table = OrderedDict(sorted(index_table.items(), key=lambda x: x[0]))\n        # inverse index table: int -> str\n        self.inverse_index_table = OrderedDict(sorted(\n            [(v, k) for k, v in self.index_table.items()],\n            key=lambda x: x[0]\n        ))  # type: OrderedDict[int, str]\n\n    def lookup(self, string: str):\n        if string not in self.index_table:\n            raise ValueError(\"'{}' not in index_table\".format(string))\n        else:\n            return self.index_table.get(string)\n\n    def lookup_str_list(self, str_list: List[str]) -> List[int]:\n        return list([self.lookup(i) for i in str_list])\n\n    def lookup_list_of_str_list(self, list_of_str_list: List[List[str]]) -> List[List[int]]:\n        list_of_id_list = []\n        for str_list in list_of_str_list:\n            id_list = self.lookup_str_list(str_list)\n            list_of_id_list.append(id_list)\n\n        return list_of_id_list\n\n    def inverse_lookup(self, id_: int):\n        if id_ not in self.inverse_index_table:\n            raise ValueError(\"'{}' not in index_table\".format(id_))\n        else:\n            return self.inverse_index_table.get(id_)\n\n    def inverse_lookup_id_list(self, id_list: List[int]):\n        return list([self.inverse_lookup(i) for i in id_list])\n\n    def inverse_lookup_list_of_id_list(self, list_of_id_list: List[List[int]]):\n        list_of_str_list = []\n        for id_list in list_of_id_list:\n            str_list = self.inverse_lookup_id_list(id_list)\n            list_of_str_list.append(str_list)\n\n        return list_of_str_list\n\n    def size(self) -> int:\n        return len(self.index_table)\n\n    def check_id_continuity(self) -> bool:\n        for i in range(self.size()):\n            if i not in self.inverse_index_table:\n                return False\n        return True\n\n    def tolist(self) -> List[str]:\n        assert self.check_id_continuity()\n\n        return [self.inverse_index_table[i] for i in range(self.size())]\n\n    @classmethod\n    def load_from_file(cls, data_file):\n        with open(data_file, 'rt') as fd:\n            # since json or yaml can not guarantee the dict order, list of (key, value) is adopted\n            paired_dict = json.load(fd)\n\n            return cls(dict(paired_dict))\n\n    def dump_to_file(self, data_file):\n        with open(data_file, 'wt') as fd:\n            # since json or yaml can not guarantee the dict order, list of (key, value) is adopted\n            paired_dict = list((k, v) for k, v in self.index_table.items())\n\n            # set ensure_ascii=False for human readability of dumped file\n            json.dump(paired_dict, fd, ensure_ascii=False)\n\n\ndef index_table_from_file(vocabulary_file=None):\n    index_table = {}\n    index_counter = 1\n    with open(vocabulary_file) as fd:\n        for line in fd:\n            key = line.strip('\\n')\n            index_table[key] = index_counter\n            index_counter += 1\n\n    return Lookuper(index_table)\n\n\ndef read_assets():\n    return {\n        'vocab_filename': 'data/unicode_char_list.txt',\n        'tag_filename': 'data/tags.txt'\n    }\n\n\ndef generator_func(data_generator_func, config):\n    # load plugin\n    preprocess_hook = load_hook(config.get('preprocess_hook', []))\n\n    for sentence in data_generator_func():\n        for hook in preprocess_hook:\n            sentence = hook(sentence)\n\n        if isinstance(sentence, list):\n            for s in sentence:\n                yield parse_fn(s)\n        else:\n            yield parse_fn(sentence)\n\n\ndef parse_fn(offset_data):\n    tags = offset_to_biluo(offset_data)\n    words = offset_data.text\n    assert len(words) == len(tags), \"Words and tags lengths don't match\"\n\n    logger.debug((words, len(words)), tags)\n\n    return (words, len(words)), tags\n\n\ndef parse_to_dataset(data_generator_func, config=None, shuffle_and_repeat=False):\n    config = config if config is not None else {}\n    shapes = (([None], ()), [None])\n    types = ((tf.string, tf.int32), tf.string)\n    defaults = (('', 0), 'O')\n\n    dataset = tf.data.Dataset.from_generator(\n        functools.partial(generator_func, data_generator_func, config),\n        output_shapes=shapes, output_types=types)\n\n    if shuffle_and_repeat:\n        # print(\">>> {}\".format(config))\n        dataset = dataset.shuffle(config['shuffle_pool_size']).repeat(config['epochs'])\n\n    # char_encoder = tfds.features.text.SubwordTextEncoder.load_from_file(read_assets()['vocab_filename'])\n    # tag_encoder = tfds.features.text.SubwordTextEncoder.load_from_file(read_assets()['tag_filename'])\n    # dataset = dataset.map(lambda x: (char_encoder.encode(x[0][0]), tag_encoder.encode(x[0][1]), x[1]))\n\n    # words_index_table = index_table_from_file(read_assets()['vocab_filename'])\n    # tags_index_table = index_table_from_file(read_assets()['tag_filename'])\n    # dataset = dataset.map(lambda x, y: ((words_index_table.lookup(x[0]), x[1]), tags_index_table.lookup(y)))\n\n    dataset = (dataset\n               .padded_batch(config['batch_size'], shapes, defaults)\n               .prefetch(1))\n\n    return dataset\n\n\ndef dataset_to_feature_column(dataset):\n    (words, words_len), label = dataset.make_one_shot_iterator().get_next()\n\n    # word_index_lookuper = tf.contrib.lookup.index_table_from_file(\n    #     read_assets()['vocab_filename'],\n    #     num_oov_buckets=1\n    # )\n    # words = word_index_lookuper.lookup(words)\n    #\n    # tag_index_lookuper = tf.contrib.lookup.index_table_from_file(\n    #     read_assets()['tag_filename'],\n    #     num_oov_buckets=1\n    # )\n    # label = tag_index_lookuper.lookup(label)\n\n    return {'words': words, 'words_len': words_len}, label\n\n\ndef build_input_func(data_generator_func, config=None):\n    def input_func():\n        train_dataset = parse_to_dataset(data_generator_func, config, shuffle_and_repeat=True)\n        data_iterator = dataset_to_feature_column(train_dataset)\n\n        return data_iterator\n\n    return input_func\n\n\ndef build_gold_generator_func(offset_dataset):\n    return functools.partial(generator_func, offset_dataset)\n\n\ndef generate_tagset(tags) -> List[str]:\n    if not tags:\n        # empty entity still have O tag\n        return [BILUOEncoderDecoder.oscar]\n\n    tagset = set()\n    for tag in tags:\n        encoder = BILUOEncoderDecoder(tag)\n        tagset.update(encoder.all_tag_set())\n\n    tagset_list = list(tagset)\n\n    # make sure O is first tag,\n    # this is a bug feature, otherwise sentence_correct is not correct\n    # due to the crf decoder, need fix\n    tagset_list.remove(BILUOEncoderDecoder.oscar)\n    tagset_list = list(sorted(tagset_list, key=lambda x: x))\n\n    tagset_list.insert(0, BILUOEncoderDecoder.oscar)\n\n    return tagset_list\n", "sub_path": "seq2annotation/input.py", "file_name": "input.py", "file_ext": "py", "file_size_in_byte": 7203, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 18, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 20, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 22, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 33, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 36, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 50, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 53, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 70, "usage_type": "name"}, {"api_name": "json.load", "line_number": 79, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 89, "usage_type": "call"}, {"api_name": "seq2annotation.utils.load_hook", "line_number": 113, "usage_type": "call"}, {"api_name": "tokenizer_tools.tagset.converter.offset_to_biluo.offset_to_biluo", "line_number": 127, "usage_type": "call"}, {"api_name": "tensorflow.string", "line_number": 139, "usage_type": "attribute"}, {"api_name": "tensorflow.int32", "line_number": 139, "usage_type": "attribute"}, {"api_name": "tensorflow.data.Dataset.from_generator", "line_number": 142, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 142, "usage_type": "attribute"}, {"api_name": "functools.partial", "line_number": 143, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 194, "usage_type": "call"}, {"api_name": "tokenizer_tools.tagset.NER.BILUO.BILUOEncoderDecoder.oscar", "line_number": 200, "usage_type": "attribute"}, {"api_name": "tokenizer_tools.tagset.NER.BILUO.BILUOEncoderDecoder", "line_number": 200, "usage_type": "name"}, {"api_name": "tokenizer_tools.tagset.NER.BILUO.BILUOEncoderDecoder", "line_number": 204, "usage_type": "call"}, {"api_name": "tokenizer_tools.tagset.NER.BILUO.BILUOEncoderDecoder.oscar", "line_number": 212, "usage_type": "attribute"}, {"api_name": "tokenizer_tools.tagset.NER.BILUO.BILUOEncoderDecoder", "line_number": 212, "usage_type": "name"}, {"api_name": "tokenizer_tools.tagset.NER.BILUO.BILUOEncoderDecoder.oscar", "line_number": 215, "usage_type": "attribute"}, {"api_name": "tokenizer_tools.tagset.NER.BILUO.BILUOEncoderDecoder", "line_number": 215, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 197, "usage_type": "name"}]}
+{"seq_id": "534380704", "text": "import logging\n\nfrom fastapi import APIRouter, Depends, Query\nfrom enum import Enum\nfrom ..db import DB\nfrom ..models.queries import APIBase, Country\nfrom openaq_fastapi.models.responses import (\n    OpenAQCountriesResult,\n    converter\n)\nimport jq\nlogger = logging.getLogger(\"locations\")\nlogger.setLevel(logging.DEBUG)\n\nrouter = APIRouter()\n\n\nclass CountriesOrder(str, Enum):\n    country = \"country\"\n    firstUpdated = \"firstUpdated\"\n    lastUpdated = \"lastUpdated\"\n    locations = \"locations\"\n    count = \"count\"\n\n\nclass Countries(Country, APIBase):\n    order_by: CountriesOrder = Query(\"country\")\n    limit: int = Query(200)\n\n    def where(self):\n        wheres = []\n        for f, v in self:\n            if v is not None:\n                if f == \"country\":\n                    wheres.append(\" code = ANY(:country) \")\n        if len(wheres) > 0:\n            return (\" AND \").join(wheres)\n        return \" TRUE \"\n\n\n@router.get(\n    \"/v1/countries/{country_id}\",\n    response_model=OpenAQCountriesResult,\n    tags=[\"v1\"],\n)\n@router.get(\n    \"/v2/countries/{country_id}\",\n    response_model=OpenAQCountriesResult,\n    tags=[\"v2\"],\n)\n@router.get(\"/v2/countries\", response_model=OpenAQCountriesResult, tags=[\"v2\"])\nasync def countries_get(\n    db: DB = Depends(),\n    countries: Countries = Depends(Countries.depends()),\n):\n    order_by = countries.order_by\n    if countries.order_by == \"lastUpdated\":\n        order_by = \"8\"\n    elif countries.order_by == \"firstUpdated\":\n        order_by = \"7\"\n    elif countries.order_by == \"country\":\n        order_by = \"code\"\n    elif countries.order_by == \"count\":\n        order_by = \"count\"\n    elif countries.order_by == \"locations\":\n        order_by = \"locations\"\n\n    q = f\"\"\"\n    WITH t AS (\n    SELECT\n    count(*) over () as countriescount,\n        *\n    FROM country_stats\n    WHERE\n    {countries.where()}\n    AND code is not null\n    ORDER BY {order_by} {countries.sort}\n    OFFSET :offset\n    LIMIT :limit\n    )\n    SELECT countriescount as count, to_jsonb(t)-'{{countriescount}}'::text[] as json FROM t\n\n    \"\"\"\n    params = countries.params()\n    output = await db.fetchOpenAQResult(q, params)\n\n    return output\n\n@router.get(\"/v1/countries\", response_model=OpenAQCountriesResult, tags=[\"v1\"])\nasync def countries_getv1(\n    db: DB = Depends(),\n    countries: Countries = Depends(Countries.depends()),\n):\n    data = await countries_get(db, countries)\n    meta = data.meta\n    res = data.results\n\n    if len(res) == 0:\n        return data\n\n\n    v1_jq = jq.compile(\n        \"\"\"\n        .[] | . as $m |\n            {\n                code: .code,\n                count: .count,\n                locations: .locations,\n                cities: .cities,\n                name:.name\n            }\n\n        \"\"\"\n    )\n\n    return converter(meta, res, v1_jq)\n", "sub_path": "openaq_fastapi/openaq_fastapi/routers/countries.py", "file_name": "countries.py", "file_ext": "py", "file_size_in_byte": 2795, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 13, "usage_type": "attribute"}, {"api_name": "fastapi.APIRouter", "line_number": 15, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 18, "usage_type": "name"}, {"api_name": "models.queries.Country", "line_number": 26, "usage_type": "name"}, {"api_name": "models.queries.APIBase", "line_number": 26, "usage_type": "name"}, {"api_name": "fastapi.Query", "line_number": 27, "usage_type": "call"}, {"api_name": "fastapi.Query", "line_number": 28, "usage_type": "call"}, {"api_name": "db.DB", "line_number": 53, "usage_type": "name"}, {"api_name": "fastapi.Depends", "line_number": 53, "usage_type": "call"}, {"api_name": "fastapi.Depends", "line_number": 54, "usage_type": "call"}, {"api_name": "db.fetchOpenAQResult", "line_number": 85, "usage_type": "call"}, {"api_name": "openaq_fastapi.models.responses.OpenAQCountriesResult", "line_number": 43, "usage_type": "name"}, {"api_name": "openaq_fastapi.models.responses.OpenAQCountriesResult", "line_number": 48, "usage_type": "name"}, {"api_name": "openaq_fastapi.models.responses.OpenAQCountriesResult", "line_number": 51, "usage_type": "name"}, {"api_name": "db.DB", "line_number": 91, "usage_type": "name"}, {"api_name": "fastapi.Depends", "line_number": 91, "usage_type": "call"}, {"api_name": "fastapi.Depends", "line_number": 92, "usage_type": "call"}, {"api_name": "jq.compile", "line_number": 102, "usage_type": "call"}, {"api_name": "openaq_fastapi.models.responses.converter", "line_number": 116, "usage_type": "call"}, {"api_name": "openaq_fastapi.models.responses.OpenAQCountriesResult", "line_number": 89, "usage_type": "name"}]}
+{"seq_id": "87163849", "text": "import json\n\n\ndef switch(argument):\n    switcher = {\n        1: \"January\",\n        2: \"February\",\n        3: \"March\",\n        4: \"April\",\n        5: \"May\",\n        6: \"June\",\n        7: \"July\",\n        8: \"August\",\n        9: \"September\",\n        10: \"October\",\n        11: \"November\",\n        12: \"December\"\n    }\n    return switcher.get(argument, \"Invaild month\")\n\n\nbirthday_dict = {}\n\nwith open(\"34.json\", \"r\") as json_file:\n    json_data = json.load(json_file)\n\nfor date in json_data.values():\n    month = int(date.split(\"/\")[1])\n    month = switch(month)\n\n    if month in birthday_dict.keys():\n        birthday_dict[month] += 1\n    else:\n        birthday_dict[month] = 1\n\nwith open(\"35.json\", \"w\") as json_file:\n    json.dump(birthday_dict, json_file)", "sub_path": "35_''switch''.py", "file_name": "35_''switch''.py", "file_ext": "py", "file_size_in_byte": 756, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "json.load", "line_number": 25, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 37, "usage_type": "call"}]}
+{"seq_id": "618390925", "text": "\"\"\"\n알고스팟 운영진이 모두 미로에 갇혔다. 미로는 N*M 크기이며, 총 1*1크기의 방으로 이루어져 있다. \n미로는 빈 방 또는 벽으로 이루어져 있고, 빈 방은 자유롭게 다닐 수 있지만, 벽은 부수지 않으면 이동할 수 없다.\n\n알고스팟 운영진은 여러명이지만, 항상 모두 같은 방에 있어야 한다. \n즉, 여러 명이 다른 방에 있을 수는 없다. \n어떤 방에서 이동할 수 있는 방은 상하좌우로 인접한 빈 방이다. \n즉, 현재 운영진이 (x, y)에 있을 때, 이동할 수 있는 방은 (x+1, y), (x, y+1), (x-1, y), (x, y-1) 이다. \n단, 미로의 밖으로 이동 할 수는 없다.\n\n벽은 평소에는 이동할 수 없지만, 알고스팟의 무기 AOJ를 이용해 벽을 부수어 버릴 수 있다. \n벽을 부수면, 빈 방과 동일한 방으로 변한다.\n\n만약 이 문제가 알고스팟에 있다면, 운영진들은 궁극의 무기 sudo를 이용해 벽을 한 번에 다 없애버릴 수 있지만, \n안타깝게도 이 문제는 Baekjoon Online Judge에 수록되어 있기 때문에, sudo를 사용할 수 없다.\n\n현재 (1, 1)에 있는 알고스팟 운영진이 (N, M)으로 이동하려면 벽을 최소 몇 개 부수어야 하는지 구하는 프로그램을 작성하시오.\n\n\n[ input ]\n첫째 줄에 미로의 크기를 나타내는 가로 크기 M, 세로 크기 N (1 ≤ N, M ≤ 100)이 주어진다. \n다음 N개의 줄에는 미로의 상태를 나타내는 숫자 0과 1이 주어진다. \n0은 빈 방을 의미하고, 1은 벽을 의미한다.\n\n(1, 1)과 (N, M)은 항상 뚫려있다.\n\n[ output ]\n첫째 줄에 알고스팟 운영진이 (N, M)으로 이동하기 위해 벽을 최소 몇 개 부수어야 하는지 출력한다.\n\n\n\"\"\"\nfrom collections import deque\ndx = [0, 0, 1, -1]\ndy = [1, -1, 0, 0]\n\nm, n = map(int, input().split())\nmaze = [list(map(int, list(input()))) for _ in range(n)]\n\ncheck = [[False]*m for _ in range(n)]\ndist = [[-1]*m for _ in range(n)]\ndq = deque()\n\n#initialize\ncheck[0][0] = True\ndist[0][0] = 0\ndq.append((0, 0))\n\nwhile dq:\n    x, y = dq.popleft()\n    for i in range(4):\n        nx = x+dx[i]\n        ny = y+dy[i]\n        if 0 <= nx < n and 0 <= ny < m:\n            # 빈방인경우\n            if maze[nx][ny] == 0 and check[nx][ny] == False:\n                check[nx][ny] = True\n                dist[nx][ny] = dist[x][y]\n                dq.appendleft((nx, ny))\n            if maze[nx][ny] == 1 and check[nx][ny] == False:\n                check[nx][ny] = True\n                dist[nx][ny] = dist[x][y]+1\n                dq.append((nx, ny))\n\nprint(dist[n-1][m-1])\n", "sub_path": "Baekjoon/1261_AlgoSpot.py", "file_name": "1261_AlgoSpot.py", "file_ext": "py", "file_size_in_byte": 2645, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "collections.deque", "line_number": 41, "usage_type": "call"}]}
+{"seq_id": "108619948", "text": "# -*- coding: utf-8 -*-\nimport scrapy\n\n\nclass EviewscraperSpider(scrapy.Spider):\n    name = \"eviewscraper\"\n    allowed_domains = ['http://www.amazon.in', 'www.amazon.in', 'amazon']\n    count = 0\n    start_urls = ['http://www.amazon.in/b?node=976389031']\n    mydata = {}\n\n    def parse(self, response):\n        category_links = response.css(\n            'div.acs-en-main-section-container > div.acs-en-middle-section  a::attr(href)').extract()\n        self.base = self.allowed_domains[0]\n\n        for booklink in category_links:\n            if booklink is not None:\n                next_page = response.urljoin(booklink)  # combines base url with the extracted url\n                yield scrapy.Request(next_page, callback=self.parseBooks)\n                # next_page = response.urljoin(category_links[0])\n                # yield scrapy.Request(next_page, callback=self.parseBooks)\n\n    def parseBooks(self, response):\n        self.count += 1\n        pagetitle = response.css('title::text').extract_first()\n        # self.mydata={'category': pagetitle}\n        book_items = response.css('#mainResults > ul>li')\n        for item in book_items:\n            # print(item.css('h2::text').extract_first())\n            link = item.css('a::attr(href)').extract_first()\n\n            if link is not None:\n                yield scrapy.Request(link, callback=self.parseBookDetails)\n\n    def parseBookDetails(self, response):\n        title = response.css('title::text').extract_first()\n        # self.mydata['bookname']=title\n        review_link = response.css('#acrCustomerReviewLink::attr(href)').extract_first()\n        if review_link is not None:\n            yield scrapy.Request(self.base + review_link, callback=self.parseReviews)\n\n    def parseReviews(self, response):\n        # bookitle=response.css('div.product-title > h1 a::text').extract_first()\n        reviews = response.css('#cm_cr-review_list > div.review')\n        for review in reviews:\n            yield {\n                'BookName': response.css('div.product-title > h1 a::text').extract_first(),\n                'author':response.css('div.product-by-line a::text').extract_first(),\n                'reviewtitle': review.css('a::text').extract_first(),\n                'name': review.css('a.author::text').extract_first(),\n                'date': review.css('span.review-date::text').extract_first(),\n                'review': review.css('span.review-text::text').extract_first()\n            }\n            nextlink=response.css('ul.a-pagination > li.a-last a::attr(href)').extract_first()\n            if nextlink is not None:\n                self.base = self.allowed_domains[0]\n                next_page = response.urljoin(nextlink)\n                yield scrapy.Request(next_page, callback=self.parseReviews)\n", "sub_path": "eviewscraper.py", "file_name": "eviewscraper.py", "file_ext": "py", "file_size_in_byte": 2765, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "0", "api": [{"api_name": "scrapy.Spider", "line_number": 5, "usage_type": "attribute"}, {"api_name": "scrapy.Request", "line_number": 20, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 34, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 41, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 59, "usage_type": "call"}]}
+{"seq_id": "239423788", "text": "import asyncio\n\nimport pytest\n\nfrom hat import util\nfrom hat.drivers import cotp\n\n\npytestmark = pytest.mark.asyncio\n\n\n@pytest.fixture\ndef addr():\n    return cotp.Address('127.0.0.1', util.get_unused_tcp_port())\n\n\nasync def test_example_docs():\n    addr = cotp.Address('127.0.0.1', util.get_unused_tcp_port())\n\n    conn2_future = asyncio.Future()\n    srv = await cotp.listen(conn2_future.set_result, addr)\n    conn1 = await cotp.connect(addr)\n    conn2 = await conn2_future\n\n    # send from conn1 to conn2\n    data = b'123'\n    conn1.write(data)\n    result = await conn2.read()\n    assert result == data\n\n    # send from conn2 to conn1\n    data = b'321'\n    conn2.write(data)\n    result = await conn1.read()\n    assert result == data\n\n    await conn1.async_close()\n    await conn2.async_close()\n    await srv.async_close()\n", "sub_path": "test_pytest/test_cotp.py", "file_name": "test_cotp.py", "file_ext": "py", "file_size_in_byte": 822, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pytest.mark", "line_number": 9, "usage_type": "attribute"}, {"api_name": "hat.drivers.cotp.Address", "line_number": 14, "usage_type": "call"}, {"api_name": "hat.drivers.cotp", "line_number": 14, "usage_type": "name"}, {"api_name": "hat.util.get_unused_tcp_port", "line_number": 14, "usage_type": "call"}, {"api_name": "hat.util", "line_number": 14, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 12, "usage_type": "attribute"}, {"api_name": "hat.drivers.cotp.Address", "line_number": 18, "usage_type": "call"}, {"api_name": "hat.drivers.cotp", "line_number": 18, "usage_type": "name"}, {"api_name": "hat.util.get_unused_tcp_port", "line_number": 18, "usage_type": "call"}, {"api_name": "hat.util", "line_number": 18, "usage_type": "name"}, {"api_name": "asyncio.Future", "line_number": 20, "usage_type": "call"}, {"api_name": "hat.drivers.cotp.listen", "line_number": 21, "usage_type": "call"}, {"api_name": "hat.drivers.cotp", "line_number": 21, "usage_type": "name"}, {"api_name": "hat.drivers.cotp.connect", "line_number": 22, "usage_type": "call"}, {"api_name": "hat.drivers.cotp", "line_number": 22, "usage_type": "name"}]}
+{"seq_id": "314167108", "text": "import re\nfrom django import forms\nfrom django.utils.translation import ugettext_lazy as _\nfrom django.contrib.auth.models import User\nfrom django.conf import settings\n\n\nclass UserAddForm(forms.Form):\n    name = forms.CharField(label=\"Name\",\n                           error_messages={'required': _('No User name has been entered')},\n                           max_length=20)\n    password = forms.CharField(required=not settings.ALLOW_EMPTY_PASSWORD, error_messages={'required': _('No password has been entered')},)\n\n    def clean_name(self):\n        name = self.cleaned_data['name']\n        have_symbol = re.match('^[a-z0-9]+$', name)\n        if not have_symbol:\n            raise forms.ValidationError(_('The flavor name must not contain any special characters'))\n        elif len(name) > 20:\n            raise forms.ValidationError(_('The flavor name must not exceed 20 characters'))\n        try:\n            User.objects.get(username=name)\n        except User.DoesNotExist:\n            return name\n        raise forms.ValidationError(_('Flavor name is already use'))\n", "sub_path": "accounts/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 1071, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.forms.Form", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 8, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 9, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 9, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 10, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 12, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 12, "usage_type": "name"}, {"api_name": "django.conf.settings.ALLOW_EMPTY_PASSWORD", "line_number": 12, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 12, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 12, "usage_type": "call"}, {"api_name": "re.match", "line_number": 16, "usage_type": "call"}, {"api_name": "django.forms.ValidationError", "line_number": 18, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 18, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 18, "usage_type": "call"}, {"api_name": "django.forms.ValidationError", "line_number": 20, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 20, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 20, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 22, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 22, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.DoesNotExist", "line_number": 23, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 23, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 25, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 25, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 25, "usage_type": "call"}]}
+{"seq_id": "67106818", "text": "import pandas as pd\nfrom email.mime.text import MIMEText\nimport smtplib\nimport time\nimport sys\nimport imaplib\nimport email\nimport camelot\nimport PyPDF2\nimport csv\nimport xlsxwriter\nfrom xlrd import open_workbook\nimport xlwt\nimport os\nimport glob\nimport os.path\nimport xlrd\nfrom os import listdir\nfrom os import path\nfrom os.path import isfile, join\nfrom html.parser import HTMLParser\nimport pdfkit\nimport pandas as pd\nimport pdftotext\nimport html2text\nfrom openpyxl.styles import Color, PatternFill, Font, Border\nimport subprocess\nfrom decode_error import check_subject, read_from_delete\nfrom make_log import log_exceptions\n\ntry:\n    subprocess.run([\"python\", \"updation.py\", \"1\", \"max\", \"9\", \"X\"])\n\n    redFill = PatternFill(start_color='FFFF0000',\n                          end_color='FFFF0000',\n                          fill_type='solid')\n    fg = []\n    eu = []\n    # path_wkhtmltopdf = r'C:\\Program Files\\wkhtmltopdf\\bin\\wkhtmltopdf.exe'\n    # config = pdfkit.configuration(wkhtmltopdf=path_wkhtmltopdf)\n    config = pdfkit.configuration(wkhtmltopdf='/usr/bin/wkhtmltopdf')\n\n\n\n    def read_email_from_gmail():\n        b = 0\n        SMTP_SERVER = str(sys.argv[5])\n        mail = imaplib.IMAP4_SSL(SMTP_SERVER)\n        # mail.login(user = 'Mediclaim@therisingmedicare.com', password = 'cef@2018')\n\n        e_id = str(sys.argv[1])\n        pswd = str(sys.argv[2])\n        srt = str(sys.argv[3])\n        stp = str(sys.argv[4])\n        mail.login(user=e_id, password=pswd)\n        mail.select(\"inbox\", readonly=True)\n        ###############################################<\n        mail_uid = str(sys.argv[7])\n        if mail_uid == -1:\n            type, data = mail.search(None, '(SUBJECT \"Claims settlement-NEFT\" since ' + srt + ' before ' + stp + ')')\n            ids = data[0]\n            id_list = ids.split()\n        else:\n            ids = mail_uid  # data is a list.\n            # accept id from outside and put in id_list akshay var name = id\n\n            id_list = []  # ids is a space separated string\n            id_list.append(ids)\n        ###############################################>\n        # type, data = mail.search(None,\n        #                          '(FROM \"claim.support@apollomunichinsurance.com\" SUBJECT \"Claim Settlement for Claim ID\" since ' + srt + ' before ' + stp + ')')\n        # ids = data[0]  # data is a list.\n        # id_list = ids.split()  # ids is a space separated string\n        # # print(id_list)\n        for i in range(0, len(id_list)):\n            latest_email_id = id_list[i]  # get the latest\n            result, data = mail.fetch(latest_email_id, \"(RFC822)\")\n            ##################################################ak\n            try:\n                raw_email = data[0][1].decode('utf-8')\n                email_message = email.message_from_string(raw_email)\n                subject = email_message['Subject']\n                result, sys.argv[8] = check_subject(subject, sys.argv[8], mail)\n                if result == 'Changed':\n                    # raise Exception('subject not matched')\n                    raise Exception('subject not matched', )\n            except:\n                try:\n                    log_exceptions(syssubject=sys.argv[8], subject=subject, error='subject not matched')\n                except:\n                    pass\n                if result != 'OK':\n                    data = {'server': SMTP_SERVER,\n                            'hospmail': e_id,\n                            'pass': pswd,\n                            'subject': sys.argv[8]}\n                    try:\n                        data = read_from_delete(data)\n                        if data == None:\n                            raise Exception(\"Not found\")\n                    except:\n                        log_exceptions(msg='not found in deleted', subject=sys.argv[8])\n            ##################################################akend\n            raw_email = data[0][1].decode('utf-8')\n            email_message = email.message_from_string(raw_email)\n            # if path.exists(r'/home/shivam/Desktop/vnu_scripts/Paramount/email.html'):\n            # os.remove(r'email.html')\n            # Body details\n            if email_message['Subject'] not in fg:\n                b += 1\n                for part in email_message.walk():\n                    # print(part.get_content_type())\n                    if part.get_content_type() == \"text/html\":\n                        # print('hi')\n                        body = part.get_payload(decode=True)\n                        file_name = \"apollo_munich/email.html\"\n                        output_file = open(file_name, 'w')\n                        output_file.write(\"Body: %s\" % (body.decode('utf-8')))\n                        output_file.close()\n\n                        pdfkit.from_file('apollo_munich/email.html',\n                                         'apollo_munich/attachments_' + str(sys.argv[6]) + '/' + str(b) + '.pdf',\n                                         configuration=config)\n            fg.append(email_message['Subject'])\n\n\n    mypath = os.getcwd() + '/apollo_munich'\n    if not path.exists(mypath):\n        os.mkdir(mypath)\n    if not path.exists(mypath + '/attachments_' + str(sys.argv[6])):\n        os.mkdir(mypath + '/attachments_' + str(sys.argv[6]))\n    mypath = os.getcwd() + '/apollo_munich/attachments_' + str(sys.argv[6]) + '/'\n    for filename in os.listdir(mypath):\n        file_path = os.path.join(mypath, filename)\n        if os.path.isfile(file_path) or os.path.islink(file_path):\n            os.unlink(file_path)\n    read_email_from_gmail()\n    # print(fg)\n    ccn = []\n    name = []\n    uhid = []\n    for i in fg:\n        d = i.find('Claim ID:') + 9\n        k = i.find('of')\n        ccn.append(i[d:k])\n        d = i.find('covered')\n        k = i.find('of') + 2\n        name.append(i[k:d])\n        d = i.find('UHID No:') + 9\n        k = i.find('.')\n        uhid.append(i[d:k])\n    # print(ccn,uhid)\n    onlyfiles = [f for f in listdir(mypath) if isfile(join(mypath, f))]\n    onlyfiles.sort()\n    # print(onlyfiles)\n    if path.exists(r'apollo_munich/apollo' + str(sys.argv[6]) + '.xlsx'):\n        os.remove(r'apollo_munich/apollo' + str(sys.argv[6]) + '.xlsx')\n    import openpyxl\n\n    po = []\n    wq = 0\n    wbkName = 'apollo_munich/apollo' + str(sys.argv[6]) + '.xlsx'\n    wbk = openpyxl.Workbook()\n    wbk.create_sheet('1')\n    s1 = wbk.worksheets[0]\n    s2 = wbk.worksheets[1]\n    for t in range(0, len(onlyfiles)):\n        sh1 = ['Sr No.', 'Preauth Id', 'Hospital Name', 'Claimed Amount', 'Diagnosis', 'Billed Amount', 'Date of Admission',\n               'settled Amount', 'Date of Discharge', 'Cheque Amount', 'Cheque Number/NEFT reference', 'Disallowed Amount',\n               'Cheque/NEFT date', 'Discount Amount', 'TDS Amount', 'IP Number', 'Bill No', 'ccn', 'uhid', 'patient name']\n        sh2 = ['Sr No.', 'Claim ID', 'category', 'Disallowance amount', 'Disallowance Reasons']\n\n        for i in range(0, len(sh1)):\n            s1.cell(row=1, column=i + 1).value = sh1[i]\n        for i in range(0, len(sh2)):\n            s2.cell(row=1, column=i + 1).value = sh2[i]\n        tables = camelot.read_pdf(mypath + onlyfiles[t], pages='all', Line_scale=10)\n        tables.export('apollo_munich/foo1.xls', f='excel')\n        loc = (\"apollo_munich/foo1.xls\")\n        wb = xlrd.open_workbook(loc)\n        s = []\n        sheet_3 = wb.sheet_by_index(0)\n        sheet_3.cell_value(0, 0)\n\n        for i in range(1, sheet_3.nrows):\n            s.append(sheet_3.cell_value(i, 2))\n            s.append(sheet_3.cell_value(i, 4))\n        mid = s[-1]\n        s.pop(-1)\n        s = [sub.replace('\\t', ' ') for sub in s]\n        s = [sub.replace('Rs.', '') for sub in s]\n        # print(s)\n        s1.cell(row=t + 2, column=1).value = t + 1\n        s1.cell(row=t + 2, column=2).value = mid\n        for i in range(0, len(s)):\n            s1.cell(row=t + 2, column=i + 3).value = s[i]\n        s1.cell(row=t + 2, column=i + 4).value = ccn[t]\n        s1.cell(row=t + 2, column=i + 5).value = uhid[t]\n        s1.cell(row=t + 2, column=i + 6).value = name[t]\n        with open(mypath + onlyfiles[t], \"rb\") as f:\n            pdf = pdftotext.PDF(f)\n\n        with open('apollo_munich/output.txt', 'w') as f:\n            f.write(\" \".join(pdf))\n        with open('apollo_munich/output.txt', 'r') as myfile:\n            f = myfile.read()\n        hg = []\n        w = f.find('Disallowance Reasons :') + 22\n        u = f.find('Please note')\n        g = f[w:u]\n        sy = g.split('\\n')\n        sy.pop(0)\n        sy.pop(-1)\n        for i in sy:\n            # print(i)\n            if (i.find(':') != -1):\n                k = i\n                k = k.replace(':', '')\n                continue\n            else:\n                w1 = i.find('Rs.') + 3\n                g = i[w1:]\n                u1 = g.find('.') + w1 + 3\n                m = i[w1:u1]\n                h = i[u1:]\n            row_num = s2.max_row + 1\n            wq += 1\n            s2.cell(row=row_num, column=1).value = wq\n            s2.cell(row=row_num, column=2).value = mid\n            s2.cell(row=row_num, column=3).value = k\n            s2.cell(row=row_num, column=4).value = m\n            s2.cell(row=row_num, column=5).value = h\n        # print(s)\n        os.rename(os.getcwd() + '/apollo_munich/attachments_' + str(sys.argv[6]) + '/' + onlyfiles[t],\n                  os.getcwd() + '/apollo_munich/attachments_' + str(sys.argv[6]) + '/' + mid + '.pdf')\n        '''w1=f.find('Reference/UTR No.')+17\n        g=f[w1:]\n        u1=g.find('\\n')+w1\n        hg.append(f[w1:u1])\n    \n        w2=f.find('Payment Date')+13\n        g=f[w2:]\n        u2=g.find('\\n')+w2\n        hg.append(f[w2:u2])\n        \n    \n        w9=f.find('ID Card Number :')+17\n        g=f[w9:]\n        u9=g.find('\\n')+w9\n        hg.append(f[w9:u9])\n    \n        hg=[sub.replace('  ','') for sub in hg]\n        hg=[sub.replace(':','') for sub in hg]\n        hg=[sub.replace('\\n',' ') for sub in hg]'''\n        # print(sy[1].find(':'))\n        '''except Exception as e:\n            eu.append(t)\n            print(onlyfiles[t],e)\n    for t in range(0,len(onlyfiles)):\n        if t in eu:\n            s1.cell(row=t+1, column=1).fill = redFill'''\n    print(\"Done\")\n    wbk.save(wbkName)\n    wbk.close\n    wbkName = 'count/count.xlsx'\n    wbk = openpyxl.load_workbook(wbkName)\n    s1 = wbk.worksheets[0]\n    s1.cell(row=3, column=1).value = 'apollo munich'\n    s1.cell(row=3, column=2).value = len(fg)\n    s1.cell(row=3, column=3).value = len(onlyfiles)\n    wbk.save(wbkName)\n    wbk.close\n    subprocess.run([\"python\", \"updation.py\", \"1\", \"max\", \"9\", \" \"])\nexcept:\n    log_exceptions()", "sub_path": "apollo_munich.py", "file_name": "apollo_munich.py", "file_ext": "py", "file_size_in_byte": 10618, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "subprocess.run", "line_number": 32, "usage_type": "call"}, {"api_name": "openpyxl.styles.PatternFill", "line_number": 34, "usage_type": "call"}, {"api_name": "pdfkit.configuration", "line_number": 41, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 47, "usage_type": "attribute"}, {"api_name": "imaplib.IMAP4_SSL", "line_number": 48, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 51, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 52, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 53, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 54, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 58, "usage_type": "attribute"}, {"api_name": "email.message_from_string", "line_number": 81, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 83, "usage_type": "attribute"}, {"api_name": "decode_error.check_subject", "line_number": 83, "usage_type": "call"}, {"api_name": "make_log.log_exceptions", "line_number": 89, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 89, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 96, "usage_type": "attribute"}, {"api_name": "decode_error.read_from_delete", "line_number": 98, "usage_type": "call"}, {"api_name": "make_log.log_exceptions", "line_number": 102, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 102, "usage_type": "attribute"}, {"api_name": "email.message_from_string", "line_number": 105, "usage_type": "call"}, {"api_name": "pdfkit.from_file", "line_number": 121, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 122, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path", "line_number": 128, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 130, "usage_type": "call"}, {"api_name": "os.path", "line_number": 130, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 130, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 131, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 131, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 132, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 132, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path", "line_number": 134, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path", "line_number": 135, "usage_type": "attribute"}, {"api_name": "os.path.islink", "line_number": 135, "usage_type": "call"}, {"api_name": "os.unlink", "line_number": 136, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 156, "usage_type": "call"}, {"api_name": "os.path", "line_number": 156, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 156, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 157, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 157, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 162, "usage_type": "attribute"}, {"api_name": "openpyxl.Workbook", "line_number": 163, "usage_type": "call"}, {"api_name": "camelot.read_pdf", "line_number": 177, "usage_type": "call"}, {"api_name": "xlrd.open_workbook", "line_number": 180, "usage_type": "call"}, {"api_name": "pdftotext.PDF", "line_number": 201, "usage_type": "call"}, {"api_name": "os.rename", "line_number": 234, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 234, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 234, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 235, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 235, "usage_type": "attribute"}, {"api_name": "openpyxl.load_workbook", "line_number": 266, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 273, "usage_type": "call"}, {"api_name": "make_log.log_exceptions", "line_number": 275, "usage_type": "call"}]}
+{"seq_id": "386751088", "text": "\"\"\"\nPreprocess the output received from server and interface as a final result to the client\n\"\"\"\nimport os\nimport tempfile\nimport warnings\nimport collections\n\nimport pandas as pd\n\n\nclass ConvertTo:\n    \"\"\"Convert tabular JSON to an user requested output format\"\"\"\n    FORMATS = {\"df\", \"dataframe\", \"json\", \"csv\", \"dict\"}\n    DEFAULT = \"df\"\n\n    def __init__(self, data: dict, fmt: str = DEFAULT, indexing: bool = False):\n        \"\"\"\n\n        :param data: Tabular JSON data from server\n        :param fmt: format to be converted into\n        :param indexing: row & column index consideration in the output\n        \"\"\"\n        self.data = data\n        self.output = self._converter(fmt.lower(), indexing=indexing)\n\n    def _converter(self, fmt: str, indexing: bool = False) -> list:\n        \"\"\"\n        Actual conversion takes place here using Pandas\n        :param fmt: format to be converted into\n        :param indexing: row index consideration in the output\n        :return: list of tables from converted into the requested output format\n        \"\"\"\n        dfs = []\n        for table in self.data.get(\"Tables\", []):\n            tmp = {int(k): v for k, v in table[\"TableJson\"].items()}\n            # To convert column indices to int to maintain the table order with more than 9 columns\n            cols = [str(x) for x in sorted([int(x) for x in tmp[0]])]\n            # To convert row indices to int and maintain the table order with more than 9 rows\n            tmp = collections.OrderedDict(sorted(tmp.items()))\n            dfs.append(pd.DataFrame.from_dict(tmp, orient=\"index\", columns=cols))\n\n        if fmt in (\"df\", \"dataframe\"):\n            return dfs\n        elif fmt == \"dict\":\n            return [df.to_dict() for df in dfs]\n        elif fmt == \"csv\":\n            save_folder = tempfile.mkdtemp()\n            output_location = []\n            for tbl_n, df in enumerate(dfs):\n                csv_name = os.path.join(save_folder, f\"_table_{tbl_n+1}.csv\")\n                df.to_csv(csv_name, index=indexing, header=indexing)\n                output_location.append(csv_name)\n            return output_location\n        elif fmt == \"json\":\n            return [df.to_json() for df in dfs]\n        else:\n            warn_msg = f\"Supported output formats {self.FORMATS} only. Assigned to default: {self.DEFAULT}\"\n            warnings.warn(warn_msg)\n            return dfs\n", "sub_path": "ExtractTable/common.py", "file_name": "common.py", "file_ext": "py", "file_size_in_byte": 2375, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "collections.OrderedDict", "line_number": 40, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 41, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 41, "usage_type": "attribute"}, {"api_name": "tempfile.mkdtemp", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "warnings.warn", "line_number": 59, "usage_type": "call"}]}
+{"seq_id": "623677138", "text": "import pandas as pd\nfrom scipy.stats import skew,probplot\nfrom scipy.special import boxcox1p\nimport numpy as np\nimport seaborn as sns\nfrom time import time\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import train_test_split,GridSearchCV,KFold,cross_val_score\nfrom sklearn.ensemble import GradientBoostingRegressor,RandomForestRegressor,AdaBoostRegressor\nfrom sklearn.metrics import mean_squared_error,r2_score\nfrom sklearn.tree import DecisionTreeRegressor\nfrom sklearn.svm import SVR,libsvm\nfrom sklearn.linear_model import LassoCV,RidgeCV,LinearRegression,ElasticNet,ElasticNetCV\nfrom sklearn.model_selection import GridSearchCV\nimport pickle\nimport sklearn.tree.tree\nimport matplotlib.pyplot as plt\nfrom sklearn.preprocessing import OneHotEncoder,LabelEncoder\ntrain=pd.read_csv(\"E:/pycharm project/compitition/train.csv\")\ntest=pd.read_csv(\"E:/pycharm project/compitition/test.csv\")\ntrain.drop([\"Id\",\"PoolQC\",\"Alley\",\"FireplaceQu\",\"MiscFeature\",\"Fence\"],axis=1,inplace=True)\ntest.drop([\"Id\",\"PoolQC\",\"Alley\",\"FireplaceQu\",\"MiscFeature\",\"Fence\"],axis=1,inplace=True)\n#判断数值与类目关系\n\nSalePrice=train[\"SalePrice\"]\ntrain.drop([\"SalePrice\"],inplace=True,axis=1)\ntrain=pd.concat([train,test])\n\ntrain_num=train.select_dtypes(exclude=[object,'category'])\ntrain_object=train.select_dtypes(include=[object,'category'])\ntrain_num=train_num.fillna(train_num.mean()) #解决思路：先用正则将空格匹配出来，然后全部替换为NULL，再在用pandas读取csv时候指定 read_csv（na_values='NULL'）就是将NULL认为是nan处理，接下来就可以用dropna()或者fillna()来处理了\ntrain_object=train_object.fillna(train_object.mode().iloc[0])\n\nSalePrice=np.log1p(SalePrice)\nres=probplot(SalePrice,plot=plt)\nsns.distplot(SalePrice)\nfor col in train_num.columns:\n    if len(set(train_num[col]))<=25:\n        train[col]=train_num[col].astype('category')\n\ntrain_num_num=train_num.select_dtypes(exclude=[object,'category'])\ntrain_num_object=train_num.select_dtypes(include=[object,'category'])#连续lable编码\n#label后类型为int\ndef label_EN(train_num_object):\n    for col in train_num_object:\n        lbl=LabelEncoder()\n        lbl.fit(train_num_object[col].values)\n        train_object[col]=lbl.transform(train_num_object[col])\n    return train_num_object\ntrain_num_object=label_EN(train_num_object)\nskewed_feats = train_num.apply(lambda x: skew(x.dropna())).sort_values(ascending=False)\nskewness = pd.DataFrame(skewed_feats)\ndef box_cox(train_num):\n    skewness_box = skewness[abs(skewness) > 0.75]\n    for col in skewness.index:\n        train_num[col]=boxcox1p(train_num[col],0.15)\n    return train_num\ntrain_num_num=box_cox(train_num_num)\n\n\ntrain=pd.concat([train_num_num,train_num_object,train_object],axis=1)\n\ntrain=pd.get_dummies(train)#只对类目转换，提前将顺序的类目进行进行label编码，变为int后再进行get_dummies\n\ny=SalePrice\n\nX=train[:1460]\n\ntrain_final=pd.concat([X,y],axis=1)\ntrain_final.to_csv(\"E:/pycharm project/compitition/train_preprocessing.csv\",index=False)\ntest=train[1460:]\ntest.to_csv(\"E:/pycharm project/compitition/test_preprocessing.csv\",index=False)\ntrain_X,test_X, train_y, test_y = train_test_split(X, y, test_size = 0.3, random_state = 42)\n\n\n\n\n\n\n\nGBoost = GradientBoostingRegressor(n_estimators=3000, learning_rate=0.05,\n                                       max_depth=4, max_features='sqrt',\n                                       min_samples_leaf=15, min_samples_split=10,\n                                       loss='huber', random_state=5)\n\n\nELNET=ElasticNet(alpha=0.0005, l1_ratio=.9, random_state=3)\ndef cross_val_score1(model,X_train,y_train):\n    cv=KFold( 5,shuffle=True, random_state=42)\n    mean_score1=np.mean(cross_val_score(model,X_train,y_train,cv=cv,scoring=\"neg_mean_squared_error\"))\n    print(\"train_score:\"+str(mean_score1))\n# cross_val_score1(GBoost,train_X,train_y)\n# cross_val_score1(ELNET,train_X,train_y)\n# R2方法是将预测值跟只使用均值的情况下相比，看能好多少。其区间通常在（0,1）之间。0表示还不如什么都不预测，直接取均值的情况，而1表示所有预测跟真实结果完美匹配的情况。\n# ””’ 与均值相比的优秀程度，介于[0~1]。0表示不如均值。1表示完美预测. ”’\n# def R2(y_test, y_true):\n# return 1 - ((y_test - y_true)**2).sum() / ((y_true - y_true.mean())**2).sum()\n\n\n\ndef elas(train_X,train_y,test_X,test_y):\n    stan=StandardScaler()\n    stan.fit(train_X)\n    train_X=stan.transform(train_X)\n    test_X=stan.transform(test_X)\n    starttime = time()\n    clf = ElasticNetCV(fit_intercept=True, normalize=False)\n    clf.fit(train_X, train_y)\n    result = clf.predict(test_X)\n    print(\"弹性网格均方根：%f\" % np.sqrt(mean_squared_error(test_y, result)))\n    print(\"\\033[1;34m\")\n    print(\"弹性网格_r2得分：%f\" % r2_score(test_y, result))\n    print(\"\\033[0m\")\n    print(\"弹性网格用时：%f\" % (time() - starttime))\n\n\ndef linear(train_X,train_y,test_X,test_y):\n    stan = StandardScaler()\n    stan.fit(train_X)\n    train_X = stan.transform(train_X)\n    test_X = stan.transform(test_X)\n    starttime=time()\n    clf=LinearRegression(fit_intercept=True,normalize=False)\n    clf.fit(train_X,train_y)\n    result = clf.predict(test_X)\n    print(\"linear均方根：%f\" % np.sqrt(mean_squared_error(test_y, result)))\n    print(\"********\")\n    print(\"linear_r2得分：%f\" % r2_score(test_y,result))\n    print(\"linear用时：%f\" % (time()-starttime))\n\n\ndef lass(train_X,train_y,test_X,test_y):\n    stan = StandardScaler()\n    stan.fit(train_X)\n    train_X = stan.transform(train_X)\n    test_X = stan.transform(test_X)\n    starttime = time()\n    clf=LassoCV(fit_intercept=True,normalize=False,alphas=[0.01,0.1, 1.0, 10.0,100])\n    clf.fit(train_X, train_y)\n    result = clf.predict(test_X)\n    print(\"lass_&: %f\" % (clf.alpha_)) #得出&值后再细分0.1 np.lispace(0,1,100)\n    print(\"lass均方根：%f\" % np.sqrt(mean_squared_error(test_y, result)))\n    print(\"********\")\n    print(\"lass_r2得分：%f\" % r2_score(test_y, result))\n    print(\"lass用时：%f\" % (time()-starttime))\n\n\ndef ridge_cv(train_X,train_y,test_X,test_y):\n    stan = StandardScaler()\n    stan.fit(train_X)\n    train_X = stan.transform(train_X)\n    test_X = stan.transform(test_X)\n    starttime = time()\n    clf = RidgeCV(fit_intercept=True,alphas=[0.1, 1.0, 10.0],normalize=False)\n    clf.fit(train_X, train_y)\n    result = clf.predict(test_X)\n    print(\"ridge_&: %f\" % (clf.alpha_))\n    print(\"ridge_cv均方根：%f\" % np.sqrt(mean_squared_error(test_y, result)))\n    print(\"********\")\n    print(\"ridge_cv_r2得分：%f\" % r2_score(test_y, result))\n    print(\"ridge用时：%f\" % (time()-starttime))\n\ndef randomf(train_X,train_y,test_X,test_y):\n    starttime = time()\n    clf = RandomForestRegressor(n_estimators=300,max_features=\"sqrt\",\n                                max_depth=5,min_samples_split=15,min_samples_leaf=10)\n    clf.fit(train_X, train_y)\n    result = clf.predict(test_X)\n    print(\"randomf均方根：%f\" % np.sqrt(mean_squared_error(test_y, result)))\n    print(\"********\")\n    print(\"randomf_r2得分：%f\" % r2_score(test_y, result))\n    print(\"randomf用时：%f\" % (time()-starttime))\n\ndef svr(train_X,train_y,test_X,test_y):\n    starttime = time()\n    clf = SVR(kernel='rbf', C=1.0, epsilon=0.05)\n    clf.fit(train_X, train_y)\n    result = clf.predict(test_X)\n    print(\"svr均方根：%f\" % np.sqrt(mean_squared_error(test_y, result)))\n    print(\"********\")\n    print(\"svr_r2得分：%f\" % r2_score(test_y, result))\n    print(\"svr用时：%f\" % (time()-starttime))\ndef ada(train_X,train_y,test_X,test_y):\n    starttime = time()\n    clf =AdaBoostRegressor()\n    clf.fit(train_X, train_y)\n    result = clf.predict(test_X)\n    print(\"ada均方根：%f\" % np.sqrt(mean_squared_error(test_y, result)))\n    print(\"********\")\n    print(\"ada_r2得分：%f\" % r2_score(test_y, result))\n    print(\"ada用时：%f\" % (time()-starttime))\ndef decision(train_X,train_y,test_X,test_y):\n    starttime = time()\n    clf =DecisionTreeRegressor()\n    clf.fit(train_X, train_y)\n    result = clf.predict(test_X)\n    print(\"decision均方根：%f\" % np.sqrt(mean_squared_error(test_y, result)))\n    print(\"********\")\n    print(\"decision_r2得分：%f\" % r2_score(test_y, result))\n    print(\"decision用时：%f\" % (time()-starttime))\n\n\ndef GBDTRR(train_X,train_y,test_X,test_y):\n    starttime = time()\n    est = GradientBoostingRegressor(\n    loss='huber',      ##默认ls损失函数'ls'是指最小二乘回归lad'（最小绝对偏差）'huber'是两者的组合\n    n_estimators=1500, ##默认100 回归树个数 弱学习器个数\n    learning_rate=0.1,  ##默认0.1学习速率/步长0.0-1.0的超参数  每个树学习前一个树的残差的步长\n    max_depth=3,   ## 默认值为3每个回归树的深度  控制树的大小 也可用叶节点的数量max leaf nodes控制\n    subsample=1,  ##用于拟合个别基础学习器的样本分数 选择子样本<1.0导致方差的减少和偏差的增加\n    min_samples_split=2, ##生成子节点所需的最小样本数 如果是浮点数代表是百分比\n    min_samples_leaf=1, ##叶节点所需的最小样本数  如果是浮点数代表是百分比\n    max_features=None, ##在寻找最佳分割点要考虑的特征数量auto全选/sqrt开方/log2对数/None全选/int自定义几个/float百分比\n    max_leaf_nodes=None, ##叶节点的数量 None不限数量\n    min_impurity_decrease=1e-7, ##停止分裂叶子节点的阈值\n    verbose=0,  ##打印输出 大于1打印每棵树的进度和性能\n    warm_start=False, ##True在前面基础上增量训练 False默认擦除重新训练 增加树\n    random_state=0  ##随机种子-方便重现\n    )\n    est.fit(train_X,train_y)\n    result=est.predict(test_X)\n    print(\"gbdt均方差根: %f\" % np.sqrt(mean_squared_error(test_y,result)))\n    print(\"gbdt均方差: %f\" % (mean_squared_error(test_y,result)))\n    print(\"********\")\n    print(\"gbdt_r2得分：%f\" % r2_score(test_y,result))\n    print(\"gbdt用时：%f\" % (time()-starttime))\n#GBDTRR(train_X,train_y,test_X,test_y)\n\ndef all_clf(train_X,train_y,test_X,test_y):\n    #linear(train_X,train_y,test_X,test_y)\n    lass(train_X,train_y,test_X,test_y)\n    ridge_cv(train_X,train_y,test_X,test_y)\n    elas(train_X, train_y, test_X, test_y)\n    randomf(train_X,train_y,test_X,test_y)\n    GBDTRR(train_X,train_y,test_X,test_y)\n    svr(train_X, train_y, test_X, test_y)\n    ada(train_X, train_y, test_X, test_y)\n    decision(train_X, train_y, test_X, test_y)\nall_clf(train_X,train_y,test_X,test_y)\n\n\n\nclass Ensemble(object):\n    #base_models=[clf1,clf2,clf3,clf4,....]  stacking using simple model\n    def __init__(self, n_splits, stacker, base_models):\n        self.n_splits = n_splits\n        self.stacker = stacker\n        self.base_models = base_models\n\n    def fit_predict(self, X, y, T):\n        X = np.array(X)\n        y = np.array(y)\n        T = np.array(T)\n        folds = list(KFold(n_splits=self.n_splits,\n                           shuffle=True, random_state=2016).split(X, y))\n        S_train = np.zeros((X.shape[0], len(self.base_models)))\n        S_test = np.zeros((T.shape[0], len(self.base_models)))\n        for i, clf in enumerate(self.base_models):\n            S_test_i = np.zeros((T.shape[0], self.n_splits))\n            for j, (train_idx, test_idx) in enumerate(folds):\n                X_train = X[train_idx]\n                y_train = y[train_idx]\n                X_holdout = X[test_idx]\n                y_holdout = y[test_idx]\n                print (\"Fit Model %d fold %d\" % (i, j))\n                clf.fit(X_train, y_train)\n                y_pred = clf.predict(X_holdout)[:]\n                S_train[test_idx, i] = y_pred\n                S_test_i[:, j] = clf.predict(T)[:]\n                #feature_importances=clf.feature_importance\n                #feature_importances_df=pd.DateFrame(feature_importances,index=X.colums).sort_values()\n                #li.append()\n            S_test[:, i] = S_test_i.mean(axis=1)\n\n        # results = cross_val_score(self.stacker, S_train, y, cv=5, scoring='r2')\n        # print(\"Stacker score: %.4f (%.4f)\" % (results.mean(), results.std()))\n        # exit()\n\n        self.stacker.fit(S_train, y)\n        res = self.stacker.predict(S_test)[:]\n        return res\n\n\n\n\n\ndef stacking(X,y,test_X,test_y,test):\n    # stan=StandardScaler()\n    # stan.fit(X)\n    # X=stan.transform(X)\n    # test=stan.transform(test)\n    enet = ElasticNetCV(fit_intercept=True, normalize=False)\n    ridge = RidgeCV(fit_intercept=True, alphas=[0.1, 1.0, 10.0], normalize=False)\n    lass = LassoCV(fit_intercept=True, normalize=False, alphas=[0.01, 0.1, 1.0, 10.0, 100])\n    rf=RandomForestRegressor()\n    ada=AdaBoostRegressor()\n    dt=DecisionTreeRegressor()\n    gbdt=GradientBoostingRegressor(\n        loss='huber',  ##默认ls损失函数'ls'是指最小二乘回归lad'（最小绝对偏差）'huber'是两者的组合\n        n_estimators=500,  ##默认100 回归树个数 弱学习器个数\n        learning_rate=0.1,  ##默认0.1学习速率/步长0.0-1.0的超参数  每个树学习前一个树的残差的步长\n        max_depth=3,  ## 默认值为3每个回归树的深度  控制树的大小 也可用叶节点的数量max leaf nodes控制\n        subsample=1,  ##用于拟合个别基础学习器的样本分数 选择子样本<1.0导致方差的减少和偏差的增加\n        min_samples_split=2,  ##生成子节点所需的最小样本数 如果是浮点数代表是百分比\n        min_samples_leaf=1,  ##叶节点所需的最小样本数  如果是浮点数代表是百分比\n        max_features=None,  ##在寻找最佳分割点要考虑的特征数量auto全选/sqrt开方/log2对数/None全选/int自定义几个/float百分比\n        max_leaf_nodes=None,  ##叶节点的数量 None不限数量\n        min_impurity_decrease=1e-7,  ##停止分裂叶子节点的阈值\n        verbose=0,  ##打印输出 大于1打印每棵树的进度和性能\n        warm_start=False,  ##True在前面基础上增量训练 False默认擦除重新训练 增加树\n        random_state=0  ##随机种子-方便重现\n    )\n    svr = SVR(kernel='rbf', C=1.0, epsilon=0.05)\n    stack = Ensemble(n_splits=5,\n                     stacker=LinearRegression(),\n                     #1 :1500 rf,ada,dt,svr,gbdt\n                     base_models=(svr,gbdt,enet,ridge,lass))\n    result=stack.fit_predict(train_X, train_y,test)\n    #test_y=stack.stacker.predict(test)[:]\n    # print(\"stacking均方根：%f\" % np.sqrt(mean_squared_error(test_y, result)))\n    # print(\"stacking_r2得分：%f\" % r2_score(test_y, result))\n    # print(\"stacking用时：%f\" % (time()-starttime))\n    return result\ntest_predict=stacking(train_X,train_y,test_X,test_y,test)\ntest_predict=np.exp(test_predict)-1\nsubmission=pd.DataFrame(test_predict)\nprint(test_predict)\nsubmission.to_csv(\"C:/Users/yu/Desktop/compitition/submission2.csv\")\n\n\n# General Parameters（常规参数）\n# 1.booster [default=gbtree]：选择基分类器，gbtree: tree-based models/gblinear: linear models\n# 2.silent [default=0]:设置成1则没有运行信息输出，最好是设置为0.\n# 3.nthread [default to maximum number of threads available if not set]：线程数\n#\n# Booster Parameters（模型参数）\n# 1.eta [default=0.3]:shrinkage参数，用于更新叶子节点权重时，乘以该系数，避免步长过大。参数值越大，越可能无法收敛。把学习率 eta 设置的小一些，小学习率可以使得后面的学习更加仔细。\n# 2.min_child_weight [default=1]:这个参数默认是 1，是每个叶子里面 h 的和至少是多少，对正负样本不均衡时的 0-1 分类而言，假设 h 在 0.01 附近，min_child_weight 为 1 意味着叶子节点中最少需要包含 100 个样本。这个参数非常影响结果，控制叶子节点中二阶导的和的最小值，该参数值越小，越容易 overfitting。\n# 3.max_depth [default=6]: 每颗树的最大深度，树高越深，越容易过拟合。\n# 4.max_leaf_nodes:最大叶结点数，与max_depth作用有点重合。\n# 5.gamma [default=0]：后剪枝时，用于控制是否后剪枝的参数。\n# 6.max_delta_step [default=0]：这个参数在更新步骤中起作用，如果取0表示没有约束，如果取正值则使得更新步骤更加保守。可以防止做太大的更新步子，使更新更加平缓。\n# 7.subsample [default=1]：样本随机采样，较低的值使得算法更加保守，防止过拟合，但是太小的值也会造成欠拟合。\n# 8.colsample_bytree [default=1]：列采样，对每棵树的生成用的特征进行列采样.一般设置为： 0.5-1\n# 9.lambda [default=1]：控制模型复杂度的权重值的L2正则化项参数，参数越大，模型越不容易过拟合。\n# 10.alpha [default=0]:控制模型复杂程度的权重值的 L1 正则项参数，参数值越大，模型越不容易过拟合。\n# 11.scale_pos_weight [default=1]：如果取值大于0的话，在类别样本不平衡的情况下有助于快速收敛。\n#\n# Learning Task Parameters（学习任务参数）\n# 1.objective [default=reg:linear]：定义最小化损失函数类型，常用参数：\n# binary:logistic –logistic regression for binary classification, returns predicted probability (not class)\n# multi:softmax –multiclass classification using the softmax objective, returns predicted class (not probabilities)\n# you also need to set an additional num_class (number of classes) parameter defining the number of unique classes\n# multi:softprob –same as softmax, but returns predicted probability of each data point belonging to each class.\n# 2.eval_metric [ default according to objective ]：\n# The metric to be used for validation data.\n# The default values are rmse for regression and error for classification.\n# Typical values are:\n# rmse – root mean square error\n# mae – mean absolute error\n# logloss – negative log-likelihood\n# error – Binary classification error rate (0.5 threshold)\n# merror – Multiclass classification error rate\n# mlogloss – Multiclass logloss\n# auc: Area under the curve\n# 3.seed [default=0]：\n# The random number seed. 随机种子，用于产生可复现的结果\n# Can be used for generating reproducible results and also for parameter tuning.\n#\n# 注意: python sklearn style参数名会有所变化\n# eta –> learning_rate\n# lambda –> reg_lambda\n# alpha –> reg_alpha\n\n\n\n\n\n\n\n\n", "sub_path": "house_price.py", "file_name": "house_price.py", "file_ext": "py", "file_size_in_byte": 18444, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pandas.read_csv", "line_number": 19, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.log1p", "line_number": 34, "usage_type": "call"}, {"api_name": "scipy.stats.probplot", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "seaborn.distplot", "line_number": 36, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 46, "usage_type": "call"}, {"api_name": "scipy.stats.skew", "line_number": 51, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 52, "usage_type": "call"}, {"api_name": "scipy.special.boxcox1p", "line_number": 56, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 61, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 63, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 69, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 73, "usage_type": "call"}, {"api_name": "sklearn.ensemble.GradientBoostingRegressor", "line_number": 81, "usage_type": "call"}, {"api_name": "sklearn.linear_model.ElasticNet", "line_number": 87, "usage_type": "call"}, {"api_name": "sklearn.model_selection.KFold", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 90, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 90, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 102, "usage_type": "call"}, {"api_name": "time.time", "line_number": 106, "usage_type": "call"}, {"api_name": "sklearn.linear_model.ElasticNetCV", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 110, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 110, "usage_type": "call"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 112, "usage_type": "call"}, {"api_name": "time.time", "line_number": 114, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 118, "usage_type": "call"}, {"api_name": "time.time", "line_number": 122, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 126, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 126, "usage_type": "call"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 128, "usage_type": "call"}, {"api_name": "time.time", "line_number": 129, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 133, "usage_type": "call"}, {"api_name": "time.time", "line_number": 137, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LassoCV", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 142, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 142, "usage_type": "call"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 144, "usage_type": "call"}, {"api_name": "time.time", "line_number": 145, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 149, "usage_type": "call"}, {"api_name": "time.time", "line_number": 153, "usage_type": "call"}, {"api_name": "sklearn.linear_model.RidgeCV", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 158, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 158, "usage_type": "call"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 160, "usage_type": "call"}, {"api_name": "time.time", "line_number": 161, "usage_type": "call"}, {"api_name": "time.time", "line_number": 164, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 169, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 169, "usage_type": "call"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 171, "usage_type": "call"}, {"api_name": "time.time", "line_number": 172, "usage_type": "call"}, {"api_name": "time.time", "line_number": 175, "usage_type": "call"}, {"api_name": "sklearn.svm.SVR", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 179, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 179, "usage_type": "call"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 181, "usage_type": "call"}, {"api_name": "time.time", "line_number": 182, "usage_type": "call"}, {"api_name": "time.time", "line_number": 184, "usage_type": "call"}, {"api_name": "sklearn.ensemble.AdaBoostRegressor", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 188, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 188, "usage_type": "call"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 190, "usage_type": "call"}, {"api_name": "time.time", "line_number": 191, "usage_type": "call"}, {"api_name": "time.time", "line_number": 193, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeRegressor", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 197, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 197, "usage_type": "call"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 199, "usage_type": "call"}, {"api_name": "time.time", "line_number": 200, "usage_type": "call"}, {"api_name": "time.time", "line_number": 204, "usage_type": "call"}, {"api_name": "sklearn.ensemble.GradientBoostingRegressor", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 222, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 222, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 223, "usage_type": "call"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 225, "usage_type": "call"}, {"api_name": "time.time", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 251, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 252, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 253, "usage_type": "call"}, {"api_name": "sklearn.model_selection.KFold", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 257, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 259, "usage_type": "call"}, {"api_name": "sklearn.linear_model.ElasticNetCV", "line_number": 292, "usage_type": "call"}, {"api_name": "sklearn.linear_model.RidgeCV", "line_number": 293, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LassoCV", "line_number": 294, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 295, "usage_type": "call"}, {"api_name": "sklearn.ensemble.AdaBoostRegressor", "line_number": 296, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeRegressor", "line_number": 297, "usage_type": "call"}, {"api_name": "sklearn.ensemble.GradientBoostingRegressor", "line_number": 298, "usage_type": "call"}, {"api_name": "sklearn.svm.SVR", "line_number": 313, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 315, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 325, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 326, "usage_type": "call"}]}
+{"seq_id": "120822901", "text": "import matplotlib.pyplot as plt\nimport numpy as np\nimport sys\nimport csv\n\ntime = []\ntrue_pos = []\nest_pos = []\nest_pos_std_dev = []\ntrue_vel = []\nest_vel = []\nest_vel_std_dev = []\ntrue_alt = []\nest_alt = []\nest_alt_std_dev = []\ntrue_climb = []\nest_climb = []\nest_climb_std_dev = []\n\nwith open(sys.argv[1],'r') as csv_file:\n    plots = csv.reader(csv_file, delimiter=',')\n    headers = next(plots, None)\n    for row in plots:\n        time.append(float(row[0]))\n        true_pos.append(float(row[1]))\n        est_pos.append(float(row[2]))\n        est_pos_std_dev.append(float(row[3]))\n        true_vel.append(float(row[4]))\n        est_vel.append(float(row[5]))\n        est_vel_std_dev.append(float(row[6]))\n        true_alt.append(float(row[7]))\n        est_alt.append(float(row[8]))\n        est_alt_std_dev.append(float(row[9]))\n        true_climb.append(float(row[10]))\n        est_climb.append(float(row[11]))\n        est_climb_std_dev.append(float(row[12]))\n\nfig, axes = plt.subplots(4, 1)\nfig.suptitle(\"Airplane Tracking Estimation\")\n\naxes[0].plot(time, true_pos, \"k-\", time, est_pos, \"b-\", \\\n             time, np.array(est_pos) + np.array(est_pos_std_dev), \"g--\", \\\n             time, np.array(est_pos) - np.array(est_pos_std_dev), \"g--\")\naxes[0].set(xlabel=\"Time [s]\", ylabel=\"Position [m]\")\n\naxes[1].plot(time, true_vel, \"k-\", time, est_vel, \"b-\", \\\n             time, np.array(est_vel) + np.array(est_vel_std_dev), \"g--\", \\\n             time, np.array(est_vel) - np.array(est_vel_std_dev), \"g--\")\naxes[1].set(xlabel=\"Time [s]\", ylabel=\"Velocity [m/s]\")\n\naxes[2].plot(time, true_alt, \"k-\", time, est_alt, \"b-\", \\\n             time, np.array(est_alt) + np.array(est_alt_std_dev), \"g--\", \\\n             time, np.array(est_alt) - np.array(est_alt_std_dev), \"g--\")\naxes[2].set(xlabel=\"Time [s]\", ylabel=\"Altitude [m]\")\n\naxes[3].plot(time, true_climb, \"k-\", time, est_climb, \"b-\", \\\n             time, np.array(est_climb) + np.array(est_climb_std_dev), \"g--\", \\\n             time, np.array(est_climb) - np.array(est_climb_std_dev), \"g--\")\naxes[3].set(xlabel=\"Time [s]\", ylabel=\"Climb Rate [m/s]\")\n\n# Hide x labels and tick labels for top plots and y ticks for right plots.\nfor axis in axes.flat:\n    axis.label_outer()\n\nplt.show()\n", "sub_path": "examples/airplane_tracking/plot_airplane_tracking.py", "file_name": "plot_airplane_tracking.py", "file_ext": "py", "file_size_in_byte": 2234, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sys.argv", "line_number": 20, "usage_type": "attribute"}, {"api_name": "csv.reader", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}]}
+{"seq_id": "301361502", "text": "#! /usr/bin/env python\nimport rospy\nimport argparse\nimport torch\nimport time\nimport os\nimport numpy as np\nfrom pathlib import Path\nfrom torch.autograd import Variable\nfrom tensorboardX import SummaryWriter\n\nfrom train.algorithms.pmdqn import PMDQN\nfrom train.env import Env\nfrom train.utils.buffer import ReplayBuffer\nimport pickle\nimport threading\n\nUSE_CUDA = False\nglobal replay_buffer\nglobal epi_record\n\nclass myThread (threading.Thread):\n    def __init__(self):\n        threading.Thread.__init__(self)\n    def run(self):\n        print (\"start save file, episode=\", epi_record)\n        with open('checkpoints_PMDQN_6_22/logs/incremental' +'dqnBuffer' + str(epi_record) + '.pkl', 'wb') as file:\n            pickle.dump(replay_buffer, file)\n        print (\"finish save file：\")\n\ndef run(config):\n    log_dir = 'checkpoints_PMDQN_6_22_epi20/'\n    run_dir = log_dir + 'logs/'\n    logger = SummaryWriter(str(log_dir))\n\n    env = Env()\n    pmdqn = PMDQN.init_from_env(agent_alg=config.agent_alg,\n                                  tau=config.tau,\n                                  lr=config.lr,\n                                  hidden_dim=config.hidden_dim)\n    global replay_buffer\n    replay_buffer = ReplayBuffer(config.buffer_length, pmdqn.nagents,\n                                 [362]*3,\n                                 [4]*3)\n    t = 0\n    global epi_record\n    for ep_i in range(0, config.n_episodes):\n        print(\"Episodes %i-%i of %i\" % (ep_i + 1,\n                                        ep_i + 2,\n                                        config.n_episodes))\n        epi_record = ep_i\n        obs = env.reset()\n        pmdqn.prep_rollouts(device='cpu')\n\n        collision_flag = 0\n        for et_i in range(config.episode_length):\n            print(et_i)\n            collision_flag = collision_flag + 1\n            # env.render(close=False)\n            # rearrange observations to be per agent, and convert to torch Variable\n            torch_obs = [Variable(torch.Tensor(np.vstack(obs[:, i])),\n                                  requires_grad=False)\n                         for i in range(pmdqn.nagents)]\n            # get actions as torch Variables\n            agent_actions = pmdqn.step(torch_obs, explore=True)\n            # convert actions to numpy arrays\n            # rearrange actions to be per environment\n            actions = [[ac for ac in agent_actions] for i in range(config.n_rollout_threads)]\n            next_obs, rewards, dones = env.step(actions)\n            print(rewards)\n            replay_buffer.push(obs, agent_actions, rewards, next_obs, dones)\n            obs = next_obs\n            t += config.n_rollout_threads\n            if (len(replay_buffer) >= config.batch_size and\n                    (t % config.steps_per_update) < config.n_rollout_threads):\n                if USE_CUDA:\n                    pmdqn.prep_training(device='cpu')\n                else:\n                    pmdqn.prep_training(device='cpu')\n                for u_i in range(config.n_rollout_threads):\n                    for a_i in range(pmdqn.nagents):\n                        sample = replay_buffer.sample(config.batch_size,\n                                                      to_gpu=USE_CUDA)\n                        pmdqn.update(sample, a_i, logger=logger)\n                    pmdqn.update_all_targets()\n                pmdqn.prep_rollouts(device='cpu')\n            print(np.max(dones))\n            if np.max(dones)==1:\n                break\n            time.sleep(0.5)\n\n        ep_rews = replay_buffer.get_average_rewards(\n            config.episode_length * config.n_rollout_threads)\n        for a_i, a_ep_rew in enumerate(ep_rews):\n            logger.add_scalar('agent%i/mean_episode_rewards' % a_i, a_ep_rew, ep_i)\n        # if ep_i % config.save_interval < config.n_rollout_threads:\n        #     os.makedirs(run_dir + 'incremental', exist_ok=True)\n        #     dqn.save(run_dir + 'incremental' + ('model_ep%i.pt' % (ep_i + 1)))\n        #     dqn.save(run_dir + 'model.pt')\n        #     save_thread = myThread()\n        #     save_thread.start()\n        if collision_flag == config.episode_length:\n            logger.add_scalar('collision', 0, ep_i)\n        else:\n            logger.add_scalar('collision',1, ep_i)\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--env_id\", help=\"Name of environment\", default=\"Autodriving\")\n    parser.add_argument(\"--model_name\",\n                        help=\"Name of directory to store \" +\n                             \"model/training contents\", default=\"DQN\")\n    parser.add_argument(\"--seed\",\n                        default=1, type=int,\n                        help=\"Random seed\")\n    parser.add_argument(\"--n_rollout_threads\", default=1, type=int)\n    parser.add_argument(\"--n_training_threads\", default=6, type=int)\n    parser.add_argument(\"--buffer_length\", default=int(1e5), type=int)\n    parser.add_argument(\"--n_episodes\", default=20000, type=int)\n    parser.add_argument(\"--episode_length\", default=20, type=int)\n    parser.add_argument(\"--steps_per_update\", default=100, type=int)\n    parser.add_argument(\"--batch_size\",\n                        default=1024, type=int,\n                        help=\"Batch size for model training\")\n    parser.add_argument(\"--n_exploration_eps\", default=5000, type=int)\n    parser.add_argument(\"--init_noise_scale\", default=0.3, type=float)\n    parser.add_argument(\"--final_noise_scale\", default=0.0, type=float)\n    parser.add_argument(\"--save_interval\", default=1000, type=int)\n    parser.add_argument(\"--hidden_dim\", default=32, type=int)\n    parser.add_argument(\"--lr\", default=0.01, type=float)\n    parser.add_argument(\"--tau\", default=0.01, type=float)\n    parser.add_argument(\"--agent_alg\",\n                        default=\"DQN\", type=str,\n                        choices=['DQN', 'DQN'])\n    parser.add_argument(\"--adversary_alg\",\n                        default=\"DQN\", type=str,\n                        choices=['DQN', 'DQN'])\n    parser.add_argument(\"--discrete_action\", default=True, type=bool)\n\n    config = parser.parse_args()\n    run(config)", "sub_path": "RL_Training/train/main_PMDQN.py", "file_name": "main_PMDQN.py", "file_ext": "py", "file_size_in_byte": 6098, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "threading.Thread", "line_number": 22, "usage_type": "attribute"}, {"api_name": "threading.Thread.__init__", "line_number": 24, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 28, "usage_type": "call"}, {"api_name": "tensorboardX.SummaryWriter", "line_number": 34, "usage_type": "call"}, {"api_name": "train.env.Env", "line_number": 36, "usage_type": "call"}, {"api_name": "train.algorithms.pmdqn.PMDQN.init_from_env", "line_number": 37, "usage_type": "call"}, {"api_name": "train.algorithms.pmdqn.PMDQN", "line_number": 37, "usage_type": "name"}, {"api_name": "train.utils.buffer.ReplayBuffer", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 88, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 90, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 109, "usage_type": "call"}]}
+{"seq_id": "308140088", "text": "import sqlalchemy as sa\nfrom sqlalchemy.engine.url import make_url\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy.orm import Session, scoped_session, sessionmaker\nfrom sqlalchemy.util import get_cls_kwargs\n\nfrom .routing import get_routing_class\nfrom .default_model import get_default_model_class\nfrom .default_meta import DefaultMeta\nfrom .session_proxy import SessionProxy\n\n\nclass SQLAlchemy(SessionProxy):\n    \"\"\"This class is used to easily instantiate a SQLAlchemy connection to\n    a database, to provide a base class for your models, and to get a session\n    to interact with them.\n\n    ```python\n    db = SQLAlchemy({\"default\": }})\n\n    class User(db.Model):\n        login = Column(String(80), unique=True)\n        passw_hash = Column(String(80))\n    ```\n\n    **IMPORTANT**\n\n    In a web application or a multithreaded environment you need to call\n    ``session.remove()`` when a request/thread ends. Use your framework's\n    ``after_request`` hook, to do that. For example, in `Flask`:\n\n    ```python\n    app = Flask(…)\n    db = SQLAlchemy(…)\n\n    @app.teardown_appcontext\n    def shutdown(response=None):\n        db.remove()\n        return response\n    ```\n\n    Use the ``db`` to interact with the data:\n\n    ```python\n    user = User('tiger')\n    db.add(user)\n    db.commit()\n    # etc\n    ```\n\n    To query, you can use ``db.query``\n\n    ```python\n    db.query(User.id, User.email).all()\n    db.query(User).filter_by(login == 'tiger').first()\n    # etc.\n    ```\n\n    **Scoping**\n\n    By default, sessions are scoped to the current thread, but he SQLAlchemy\n    documentation recommends scoping the session to something more\n    application-specific if you can, like a web request in a web app.\n\n    To do that, you can use the ``scopefunc`` argument, passing a function that\n    returns something unique (and hashable) like a request.\n    \"\"\"\n\n    def __init__(\n        self,\n        databases=None,\n        *,\n        metadata=None,\n        metaclass=None,\n        model_class=None,\n        scopefunc=None,\n        **options\n    ):\n        self.databases = databases\n        self.Model = self._make_declarative_base(model_class, metadata, metaclass)\n\n        self._set_session_options(options)\n        self.engines = {k: sa.create_engine(v, self.engine_options) for k, v in self.databases.items()}\n        self.RoutingSession = get_routing_class(self.engines)\n        self.Session = sessionmaker(class_=self.RoutingSession, **self.session_options)\n        self._session = scoped_session(self.Session, scopefunc)\n\n        _include_sqlalchemy(self)\n\n    def _set_session_options(self, options):\n        session_options = {}\n\n        for arg in get_cls_kwargs(Session):\n            if arg in options:\n                session_options[arg] = options.pop(arg)\n\n        options.setdefault(\"echo\", False)\n        self.engine_options = options\n\n        session_options.setdefault(\"autoflush\", True)\n        session_options.setdefault(\"autocommit\", False)\n        self.session_options = session_options\n\n    def _make_declarative_base(self, model_class, metaclass=None, metadata=None):\n        \"\"\"Creates the declarative base.\"\"\"\n        return declarative_base(\n            name=\"Model\",\n            cls=model_class or get_default_model_class(self),\n            metaclass=metaclass or DefaultMeta,\n            metadata=metadata,\n        )\n\n    @property\n    def metadata(self):\n        \"\"\"Proxy for ``Model.metadata``.\"\"\"\n        return self.Model.metadata\n\n    def create_all(self, engine='default', *args, **kwargs):\n        \"\"\"Creates all tables.\"\"\"\n        _engine = self.engines[engine]\n        kwargs.setdefault(\"bind\", _engine)\n        self.Model.metadata.create_all(*args, **kwargs)\n\n    def drop_all(self, engine='default', *args, **kwargs):\n        \"\"\"Drops all tables.\"\"\"\n        _engine = self.engines[engine]\n        kwargs.setdefault(\"bind\", _engine)\n        self.Model.metadata.drop_all(*args, **kwargs)\n\n    def reconfigure(self, **kwargs):\n        \"\"\"Updates the session options.\"\"\"\n        self._session.remove()\n        self.session_options.update(**kwargs)\n        self._session.configure(**self.session_options)\n\n    def __repr__(self):\n        return \"\".format(str(self.databases))\n\n\ndef _include_sqlalchemy(obj):\n    for module in sa, sa.orm:\n        for key in module.__all__:\n            if not hasattr(obj, key):\n                setattr(obj, key, getattr(module, key))\n    obj.event = sa.event\n", "sub_path": "sqla_wrapper/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4488, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "session_proxy.SessionProxy", "line_number": 13, "usage_type": "name"}, {"api_name": "sqlalchemy.create_engine", "line_number": 83, "usage_type": "call"}, {"api_name": "routing.get_routing_class", "line_number": 84, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 85, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.scoped_session", "line_number": 86, "usage_type": "call"}, {"api_name": "sqlalchemy.util.get_cls_kwargs", "line_number": 93, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 93, "usage_type": "argument"}, {"api_name": "sqlalchemy.ext.declarative.declarative_base", "line_number": 106, "usage_type": "call"}, {"api_name": "default_model.get_default_model_class", "line_number": 108, "usage_type": "call"}, {"api_name": "default_meta.DefaultMeta", "line_number": 109, "usage_type": "name"}, {"api_name": "sqlalchemy.orm", "line_number": 141, "usage_type": "attribute"}, {"api_name": "sqlalchemy.event", "line_number": 145, "usage_type": "attribute"}]}
+{"seq_id": "488733338", "text": "from collections import defaultdict\n\nfrom cogent3.core.alignment import SequenceCollection\nfrom cogent3.core.genetic_code import DEFAULT, get_code\nfrom cogent3.core.moltype import get_moltype\n\nfrom .composable import (\n    ALIGNED_TYPE,\n    SEQUENCE_TYPE,\n    ComposableSeq,\n    NotCompleted,\n)\n\n\n__author__ = \"Gavin Huttley\"\n__copyright__ = \"Copyright 2007-2020, The Cogent Project\"\n__credits__ = [\"Gavin Huttley\"]\n__license__ = \"BSD-3\"\n__version__ = \"2020.2.7a\"\n__maintainer__ = \"Gavin Huttley\"\n__email__ = \"Gavin.Huttley@anu.edu.au\"\n__status__ = \"Alpha\"\n\n\ndef best_frame(seq, gc=DEFAULT, allow_rc=False, require_stop=False):\n    \"\"\"returns reading frame start that has either no stops or a single\n    terminal stop codon\n\n    result will be either 1, 2, 3 (or -1, -2, -3)\n\n    Parameters\n    ----------\n    gc\n        genetic code ID, name or instance\n    allow_rc\n        If False, forward strand considered only. If True, and\n          best frame on rc, it will be negative\n    require_stop\n        a terminal stop must be present\n    \n    Returns\n    -------\n    int\n        1, 2, 3 if the best frame on the +_ strand; -1, -2, -3 if the best\n        frame is on the reverse strand\n    \n    Raises\n    ------\n    ValueError\n        if the minimum number of stop codons across all frames exceeds 1,\n        or the the stop codon is not at the sequence end\n    \"\"\"\n    gc = get_code(gc)\n    translations = gc.sixframes(seq)\n    if not allow_rc:\n        translations = translations[:3]\n\n    if not require_stop:\n        # don't count stops if they're at the end of the aa sequence\n        for i in range(len(translations)):\n            if translations[i].endswith(\"*\"):\n                translations[i] = translations[i][:-1]\n\n    stops_in_frame = [(tr.count(\"*\"), i) for i, tr in enumerate(translations)]\n    stops_in_frame.sort()\n    min_stops, frame = stops_in_frame[0]\n    # if min_stops > 1, cannot be translated\n    if min_stops > 1:\n        raise ValueError(\"%s cannot be robustly translated\" % seq.name)\n    elif min_stops == 0 and require_stop:\n        # find seq with 1 stop\n        min_stops = 20  # nonsense value\n        for idx, (n, fr) in enumerate(stops_in_frame):\n            if n == 1:\n                min_stops, frame = n, fr\n                break\n\n    if 0 <= min_stops <= 1:\n        if min_stops == 1 and not translations[frame].endswith(\"*\"):\n            raise ValueError(\"%s cannot be robustly translated\" % seq.name)\n    else:\n        raise ValueError(\"%s cannot be robustly translated\" % seq.name)\n\n    frame += 1\n    if allow_rc and frame > 3:\n        frame = 3 - frame\n    return frame\n\n\ndef translate_frames(seq, moltype=None, gc=DEFAULT, allow_rc=False):\n    \"\"\"translates a nucleic acid sequence \n    \n    Parameters\n    ----------\n    moltype\n        molecular type, must be either DNA or RNA\n    gc\n        identifer for a genetic code or a genetic code instance\n    allow_rc : bool\n        includes frames sequence reverse complement\n        \n    Returns\n    -------\n    [(frame, translation), ..]\n    Reverse complement frame numbers are negative\n    \"\"\"\n    gc = get_code(gc)\n    if moltype:\n        moltype = get_moltype(moltype)\n        seq = moltype.make_seq(seq)\n\n    translations = gc.sixframes(seq)\n    if not allow_rc:\n        translations = translations[:3]\n\n    return translations\n\n\ndef get_fourfold_degenerate_sets(gc, alphabet=None, as_indices=True):\n    \"\"\"returns set() of codons that are 4-fold degenerate for genetic code gc\n    \n    Parameters\n    ----------\n    gc\n        identifer for a genetic code or a genetic code instance\n    alphabet\n        nucleic acid Alphabet instance\n    as_indices\n        codons are represented as indices, rather than strings\n    \"\"\"\n    four_fold = set()\n    syns = gc.synonyms\n    for codons in list(syns.values()):\n        if len(codons) < 4:\n            continue\n        pos12s = defaultdict(list)\n        for codon in codons:\n            pos12s[codon[:2]].append(codon)\n\n        for groups in list(pos12s.values()):\n            if len(groups) == 4:\n                four_fold.update([frozenset(groups)])\n\n    if as_indices:\n        assert alphabet is not None, \"Must provide alphabet to convert to indices\"\n        ffold = set()\n        to_indices = alphabet.to_indices\n        for group in four_fold:\n            grp = frozenset([tuple(to_indices(element)) for element in group])\n            ffold.add(grp)\n        four_fold = ffold\n\n    return four_fold\n\n\nclass select_translatable(ComposableSeq):\n    \"\"\"Identifies most likely reading frame. Returns modified sequences / alignment,\n    if it could be resolved, NotCompleted otherwise.\"\"\"\n\n    _input_types = (SEQUENCE_TYPE, ALIGNED_TYPE)\n    _output_types = SEQUENCE_TYPE\n    _data_types = (\"ArrayAlignment\", \"Alignment\", \"SequenceCollection\")\n\n    def __init__(\n        self, moltype=\"dna\", gc=DEFAULT, allow_rc=False, trim_terminal_stop=True\n    ):\n        \"\"\"selects translatable sequences\n\n        Sequences are truncated to modulo 3. seqs.info has a translation_errors\n        entry.\n\n        Parameters\n        ----------\n        moltype : str\n            molecular type, must be either DNA or RNA\n        gc\n            identifier for a genetic code or a genetic code instance\n        allow_rc : bool\n            If False, forward strand considered only. If True, and\n              best frame on rc, it will be negative\n        trim_terminal_stop : bool\n            exclude terminal stop codon from seqs\n        \n        Returns\n        -------\n        A sequence collection. Sequences that could not be translated\n        are excluded.\n        \"\"\"\n        super(select_translatable, self).__init__(\n            input_types=self._input_types,\n            output_types=self._output_types,\n            data_types=self._data_types,\n        )\n        self._formatted_params()\n\n        moltype = get_moltype(moltype)\n        assert moltype.label.lower() in (\"dna\", \"rna\"), \"Invalid moltype\"\n\n        self._moltype = moltype\n        self._gc = get_code(gc)\n        self._allow_rc = allow_rc\n        self._trim_terminal_stop = trim_terminal_stop\n        self.func = self.get_translatable\n\n    def get_translatable(self, seqs):\n        \"\"\"returns the translatable sequences from seqs.\n\n        translation errors are stroed in the info object\"\"\"\n        seqs = seqs.degap()\n        if self._moltype and self._moltype != seqs.moltype:\n            seqs = seqs.to_moltype(self._moltype)\n\n        translatable = []\n        error_log = []\n        for seq in seqs.seqs:\n            try:\n                frame = best_frame(seq, self._gc, allow_rc=self._allow_rc)\n                if frame < 0:\n                    seq = seq.rc()\n                    frame *= -1\n                frame -= 1  # returned from best frame as 1, 2, 3\n                num_codons = (len(seq) - frame) // 3\n                seq = seq[frame : frame + (num_codons * 3)]\n                if self._trim_terminal_stop:\n                    seq = seq.trim_stop_codon(gc=self._gc)\n                translatable.append([seq.name, seq])\n            except ValueError as msg:\n                # TODO handle case where incomplete at end OR beginning\n                # plus case where is divisible by 3 but not in frame\n                # if not divisible by 3, then calc remainder as len(seq) % 3\n                # try translating new[remainder:] and new[:-remainder]\n                error_log.append([seq.name, msg.args[0]])\n\n        if translatable:\n            translatable = SequenceCollection(\n                data=translatable, moltype=self._moltype, info=seqs.info\n            )\n            translatable.info[\"translation_errors\"] = error_log\n        else:\n            translatable = NotCompleted(\"FALSE\", self, \" \".join(error_log), source=seqs)\n\n        return translatable\n\n\nclass translate_seqs(ComposableSeq):\n    \"\"\"Translates sequences, assumes in correct reading frame.\"\"\"\n\n    _input_types = (SEQUENCE_TYPE, ALIGNED_TYPE)\n    _output_types = (SEQUENCE_TYPE, ALIGNED_TYPE)\n    _data_types = (\"ArrayAlignment\", \"Alignment\", \"SequenceCollection\")\n\n    def __init__(\n        self, moltype=\"dna\", gc=DEFAULT, allow_rc=False, trim_terminal_stop=True\n    ):\n        \"\"\"generates aa sequences\n\n        Parameters\n        ----------\n        moltype : str\n            molecular type, must be either DNA or RNA\n        gc\n            identifier for a genetic code or a genetic code instance\n        trim_terminal_stop : bool\n            exclude terminal stop codon from seqs\n\n        Returns\n        -------\n        A sequence collection. Sequences that could not be translated\n        are excluded.\n        \"\"\"\n        super(translate_seqs, self).__init__(\n            input_types=self._input_types,\n            output_types=self._output_types,\n            data_types=self._data_types,\n        )\n        self._formatted_params()\n\n        moltype = get_moltype(moltype)\n        assert moltype.label.lower() in (\"dna\", \"rna\"), \"Invalid moltype\"\n\n        self._moltype = moltype\n        self._gc = get_code(gc)\n        self._trim_terminal_stop = trim_terminal_stop\n        self.func = self.get_translated\n\n    def get_translated(self, seqs):\n        \"\"\"returns translated sequences\"\"\"\n        if self._moltype and self._moltype != seqs.moltype:\n            seqs = seqs.to_moltype(self._moltype)\n\n        if self._trim_terminal_stop:\n            seqs = seqs.trim_stop_codons(gc=self._gc)\n        aa = seqs.get_translation(gc=self._gc)\n        return aa\n", "sub_path": "src/cogent3/app/translate.py", "file_name": "translate.py", "file_ext": "py", "file_size_in_byte": 9445, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "cogent3.core.genetic_code.DEFAULT", "line_number": 25, "usage_type": "name"}, {"api_name": "cogent3.core.genetic_code.get_code", "line_number": 53, "usage_type": "call"}, {"api_name": "cogent3.core.genetic_code.DEFAULT", "line_number": 90, "usage_type": "name"}, {"api_name": "cogent3.core.genetic_code.get_code", "line_number": 107, "usage_type": "call"}, {"api_name": "cogent3.core.moltype.get_moltype", "line_number": 109, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 136, "usage_type": "call"}, {"api_name": "composable.ComposableSeq", "line_number": 156, "usage_type": "name"}, {"api_name": "composable.SEQUENCE_TYPE", "line_number": 160, "usage_type": "name"}, {"api_name": "composable.ALIGNED_TYPE", "line_number": 160, "usage_type": "name"}, {"api_name": "composable.SEQUENCE_TYPE", "line_number": 161, "usage_type": "name"}, {"api_name": "cogent3.core.genetic_code.DEFAULT", "line_number": 165, "usage_type": "name"}, {"api_name": "cogent3.core.moltype.get_moltype", "line_number": 196, "usage_type": "call"}, {"api_name": "cogent3.core.genetic_code.get_code", "line_number": 200, "usage_type": "call"}, {"api_name": "cogent3.core.alignment.SequenceCollection", "line_number": 235, "usage_type": "call"}, {"api_name": "composable.NotCompleted", "line_number": 240, "usage_type": "call"}, {"api_name": "composable.ComposableSeq", "line_number": 245, "usage_type": "name"}, {"api_name": "composable.SEQUENCE_TYPE", "line_number": 248, "usage_type": "name"}, {"api_name": "composable.ALIGNED_TYPE", "line_number": 248, "usage_type": "name"}, {"api_name": "composable.SEQUENCE_TYPE", "line_number": 249, "usage_type": "name"}, {"api_name": "composable.ALIGNED_TYPE", "line_number": 249, "usage_type": "name"}, {"api_name": "cogent3.core.genetic_code.DEFAULT", "line_number": 253, "usage_type": "name"}, {"api_name": "cogent3.core.moltype.get_moltype", "line_number": 278, "usage_type": "call"}, {"api_name": "cogent3.core.genetic_code.get_code", "line_number": 282, "usage_type": "call"}]}
+{"seq_id": "503128777", "text": "from random import choice, choices\nfrom config import *\nfrom losses.mix import *\nimport numpy as np\nimport torch\nfrom torch import nn\nimport pytorch_lightning as pl\nfrom sklearn.metrics import roc_auc_score\n\nclass LightningSETI(pl.LightningModule):\n  def __init__(self, model, choice_weights, loss_fns, optim, plist, \n  batch_size, lr_scheduler, random_id, fold=0, distributed_backend='dp',\n  cyclic_scheduler=None, num_class=1, patience=3, factor=0.5,\n   learning_rate=1e-3):\n      super().__init__()\n      self.model = model\n      self.num_class = num_class\n      self.loss_fns = loss_fns\n      self.optim = optim\n      self.plist = plist \n      self.lr_scheduler = lr_scheduler\n      self.cyclic_scheduler = cyclic_scheduler\n      self.random_id = random_id\n      self.fold = fold\n      self.distributed_backend = distributed_backend\n      self.patience = patience\n      self.factor = factor\n      self.learning_rate = learning_rate\n      self.batch_size = batch_size\n      self.choice_weights = choice_weights\n      self.criterion = self.loss_fns[0]\n      self.train_loss  = 0\n      self.epoch_end_output = [] # Ugly hack for gathering results from multiple GPUs\n  \n  def forward(self, x):\n      out = self.model(x)\n      out = out.type_as(x)\n      return out\n\n  def configure_optimizers(self):\n        optimizer = self.optim(self.plist, self.learning_rate)\n        lr_sc = self.lr_scheduler(optimizer, mode='max', factor=0.5, \n        patience=patience, verbose=True, threshold=1e-4, threshold_mode='rel', cooldown=0, min_lr=1e-7, eps=1e-08)\n        return ({\n       'optimizer': optimizer,\n       'lr_scheduler': lr_sc,\n       'monitor': f'val_roc_auc_fold_{self.fold}',\n       'cyclic_scheduler': self.cyclic_scheduler}\n        )\n \n  def loss_func(self, logits, labels):\n      return self.criterion(logits, labels)\n  \n  def step(self, batch):\n    _, x, y = batch\n    x, y = x.float(), y.float()\n    if self.criterion == self.loss_fns[1]:\n      x, y1, y2, lam = mixup(x, y)\n      y = [y1, y2, lam]\n    logits = torch.squeeze(self.forward(x))\n    loss = self.loss_func(logits, y)\n    return loss, logits, y  \n  \n  def training_step(self, train_batch, batch_idx):\n    # if self.current_epoch < 4:\n    #   loss, _, _ = self.step(train_batch, [1.0, 0.0])\n    # else:\n    self.criterion = choices(self.loss_fns, weights=choice_weights)[0]\n    loss, _, _ = self.step(train_batch)\n    self.train_loss  += loss.detach()\n    self.log(f'train_loss_fold_{self.fold}', self.train_loss/batch_idx, prog_bar=True)\n    if self.cyclic_scheduler is not None:\n      self.cyclic_scheduler.step()\n    return loss\n\n  def validation_step(self, val_batch, batch_idx):\n      self.criterion = self.loss_fns[0]\n      self.train_loss  = 0\n      loss, logits, y = self.step(val_batch)\n      self.log(f'val_loss_fold_{self.fold}', loss, on_epoch=True, sync_dist=True) \n      val_log = {'val_loss':loss, 'probs':logits, 'gt':y}\n      self.epoch_end_output.append({k:v.cpu() for k,v in val_log.items()})\n      return val_log\n\n  def test_step(self, test_batch, batch_idx):\n      self.criterion = self.loss_fns[0]\n      if len(test_batch) == 2:\n        data_id, x = test_batch\n      elif len(test_batch) == 3:\n        data_id, x, y = test_batch\n      data_id = [i.split('/')[-1].split('.')[0] for i in list(data_id)]\n      pred = self.forward(x)\n      pred = pred.sigmoid().detach().cpu().numpy()\n      pred = np.nan_to_num(pred, 0.5)\n      if len(test_batch) == 2:\n        test_log = {'id':data_id, 'target':np.squeeze(pred)}\n      if len(test_batch) == 3:\n        test_log = {'id':data_id, 'target':np.squeeze(pred), 'label':np.squeeze(y.detach().cpu().numpy())}\n      \n      self.epoch_end_output.append({k:v for k,v in test_log.items()})\n      return test_log\n\n  def label_processor(self, probs, gt):\n    pr = probs.sigmoid().detach().cpu().numpy()\n    la = gt.detach().cpu().numpy()\n    return pr, la\n\n  def distributed_output(self, outputs):\n    if torch.distributed.is_initialized():\n      print('TORCH DP')\n      torch.distributed.barrier()\n      gather = [None] * torch.distributed.get_world_size()\n      torch.distributed.all_gather_object(gather, outputs)\n      outputs = [x for xs in gather for x in xs]\n    return outputs\n\n  def epoch_end(self, mode, outputs):\n    if self.distributed_backend:\n      outputs = self.epoch_end_output\n    avg_loss = torch.Tensor([out[f'{mode}_loss'].mean() for out in outputs]).mean()\n    probs = torch.cat([torch.tensor(out['probs']) for out in outputs], dim=0)\n    gt = torch.cat([torch.tensor(out['gt']) for out in outputs], dim=0)\n    pr, la = self.label_processor(torch.squeeze(probs), torch.squeeze(gt))\n    pr = np.nan_to_num(pr, 0.5)\n    roc_auc = torch.tensor(roc_auc_score(la, pr))\n    print(f'Epoch: {self.current_epoch} Loss : {avg_loss.numpy():.2f}, roc_auc: {roc_auc:.4f}')\n    logs = {f'{mode}_loss': avg_loss, f'{mode}_roc_auc': roc_auc}\n    self.log(f'{mode}_loss_fold_{self.fold}', avg_loss)\n    self.log( f'{mode}_roc_auc_fold_{self.fold}', roc_auc)\n    self.epoch_end_output = []\n    return pr, la, {f'avg_{mode}_loss': avg_loss, 'log': logs}\n\n  def validation_epoch_end(self, outputs):\n    _, _, log_dict = self.epoch_end('val', outputs)\n    self.epoch_end_output = []\n    return log_dict\n\n  def test_epoch_end(self, outputs):\n    if self.distributed_backend:\n      outputs = self.epoch_end_output\n    ids = np.array([out['id'] for out in outputs]).reshape(-1)\n    targets = np.array([out['target'] for out in outputs]).reshape(-1)\n    try:\n      labels = np.array([out['label'] for out in outputs]).reshape(-1)\n    except:\n      labels = None\n    if labels is not None:\n      zippedList =  list(zip(ids, targets, labels))\n      temp_df = pd.DataFrame(zippedList, columns = ['id','target', 'label'])\n      temp_df.to_csv(f'oof_{self.fold}.csv', index=False)\n      self.epoch_end_output = []\n    else:\n      zippedList =  list(zip(np.concatenate(ids), np.concatenate(targets)))\n      temp_df = pd.DataFrame(zippedList, columns = ['id','target'])\n      temp_df.to_csv(f'submission_{self.fold}.csv', index=False)", "sub_path": "SETIModule.py", "file_name": "SETIModule.py", "file_ext": "py", "file_size_in_byte": 6046, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pytorch_lightning.LightningModule", "line_number": 10, "usage_type": "attribute"}, {"api_name": "torch.squeeze", "line_number": 60, "usage_type": "call"}, {"api_name": "random.choices", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.nan_to_num", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.distributed.is_initialized", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 109, "usage_type": "attribute"}, {"api_name": "torch.distributed.barrier", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 111, "usage_type": "attribute"}, {"api_name": "torch.distributed.get_world_size", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 112, "usage_type": "attribute"}, {"api_name": "torch.distributed.all_gather_object", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 113, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 120, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 122, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 122, "usage_type": "call"}, {"api_name": "torch.squeeze", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.nan_to_num", "line_number": 124, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 125, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 153, "usage_type": "call"}]}