\\n\"\n\n return source\n\n def __obtain_text(self, node: html.HtmlElement) -> str:\n # Prepare text to be analysed\n text = self.__disassembly_text(node, format_text=True)\n text = utils.remove_spaces(text)\n\n # Check if disassembled text have content\n if not text:\n raise exceptions.EmptyNodeContent()\n\n # Organize source\n source = \"
\" + text + \"
\\n\"\n\n return source\n\n def __disassembly_text(self, node: html.HtmlElement, is_first=True,\n format_text=False) -> str:\n text = str()\n\n # Tags to ignore\n if node.tag in (\"script\",):\n return text\n\n # If format text is enabled:\n if format_text:\n # Check if node is a breakspace or a text\n if node.tag == \"br\":\n text = \" \"\n elif node.text:\n text += node.text\n\n # If text use tag strong, make it strong (bold)\n if node.tag == \"strong\":\n text = \"\" + text + \"\"\n # If text use em tag, make it em (italic)\n elif node.tag == \"em\":\n text = \"\" + text + \"\"\n elif node.text:\n text += node.text\n\n # Check if node have childrens and retrieve its texts\n childrens = node.xpath(\"./*\")\n if childrens:\n for child in childrens:\n text += self.__disassembly_text(child, False)\n\n # If is a children, also check the tail\n if not is_first:\n if node.tail:\n text += node.tail\n\n return text\n\n def __obtain_image(self, node: html.HtmlElement) -> str:\n # Check if node have an image url\n if \"src\" not in node.attrib.keys():\n raise exceptions.EmptyNodeContent()\n else:\n imgurl = node.get(\"src\")\n\n # Organize source\n filename = utils.filename_from_url(imgurl)\n source = \"\" + \\\n \" \\n\"\n\n return source\n\n def __obtain_caption(self, node: html.HtmlElement) -> str:\n # Prepare text to be analysed\n text = self.__disassembly_text(node)\n text = utils.remove_spaces(text)\n\n # Check if node have content\n if not text:\n raise exceptions.EmptyNodeContent()\n\n # Organize source\n source = \"
\" + text + \\\n \"
\\n\"\n\n return source\n\n def __is_valid_content(self, content: html.HtmlElement) -> bool:\n path = \"//p\"\n paragraphs = content.xpath(path)\n if len(paragraphs) >= 2:\n return True\n else:\n return False\n\n def obtain_title(self, element: html.HtmlElement) -> str:\n result = element.xpath(self.title_path)\n if result:\n pre_title = self.__disassembly_text(result[0])\n if pre_title:\n title = pre_title\n else:\n raise exceptions.TitleNotAvailable()\n else:\n raise exceptions.TitleNotAvailable()\n\n return title\n\n def obtain_author(self, element: html.HtmlElement) -> str:\n try:\n result = element.xpath(self.author_path)\n except Exception:\n raise exceptions.AuthorNotAvailable()\n if result:\n pre_author = self.__disassembly_text(result[0])\n if pre_author:\n author = pre_author\n else:\n raise exceptions.AuthorNotAvailable()\n else:\n raise exceptions.AuthorNotAvailable()\n\n return author\n\n def obtain_date(self, element: html.HtmlElement) -> datetime.datetime:\n try:\n result = element.xpath(self.date_path)\n except Exception:\n raise exceptions.DateNotAvailable()\n if result:\n # Transform to date\n date_text = result[0]\n date = self.__parse_to_date(date_text, self.date_format)\n else:\n raise exceptions.DateNotAvailable()\n\n return date\n\n def __parse_to_date(self, date_text: str, date_format: str) \\\n -> datetime.datetime:\n date_cleared = utils.remove_spaces(date_text)\n try:\n date = datetime.datetime.strptime(date_cleared, date_format)\n except Exception:\n raise exceptions.DateNotAvailable()\n return date\n","repo_name":"jonasbastos96/webclipper","sub_path":"webclipper/model/structure.py","file_name":"structure.py","file_ext":"py","file_size_in_byte":8667,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"90"}
+{"seq_id":"25731808064","text":"#coding=utf-8\n'''\n# PiApp's view here!\n# The view that return json data is in api.py\n\n# Any issues or improvements please contact jacob-chen@iotwrt.com\n'''\n\nfrom django.shortcuts import render\nfrom django import forms\nfrom django.template import RequestContext\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.template.loader import get_template\nfrom django.core.paginator import Paginator\nfrom django.core.urlresolvers import reverse_lazy\nfrom django.contrib.auth import authenticate,login,logout\nfrom django.contrib.auth.models import User\nfrom django.contrib.auth.decorators import login_required \n\nimport os\nimport json as simplejson \n\nfrom PiHome import settings\nfrom PiApp.forms import *\nfrom PiApp.models import *\nfrom common.api import client, notification\nfrom common import globaldata\nfrom common import utils\n\n\n\n@login_required \ndef dashboard(request, title='dashboard', belong=None):\n pisettings_instance = globaldata.getclient()\n user_count = PiUser.objects.count() \n device_count = Device.objects.count() \n if(client.socket_test(pisettings_instance.ip, pisettings_instance.port)):\n connection = \"True\"\n else:\n connection = \"False\"\n\n applist = globaldata.pci_list.applist()\n app_num = len(applist)\n media_root = settings.MEDIA_ROOT\n DEVICE = Device.objects.all()\n try:\n home_instance = Home.objects.get(id =1)\n home_exist = True\n except:\n home_exist = False\n\n #logfile\n pihome_log_file = open(globaldata.BASE_DIR + '/log/pihome.log', 'r')\n pihome_log_lines = pihome_log_file.readlines()\n pihome_log = utils.lineslimit(pihome_log_lines, 100)\n cpp_log_file = open(globaldata.BASE_DIR + '/log/pihome.log', 'r')\n cpp_log_lines = cpp_log_file.readlines()\n cpp_log = utils.lineslimit(cpp_log_lines, 100)\n\n t = get_template('dashboard/dashboard.html')\n c = RequestContext(request,locals())\n return HttpResponse(t.render(c))\n\n@login_required \ndef notification_view(request, title='notification', belong=None):\n others = notification.get_all(request.user.id)\n\n unread = notification.get_unread_clear(request.user.id)\n \n t = get_template('notification.html')\n c = RequestContext(request,locals())\n return HttpResponse(t.render(c))\n\ndef fzf_view(request, title='404', belong=None):\n t = get_template('404.html')\n c = RequestContext(request,locals())\n return HttpResponse(t.render(c))\n\ndef fzz_view(request, title='500', belong=None):\n t = get_template('500.html')\n c = RequestContext(request,locals())\n return HttpResponse(t.render(c))\n\n@login_required \ndef settings_account_view(request, title='account', belong=['settings']):\n user = PiUser.objects.get(username = request.user.username)\n form = PiAccountForm(request.POST or None, instance = user)\n if form.is_valid():\n user.first_name=form.cleaned_data['first_name']\n user.last_name=form.cleaned_data['last_name'] \n if(form.cleaned_data['password1'] != \"\"): \n password=form.cleaned_data['password1']\n if password != None:\n user.set_password(password)\n user.save()\n t = get_template('settings/account.html')\n c = RequestContext(request,locals())\n return HttpResponse(t.render(c))\n\n@login_required \ndef settings_general_view(request, title='general', belong=['settings']):\n pisettings_instance = globaldata.getclient()\n form = PiSettingsForm(request.POST or None, instance = pisettings_instance)\n if form.is_valid():\n form.save()\n # form = PiSettingsForm(instance = pisettings_instance)\n t = get_template('settings/general.html')\n c = RequestContext(request,locals())\n return HttpResponse(t.render(c))\n\n@login_required \ndef status_process_view(request, title='process', belong=['status']):\n info = os.popen('ps aux').readlines()\n i = 0\n process_info = list()\n for line in info :\n i += 1\n if i > 1:\n get = line.split()\n a = dict()\n a['pid'] = get[1]\n a['owner'] = get[0]\n a['command'] = get[10]\n for item in get[11:]:\n a['command'] += ' ' + item\n a['cpu'] = get[2]\n a['memory'] = get[3] \n process_info.append(a) \n t = get_template('status/process.html')\n c = RequestContext(request,locals())\n return HttpResponse(t.render(c))\n\n@login_required \ndef status_about_view(request, title='about', belong=['status']): \n t = get_template('status/about.html')\n c = RequestContext(request,locals())\n return HttpResponse(t.render(c))\n\n@login_required \ndef status_default_view(request, title='default', belong=['status']):\n pisettings_instance = globaldata.getclient()\n\n message = { \"title\" : \"status\"}\n message['cmd'] = 'get';\n\n pi_ret = client.socketjson_send_recv(pisettings_instance.ip, pisettings_instance.port, message)\n\n t = get_template('status/default.html')\n c = RequestContext(request,locals())\n return HttpResponse(t.render(c))\n\n@login_required \ndef status_dmesg_view(request, title='kernel log', belong=['status']):\n dmesg = os.popen(\"dmesg\")\n log = dmesg.read() \n t = get_template('status/dmesg.html')\n c = RequestContext(request,locals())\n return HttpResponse(t.render(c))\n\n@login_required \ndef nas_video_view(request, title='video', belong=['nas']):\n minidlna_url = request.get_host()\n minidlna_url = minidlna_url[ :minidlna_url.find(':')]+ ':8200'\n\n t = get_template('nas/video.html')\n c = RequestContext(request,locals())\n return HttpResponse(t.render(c))\n\n@login_required \ndef nas_download_view(request, title='download', belong=['nas']):\n t = get_template('nas/download.html')\n c = RequestContext(request,locals())\n return HttpResponse(t.render(c))\n\n@login_required \ndef nas_file_view(request, title='filebrowser', belong=['nas']):\n t = get_template('nas/file.html')\n c = RequestContext(request,locals())\n return HttpResponse(t.render(c))\n\n@login_required \ndef webssh_view(request, title='webssh', belong=None):\n ssh_url = request.get_host()\n ssh_url = ssh_url[ :ssh_url.find(':')]+ ':8001'\n\n t = get_template('webssh/webssh.html')\n c = RequestContext(request,locals())\n return HttpResponse(t.render(c))\n\n@login_required \ndef home_view(request, title='my house', belong=None):\n try:\n home_instance = Home.objects.get(id = 1)\n img_url = '/media/' + str(home_instance.img)\n home_exist = True\n device_all = Device.objects.all() \n except:\n home_exist = False\n t = get_template('home/home.html')\n c = RequestContext(request,locals())\n return HttpResponse(t.render(c))\n\n@login_required \ndef add_home_view(request, title='add home', belong=['my house']):\n try:\n home_instance = Home.objects.get(id =1)\n form = HomeForm(request.POST or None, request.FILES, instance = home_instance) \n home_exist = True \n except:\n form = HomeForm(request.POST or None, request.FILES)\n home_exist = False \n\n if request.method != 'POST' and home_exist == True:\n form = HomeForm(None, instance = home_instance)\n elif request.method != 'POST' and home_exist == False:\n form = HomeForm(None)\n\n if form.is_valid():\n home = form.save() \n\n if home_exist:\n img_url = '/media/' + str(home_instance.img)\n else:\n img_url = '/static/img/house_plan.jpg'\n \n t = get_template('home/add_home.html')\n c = RequestContext(request,locals())\n return HttpResponse(t.render(c))\n\n@login_required \ndef home_help_view(request, title='document', belong=['my house']):\n t = get_template('home/help.html')\n c = RequestContext(request,locals())\n return HttpResponse(t.render(c))\ndef home_help_en_view(request, title='document-en', belong=['my house']):\n t = get_template('home/help_en.html')\n c = RequestContext(request,locals())\n return HttpResponse(t.render(c))\n\n@login_required \ndef device_view(request):\n device_id = request.GET['device_id']\n device_instance = Device.objects.get(id = device_id)\n sensors = device_instance.sensor.all() \n\n t = get_template('home/device.html')\n c = RequestContext(request,locals())\n return HttpResponse(t.render(c))\n\n@login_required \ndef add_device_view(request, title='add device', belong=['my house']):\n try:\n home_instance = Home.objects.get(id =1)\n img_url = '/media/' + str(home_instance.img)\n except:\n return HttpResponseRedirect('/PiApp/home/index/')\n form = DeviceForm(request.POST or None)\n if form.is_valid():\n form.save()\n return HttpResponseRedirect('/PiApp/home/manage_device/')\n t = get_template('home/add.html')\n c = RequestContext(request,locals())\n return HttpResponse(t.render(c))\n\n@login_required \ndef manage_device_view(request, title='manage', belong=['my house']):\n device_all = Device.objects.all() \n count = Device.objects.count() \n t = get_template('home/manage.html')\n c = RequestContext(request,locals())\n return HttpResponse(t.render(c))\n\n@login_required \ndef edit_device_view(request, title='edit device', belong=['my house', 'manage']):\n try:\n home_instance = Home.objects.get(id = 1)\n img_url = '/media/' + str(home_instance.img)\n except:\n return HttpResponseRedirect('/PiApp/home/index/')\n\n device_id = request.GET['device_id']\n device_instance = Device.objects.get(id = device_id)\n form = DeviceForm(request.POST or None, instance = device_instance)\n\n if form.is_valid():\n form.save()\n return HttpResponseRedirect('/PiApp/home/manage_device/')\n\n t = get_template('home/add.html')\n c = RequestContext(request,locals())\n return HttpResponse(t.render(c))\n\n@login_required \ndef add_sensor_view(request, title='add sensor', belong=['my house', 'manage']):\n device_id = request.GET['device_id']\n get_device = Device.objects.get(id = device_id)\n form = SensorForm(request.POST or None)\n # Sensor.objects.create(name=\"11\",describe=\"111\", sensor_class=\"s\", device = get_device)\n if form.is_valid():\n sensor = form.save(commit=False)\n sensor.device = get_device\n sensor.save()\n return HttpResponseRedirect('/PiApp/home/manage_device/')\n\n t = get_template('home/add_sensor.html')\n c = RequestContext(request,locals())\n return HttpResponse(t.render(c))\n\n@login_required \ndef edit_sensor_view(request, title='edit sensor', belong=['my house', 'manage']):\n sensor_id = request.GET['sensor_id']\n get_sensor = Sensor.objects.get(id = sensor_id)\n form = SensorForm(request.POST or None, instance = get_sensor)\n # Sensor.objects.create(name=\"11\",describe=\"111\", sensor_class=\"s\", device = get_device)\n if form.is_valid():\n sensor = form.save(commit=False)\n sensor.save()\n return HttpResponseRedirect('/PiApp/home/manage_device/')\n\n t = get_template('home/edit_sensor.html')\n c = RequestContext(request,locals())\n return HttpResponse(t.render(c))\n\n@login_required \ndef sensor_data_view(request, title='sensor data', belong=['my house', 'manage']):\n sensor_id = request.GET['sensor_id']\n get_sensor = Sensor.objects.get(id = sensor_id)\n\n if get_sensor.sensor_class == 'n':\n datapoint = NumDatapoint.objects.filter(sensor = get_sensor)\n elif get_sensor.sensor_class == 'p': \n datapoint = PicDatapoint.objects.filter(sensor = get_sensor)\n\n domain = request.get_host() + settings.MEDIA_URL\n t = get_template('home/sensor_data.html')\n c = RequestContext(request,locals())\n return HttpResponse(t.render(c))\n'''\n user admin\n'''\ndef login_view(request): \n if request.user.is_authenticated():\n return HttpResponseRedirect(\"/PiApp/dashboard/\") \n if request.method != 'POST': \n try: \n next_url = request.GET['next']\n except:\n next_url = ''\n t = get_template('login.html')\n c = RequestContext(request,locals()) \n return HttpResponse(t.render(c)) \n user = authenticate(username=request.POST['username'], password=request.POST['password'])\n if user is not None:\n login(request, user) \n ret = {'msg':'ok'}\n else:\n ret = {'msg':'fail'}\n return HttpResponse(simplejson.dumps(ret)) \n\ndef logout_view(request):\n logout(request)\n return HttpResponseRedirect(\"/PiApp/dashboard/\") \n\ndef register_view(request): \n pisettings_instance = globaldata.getclient()\n if pisettings_instance.enable_register != True:\n return HttpResponse(\"You are not be allowed to register\") \n form = PiRegisterForm(request.POST or None)\n if form.is_valid():\n first_name=form.cleaned_data['first_name']\n last_name=form.cleaned_data['last_name'] \n email = form.cleaned_data['email'] \n password=form.cleaned_data['password1'] \n try:\n user = PiUser.objects.create_user(email, email, password) \n if user is not None: \n user.first_name = first_name\n user.last_name = last_name\n user.is_staff = True \n user.save() \n user = authenticate(username=email, password=password)\n login(request, user) \n ret = 'Ok'\n t = get_template('register_return.html')\n c = RequestContext(request,locals())\n return HttpResponse(t.render(c))\n except: \n ret = 'Fail'\n t = get_template('register_return.html')\n c = RequestContext(request,locals())\n return HttpResponse(t.render(c))\n else:\n t = get_template('register.html')\n c = RequestContext(request,locals())\n return HttpResponse(t.render(c)) \n\n\n# def requires_login(view):\n# def new_view(request, *args, **kwargs):\n# return view(request, *args, **kwargs) \n# return new_view \n# globaldata.getLogger().debug(\"avail space\" + str(avail)); \n","repo_name":"wzyy2/PiBox","sub_path":"PiBox/PiHome/PiApp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":14184,"program_lang":"python","lang":"en","doc_type":"code","stars":197,"dataset":"github-code","pt":"90"}
+{"seq_id":"74605499175","text":"import pandas as pd\n# from pandas import read_excel, ExcelFile, ExcelWriter \n\nprint(\"请将excel放在此程序同文件夹内\")\nprint('请输入excel文件的完整名')\nexcel_name = input(\"如(2019年中考成绩.xls): \")\n# excel_name = 'test.xls'\n\nprint('请输入excel中的关键字')\nkeyword = input('如(学校): ')\n# keyword = '学校'\n\ntry:\n print(\"参数校验中\")\n \n # 先读取所有sheet\n sheet_list = []\n sheets = pd.ExcelFile(excel_name).sheet_names\n for sheet in sheets:\n df = pd.read_excel(excel_name, sheetname=sheet)\n sheet_list.append(df)\n \n schools = list(set(sheet_list[0][keyword].tolist()))\n\n print(\"校验\" + excel_name + \"成功\")\n \n for school in schools:\n \n filename = './' + school + '.xls'\n writer = pd.ExcelWriter(filename)\n persent = str(int(schools.index(school) / len(schools) * 100))\n print('正在处理: ' + school + \" ###总进度: \" + persent + \"%\")\n for sheet in sheets:\n df = sheet_list[sheets.index(sheet)]\n df_school = df[df[keyword] == school]\n df_school.to_excel(writer, sheet_name=sheet, index=False)\n writer.save()\n writer.close()\n \n print(\"处理完成!\")\n x = input()\n \nexcept:\n print(\"输入有误或excel格式不正确, 请重新检查\")\n x = input()\n\n","repo_name":"boatfu/scripts","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1368,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"90"}
+{"seq_id":"7727748355","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Aug 22 11:29:42 2023\n\n@author: zhanghai\n\"\"\"\n\nimport numpy as np\nfrom scipy.optimize import linprog\nfrom scipy.optimize import minimize\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics.pairwise import euclidean_distances\n\n\nclass ProbabilitySetsModel:\n def __init__(self, n_trees=100, alpha=0, p_dist_type='SE', min_samples_leaf=3, random_state=42):\n self.n_trees = n_trees\n self.alpha = alpha\n self.p_dist_type = p_dist_type\n self.min_samples_leaf = min_samples_leaf\n self.classes = None\n self.n_class = None\n self.rf = RandomForestClassifier(n_estimators=n_trees, min_samples_leaf=min_samples_leaf, random_state=random_state)\n \n \n def fit(self, train_x, train_y):\n self.rf.fit(train_x, train_y)\n \n self.classes = self.rf.classes_\n self.n_class = len(self.rf.classes_)\n \n \n def predict(self, X, prediction_type='Maximality'):\n if X.ndim == 1:\n X = X.reshape(1, -1)\n n_instance = X.shape[0]\n all_probability_sets = np.zeros((n_instance, self.n_trees, self.n_class))\n for t in range(self.n_trees):\n tree = self.rf.estimators_[t]\n all_probability_sets[:,t,:] = tree.predict_proba(X)\n \n predictions = []\n n_equal = 0\n class2check = 0\n for i in range(n_instance):\n probability_set = all_probability_sets[i,:,:]\n \n if self.p_dist_type == 'L1':\n def L1_distance(p_star, probability_set):\n total_distance = 0\n total_distance = np.sum(np.abs(probability_set - p_star))\n# for p_point in probability_set:\n# dist_distance = np.sum(np.abs(p_point - p_star))\n# total_distance += dist_distance\n return total_distance\n\n constraints = [{'type': 'eq', 'fun': lambda x: np.sum(x) - 1},\n {'type': 'ineq', 'fun': lambda x: x}]\n\n result = minimize(L1_distance, np.ones(self.n_class)/self.n_class, \n args=(probability_set,), constraints=constraints)\n \n# p_star = result.x.round(8)\n p_star = result.x\n probability_set = np.concatenate([p_star.reshape((1,-1)), probability_set])\n dists_to_p_star = abs(probability_set - p_star.reshape((1,-1))).sum(axis=1)\n \n elif self.p_dist_type == 'KLD':\n def KL_divergence(p_star, probability_set):\n epsilon = 1e-10\n total_distance = 0\n p_star = np.where(p_star < epsilon, epsilon, p_star)\n probability_set = np.where(probability_set < epsilon, epsilon, probability_set)\n total_distance = np.sum(probability_set * np.log(probability_set/p_star))\n# for p_point in probability_set:\n# p_point = np.where(p_point < epsilon, epsilon, p_point)\n# dist_distance = np.sum(p_point * np.log(p_point/p_star))\n# total_distance += dist_distance\n return total_distance\n \n constraints = [{'type': 'eq', 'fun': lambda x: np.sum(x) - 1},\n {'type': 'ineq', 'fun': lambda x: x}]\n\n result = minimize(KL_divergence, np.ones(self.n_class)/self.n_class, \n args=(probability_set,), constraints=constraints)\n \n# p_star = result.x.round(5)\n p_star = result.x\n epsilon = 1e-10\n p_star = np.where(p_star < epsilon, epsilon, p_star)\n probability_set = np.where(probability_set < epsilon, epsilon, probability_set)\n probability_set = np.concatenate([p_star.reshape((1,-1)), probability_set])\n dists_to_p_star = (probability_set*np.log(probability_set/(p_star.reshape((1,-1))))).sum(axis=1)\n else: # SE\n p_star = probability_set.mean(axis=0)\n probability_set = np.concatenate([p_star.reshape((1,-1)), probability_set])\n dists_to_p_star = euclidean_distances(p_star.reshape((1,-1)), probability_set)[0]\n \n if prediction_type=='precise':\n predictions.append([self.classes[p_star.argmax()]])\n else: # imprecise predictions\n probability_set = probability_set[np.argsort(dists_to_p_star),:]\n rest_probability_set = probability_set[0:int((1-self.alpha)*self.n_trees)+1, :]\n# rest_probability_set = np.vstack((rest_probability_set, p_star))\n# print(rest_probability_set.shape)\n# print(p_star)\n# print(rest_probability_set)\n \n if prediction_type=='Maximality':\n prediction_M_indicator = np.ones(self.n_class)\n \n for j in range(self.n_class):\n compared_classes = np.setdiff1d(range(self.n_class), [j])\n for k in compared_classes:\n if np.all(rest_probability_set[:,j] <= rest_probability_set[:,k]):\n prediction_M_indicator[j] = -1\n prediction_M_index = np.where(prediction_M_indicator==1)[0]\n predictions.append(self.classes[prediction_M_index])\n else: # E-Admissibility\n prediction_E_indicator = np.zeros(self.n_class)\n prediction_M_indicator = np.ones(self.n_class)\n for j in range(self.n_class):\n compared_classes = np.setdiff1d(range(self.n_class), [j])\n for k in compared_classes:\n if np.all(rest_probability_set[:,j] <= rest_probability_set[:,k]):\n prediction_M_indicator[j] = -1\n prediction_E_indicator[j] = -1\n break\n \n if prediction_E_indicator[j] == -1:\n continue\n if rest_probability_set[:,j].max() < 1/self.n_class:\n prediction_E_indicator[j] = -1\n continue\n if rest_probability_set[:,j].max() > 0.5:\n prediction_E_indicator[j] = 1\n continue\n if np.any(np.sum(rest_probability_set[:,j].reshape((-1,1)) > rest_probability_set, axis=1)==self.n_class-1):\n prediction_E_indicator[j] = 1\n \n classes2check = np.where(prediction_E_indicator==0)[0]\n if len(classes2check) == 0:\n prediction_E_index = np.where(prediction_E_indicator==1)[0]\n else:\n for j in classes2check:\n# k = np.setdiff1d(class2check, [j])[0]\n def check_E_admissibility(distributions, class2check):\n n_distributions = len(distributions) # number of distributions\n K = len(distributions[0]) # number of class\n\n # to maximize the probability of the class to check\n c = -distributions[:, class2check]\n\n # inequality constraint matrix\n A_ub = np.zeros((K, n_distributions))\n for k in range(K):\n A_ub[k, :] = distributions[:, k] - distributions[:, class2check]\n b_ub = np.zeros(K)\n\n # Equality constraints (all weights sum to 1)\n A_eq = np.ones(n_distributions)\n b_eq = 1\n\n # weight bounds\n bounds = [(0, 1)] * n_distributions\n\n # Solve linear programming problems\n result = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=[A_eq], b_eq=[b_eq], bounds=bounds)\n\n if result.success:\n weighted_average = result.x\n return weighted_average\n else:\n return None\n \n result = check_E_admissibility(rest_probability_set, j)\n\n if result is not None:\n prediction_E_indicator[j] = 1\n else:\n prediction_E_indicator[j] = -1\n\n if sum(prediction_E_indicator==-1) < sum(prediction_M_indicator==-1):\n print(prediction_E_indicator)\n print(prediction_M_indicator, '\\n')\n prediction_E_index = np.where(prediction_E_indicator==1)[0]\n predictions.append(self.classes[prediction_E_index])\n\n# print(n_instance, n_equal, class2check) \n return predictions\n \n \n \n def evaluate(self, X_test, y_test, prediction_type='Maximality'):\n predictions = self.predict(X_test, prediction_type)\n precise_predictions = self.rf.predict(X_test)\n rf_acc = sum(precise_predictions==y_test)/len(y_test)\n determinacy = 0\n single_set_accuracy = 0\n set_accuracy = 0\n set_size = 0\n u65 = 0\n u80 = 0\n rf_abstention_acc = 0\n for i in range(len(y_test)):\n prediction = predictions[i]\n precise_prediction = precise_predictions[i]\n if len(prediction) == 1:\n determinacy += 1\n if prediction[0] == y_test[i]:\n single_set_accuracy += 1\n u65 += 1\n u80 += 1\n else:\n set_size += len(prediction)\n if y_test[i] in prediction:\n set_accuracy += 1\n u65 += (-0.6/(len(prediction)**2) + 1.6/len(prediction))\n u80 += (-1.2/(len(prediction)**2) + 2.2/len(prediction))\n if y_test[i] == precise_prediction:\n rf_abstention_acc += 1\n \n n_determinate = determinacy\n n_indeterminate = len(y_test) - determinacy\n \n determinacy /= len(y_test)\n if n_determinate == 0:\n single_set_accuracy = None\n else:\n single_set_accuracy /= n_determinate\n \n if n_indeterminate == 0:\n set_accuracy = None\n set_size = None\n rf_abstention_acc = None\n else:\n set_accuracy /= n_indeterminate\n set_size /= n_indeterminate\n rf_abstention_acc /= n_indeterminate\n \n u65 /= len(y_test)\n u80 /= len(y_test)\n \n return {'rf accuracy': rf_acc,\n 'determinacy': determinacy,\n 'single accuracy': single_set_accuracy,\n 'set accuracy': set_accuracy,\n 'set size': set_size,\n 'u65 score': u65, \n 'u80 score': u80,\n 'rf abstention accuracy': rf_abstention_acc}","repo_name":"Haifei-ZHANG/Probability-Sets-Model","sub_path":"probability_set_model.py","file_name":"probability_set_model.py","file_ext":"py","file_size_in_byte":11740,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"90"}
+{"seq_id":"18369770419","text":"import bisect\nfrom collections import deque\nN = int(input())\nA = [int(input()) for _ in range(N)]\nc = deque()\nc.append(A[0])\nfor i in range(1, N):\n j = bisect.bisect_right(c, A[i]-1)\n if j == 0:\n c.appendleft(A[i])\n else:\n c[j-1] = A[i]\nprint(len(c))\n","repo_name":"Aasthaengg/IBMdataset","sub_path":"Python_codes/p02973/s652003375.py","file_name":"s652003375.py","file_ext":"py","file_size_in_byte":274,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"90"}
+{"seq_id":"19018501575","text":"from collections import deque\nclass Solution:\n def isPossible (self, N, prerequisites):\n count, in_deg_arr, adj_lst, q = 0, [0 for i in range(N)], [[] for i in range(N)], deque()\n for start, end in prerequisites:\n in_deg_arr[end] += 1 ; adj_lst[start].append(end)\n for i in range(N):\n if (in_deg_arr[i] == 0): q.append(i)\n while (q):\n count += 1\n curr_vert = q.popleft()\n for next_vert in adj_lst[curr_vert]:\n in_deg_arr[next_vert] -= 1\n if (in_deg_arr[next_vert] == 0): q.append(next_vert)\n return True if (count == N) else False\n","repo_name":"Tejas07PSK/lb_dsa_cracker","sub_path":"Graph/Find whether it is possible to finish all tasks or not from given dependencies/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":654,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"90"}
+{"seq_id":"43263877611","text":"#input variables to Zapier Step 4: Select Winning Field Values\ninput={\n 'people': 'people_array_string' #from Step 3: Get Duplicate Leads\n }\n\nfrom datetime import datetime\nimport json\nimport re\n\n# -------------- Priority Function Start ------------------------------\n\n# return the index and value (in string format) containing the earliest created at date\ndef createdAt(list, *args):\n time_list = [datetime.strptime(i, '%Y-%m-%dT%H:%M:%SZ') for i in list]\n return [time_list.index(min(time_list)), min(time_list).strftime('%Y-%m-%dT%H:%M:%SZ')]\n\n\n# return the first index and value where the value is non-null and does not contain \"null-like\" values\ndef notNull(list, *args):\n new_list = [x.lower() for x in list]\n\n crap = [\"empty\", \"unknown\", \"n/a\", \"[\", \"]\", 'none']\n\n for i in new_list:\n good = \"true\"\n for j in crap:\n if j in i:\n good = False\n if good:\n return [new_list.index(i), list[new_list.index(i)]]\n\n return [0, list[0]]\n\n\n# define the prioritized values for certain lead fields in order of descreasing priority from left to right\npriority_dict = {\n 'leadSource': [\"Advertising\", \"Paid Search\", \"Organic\", \"Marketing Generated\", \"Event\", \"Tradeshow\", \"Content\",\n \"Webinar\", \"Referral\", \"Sales Generated\", \"Direct\"],\n 'leadStatus': ['Disqualified', 'Customer', 'Closed Won', 'SQL', 'SDR Engaged', 'SAL', 'MQL', 'ReNurture', 'SSL',\n 'Prospects', 'Prospects Cold', 'Known', 'Not a Lead']\n}\n\n\n# use the priority_dict function to find the highest priority value and corresponding index among the input\n# lead values for a certain field. Else if none of the input lead values has a prioritized value return the first\n# non-null value and index\ndef priority(list, line):\n for j in priority_dict[line]:\n for i in list:\n if j in i:\n return [list.index(i), i]\n\n return (notNull(list))\n\n\n# return the first TRUE value and its index. True is prioritized because this function is used for the unsubscribed\n# account blocked fields where it is important to favor TRUE over FALSE\ndef boolTest(list, *args):\n for i in list:\n if i is True:\n return [list.index(i), i]\n\n\n# define the prioritization function that will be called for each field\nrules = {\n 'createdAt': createdAt,\n 'sfdcLeadId': notNull,\n 'firstName': notNull,\n 'lastName': notNull,\n 'leadSource': priority,\n 'leadStatus': priority,\n 'unsubscribed': boolTest\n}\n\n\n# create a new list with None converted to string format and then pass this list to the rules dictionary so\n# the correct prioritzation function is used on the lead\ndef ruler(line, line_list):\n formatted_list = []\n for x in line_list:\n if x is None:\n formatted_list.append('None')\n else:\n formatted_list.append(x)\n return rules[line](formatted_list, line)\n\n\n# -------------- Priority Function End ------------------------------\n\npeople = re.findall('{.*?}', input['people'])\n\nfield_dict = {'id': [], 'sfdcLeadId': [], 'email': [], 'createdAt': [], 'firstName': [], 'lastName': [],'leadSource': [], 'Lead_Source_Detail__c': [], 'unsubscribed': [], 'leadStatus': []}\n\nfinal_dict = field_dict.fromkeys(field_dict, [])\n\nfor p in people:\n for key in json.loads(p):\n field_dict[key] = field_dict[key] + [json.loads(p)[key]]\n\nfor line in field_dict:\n if all(elem == field_dict[line][0] for elem in field_dict[line]) and line != 'sfdcLeadId':\n final_dict[line] = field_dict[line][0]\n else:\n if line in ['email', 'id', 'Lead_Source_Detail__c']:\n pass\n elif line == 'sfdcLeadId':\n [index, value] = ruler(line, field_dict[line])\n if value is not None:\n final_dict[line] = value\n final_dict['id'] = field_dict['id'][index]\n else:\n [index, value] = ruler(\"createdAt\", field_dict[\"createdAt\"])\n final_dict[\"createdAt\"] = value\n final_dict['id'] = field_dict['id'][index]\n elif line == 'leadSource':\n [index, value] = ruler(line, field_dict[line])\n final_dict[line] = value\n final_dict[\"Lead_Source_Detail__c\"] = field_dict[\"Lead_Source_Detail__c\"][index]\n else:\n final_dict[line] = ruler(line, field_dict[line])[1]\n\ndateTimeObj = datetime.now()\nlog = dateTimeObj.strftime(\"%m-%d-%Y_%H:%M:%S\") + \"\\n\\nWinning and losing lead information:\\n\" + str(field_dict) + \"\\n\\nWinning ID with field and value combinations to be updated after merging:\\n\"+str(final_dict) + \"\\n\\n\"\n\nreturn{'field_dict':str(field_dict),'final_dict':str(final_dict), 'log':log}\n","repo_name":"tyron-pretorius/zapier","sub_path":"automatic_marketo_merge/select_winning_values.py","file_name":"select_winning_values.py","file_ext":"py","file_size_in_byte":4710,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"90"}
+{"seq_id":"73981007018","text":"import pickle\nimport utils\n\n\noutput_path = 'output/instances.txt'\n\ninstances = pickle.load(open('ulm/instances.bin', 'rb'))\n\n\ncount = 0\nneeded = 0\n\nwith open(output_path, 'w') as outfile:\n for instance_id, instance in instances.items():\n\n sentence_tokens = []\n sentence_lemmas = []\n sentence_pos = []\n annotations = []\n\n\n for token in instance.tokens:\n\n sentence_tokens.append(token.text)\n sentence_lemmas.append(token.lemma)\n sentence_pos.append(token.pos)\n annotations.append(list(token.synsets))\n\n needed += len(token.synsets)\n\n for (target_lemma,\n target_pos,\n token_annotation,\n sentence_tokens,\n training_example,\n target_index) in utils.generate_training_instances_v2(sentence_tokens,\n sentence_lemmas,\n sentence_pos,\n annotations):\n\n outfile.write(training_example + '\\n')\n count += 1\n\n\nassert needed == count\n","repo_name":"cltl/pwgc","sub_path":"convert_ulm_to_lstm_format.py","file_name":"convert_ulm_to_lstm_format.py","file_ext":"py","file_size_in_byte":1198,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"90"}
+{"seq_id":"16542754619","text":"import wasp\nimport icons\nimport fonts\n\nclass StopwatchApp():\n \"\"\"Stopwatch application.\"\"\"\n # Stopwatch requires too many pixels to fit into the launcher\n\n NAME = 'Stopclock'\n ICON = icons.app\n\n def __init__(self):\n self._timer = wasp.widgets.Stopwatch(120-36)\n self._reset()\n\n def foreground(self):\n \"\"\"Activate the application.\"\"\"\n wasp.system.bar.clock = True\n self._draw()\n wasp.system.request_tick(97)\n wasp.system.request_event(wasp.EventMask.TOUCH |\n wasp.EventMask.BUTTON |\n wasp.EventMask.NEXT)\n\n def sleep(self):\n return True\n\n def wake(self):\n self._update()\n\n def swipe(self, event):\n \"\"\"Handle NEXT events by augmenting the default processing by resetting\n the count if we are not currently timing something.\n\n No other swipe event is possible for this application.\n \"\"\"\n if not self._timer._started_at:\n self._reset()\n return True # Request system default handling\n\n def press(self, button, state):\n if not state:\n return\n\n if self._timer.started:\n self._timer.stop()\n else:\n self._timer.start()\n\n def touch(self, event):\n if self._timer.started:\n self._splits.insert(0, self._timer.count)\n del self._splits[4:]\n self._nsplits += 1\n else:\n self._reset()\n\n self._update()\n self._draw_splits()\n\n def tick(self, ticks):\n self._update()\n\n def _reset(self):\n self._timer.reset()\n self._splits = []\n self._nsplits = 0\n\n def _draw_splits(self):\n draw = wasp.watch.drawable\n splits = self._splits\n if 0 == len(splits):\n draw.fill(0, 0, 120, 240, 120)\n return\n y = 240 - 6 - (len(splits) * 24)\n\n draw.set_font(fonts.sans24)\n draw.set_color(wasp.system.theme('mid'))\n\n n = self._nsplits\n for i, s in enumerate(splits):\n centisecs = s\n secs = centisecs // 100\n centisecs %= 100\n minutes = secs // 60\n secs %= 60\n\n t = '# {} {:02}:{:02}.{:02}'.format(n, minutes, secs, centisecs)\n n -= 1\n\n w = fonts.width(fonts.sans24, t)\n draw.string(t, 0, y + (i*24), 240)\n\n def _draw(self):\n \"\"\"Draw the display from scratch.\"\"\"\n draw = wasp.watch.drawable\n draw.fill()\n\n wasp.system.bar.draw()\n self._timer.draw()\n self._draw_splits()\n\n def _update(self):\n wasp.system.bar.update()\n self._timer.update()\n","repo_name":"wasp-os/wasp-os","sub_path":"apps/stopwatch.py","file_name":"stopwatch.py","file_ext":"py","file_size_in_byte":2717,"program_lang":"python","lang":"en","doc_type":"code","stars":752,"dataset":"github-code","pt":"90"}
+{"seq_id":"18038798199","text":"S = input()\n'''\ndp[i]はSのi文字目（1~Sの長さ)までを表せるかどうか\n'''\ndp = [0] * (len(S) + 1)\ndp[0] = 1\ncheck = \"NO\"\n\nwords = [\"dream\", \"dreamer\", \"erase\", \"eraser\"]\n\nfor i in range(len(S)):\n if dp[i] == 0:\n continue\n for w in words:\n if S[i:i+len(w)] == w:\n dp[i+len(w)] = 1\n if dp[len(S)] == 1:\n check = \"YES\"\n break\n\nprint(check)\n","repo_name":"Aasthaengg/IBMdataset","sub_path":"Python_codes/p03854/s588602077.py","file_name":"s588602077.py","file_ext":"py","file_size_in_byte":400,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"90"}
+{"seq_id":"70931197736","text":"from distutils.core import setup\n\n\nVERSION=\"0.0.1\"\n\nsetup(\n name = 'file_cache',\n version = VERSION,\n packages = ['file_cache'],\n license='MIT', # Chose a license from here: https://help.github.com/articles/licensing-a-repository\n description = 'Decorator to cache on disk',\n author = 'Ethan Swartzentruber',\n author_email = 'eswartzen@gmail.com',\n url = 'https://github.com/epswartz/file_cache',\n download_url=f'https://github.com/epswartz/block_distortion/archive/{VERSION}.zip',\n keywords = ['cache', 'disk', 'decorator'],\n install_requires=[],\n classifiers=[\n 'Development Status :: 4 - Beta', # \"3 - Alpha\", \"4 - Beta\" or \"5 - Production/Stable\"\n 'Intended Audience :: Developers',\n 'Topic :: Software Development :: Libraries :: Python Modules',\n 'License :: OSI Approved :: MIT License'\n ],\n python_requires='>=3.9',\n)\n","repo_name":"epswartz/file_cache","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"90"}
+{"seq_id":"2808198426","text":"import requests\n# response = requests.get('https://icanhazdadjoke.com/', headers={'Accept': 'application/json'})\n# print(response.text) #JSON format\n# joke_dict = response.json()#this is the joke dictionary, key is 'joke'\n# while True:\n# command = input('Want to hear a joke? yes or no ')\n# if command == 'yes':\n# print(joke_dict['joke'])\n# elif command == 'no':\n# break\n# else:\n# print('Command not recognized')\n\n#Version 2\n#search for dad jokes\n\nwhile True:\n command = input('Enter your dad joke search term or exit: ')\n if command == 'exit':\n break\n else:\n response2 = requests.get(f'https://icanhazdadjoke.com/search?term={command}', headers={'Accept': 'application/json'})\n #print(response2.text) #JSON format\n joke_dict2 = response2.json()\n # print(joke_dict2)\n jokes_refined = joke_dict2['results']\n # print(jokes_refined)\n for i in jokes_refined:\n print(i['joke'])\n","repo_name":"PdxCodeGuild/class_salmon","sub_path":"code/nick/python/lab14_API_dadjokes.py","file_name":"lab14_API_dadjokes.py","file_ext":"py","file_size_in_byte":989,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"90"}
+{"seq_id":"35660302265","text":"\"\"\"\nThis package looks Discourse api for new posts and replies in\nhttps://fiirumi.fyysikkokilta.fi and notifies about them in chats where\nnotifications are subscribed and also provides emoji responses for new posts.\n\"\"\"\n\nimport requests\nimport json\nimport datetime\nimport os\n\nfrom telegram.error import BadRequest\nfrom telegram import Update\nfrom ..utils import CallbackContext\n\nimport config\n\n\nFIIRUMI_DATA_FILE = \"data/fiirumi_data.json\"\n\n\ndef get_posts_after(time):\n \"\"\"\n Get posts from Fiirumi that are created after the given time in UTC.\n \"\"\"\n res = requests.get(config.FIIRUMI_BASE_URL + \"/posts.json\")\n data = json.loads(res.text)\n\n posts = [x for x in data[\"latest_posts\"] if x[\"created_at\"] > time]\n\n posts.reverse()\n\n return posts\n\n\nasync def check_messages(context: CallbackContext):\n global keyboard\n global data\n lorina_id = 7\n data = load_data()\n posts = get_posts_after(data[\"previous_messages\"])\n data[\"previous_messages\"] = datetime.datetime.utcnow().isoformat()\n try:\n for p in posts:\n for c in data[\"chats\"]:\n msg = format_message(p)\n if not (p[\"category_id\"] == lorina_id and c[\"name\"] == \"Fyysikkokilta\"):\n text = format_message(p)\n print(\"{} {}\".format(c[\"id\"], c[\"name\"]))\n try:\n msg = await context.bot.send_message(\n c[\"id\"], text, parse_mode=\"MARKDOWN\"\n )\n data[\"sent_messages\"].append(\n {\n \"username\": p[\"username\"],\n \"chat\": msg.chat.id,\n \"message\": msg.message_id,\n \"voters\": {},\n }\n )\n except BadRequest:\n print(\"Sending message {} to {} failed.\".format(text, c[\"name\"]))\n finally:\n save_data(data)\n\n\ndef format_message(post):\n global data\n\n user = [x for x in data[\"users\"] if x[\"username\"] == post[\"username\"]]\n\n if len(user) == 0:\n data[\"users\"] += [{\"username\": post[\"username\"], \"emojis\": {}}]\n\n post_type = \"vastaus\" if post[\"post_number\"] > 1 else \"postaus\"\n\n text = \"Uusi {} Φrumilla!\\n\\n *{}*\\n _{}_ ({}) \\n\\n[Lue koko postaus]({})\"\n text = text.format(\n post_type,\n post[\"topic_title\"],\n post[\"name\"],\n post[\"username\"].replace(\"_\", \"\\\\_\"),\n config.FIIRUMI_BASE_URL + \"/t/\" + post[\"topic_slug\"],\n )\n\n return text\n\n\n\nasync def subscribe(update: Update, context: CallbackContext):\n assert update.effective_chat is not None, \"Update unexpectedly initiated outside of chat\"\n global data\n data = load_data()\n chats = [x[\"id\"] for x in data[\"chats\"]]\n if update.effective_chat.id not in chats:\n data[\"chats\"].append({\"name\": update.effective_chat.title, \"id\": update.effective_chat.id})\n save_data(data)\n await context.bot.send_message(update.effective_chat.id, \"Fiirumipäivitykset tilattu onnistuneesti\")\n else:\n await context.bot.send_message(update.effective_chat.id, \"Fiirumipäivitykset on jo tilattu\")\n\n\ndef load_data():\n with open(FIIRUMI_DATA_FILE) as d:\n data = json.loads(d.read())\n\n return data\n\n\ndef save_data(data):\n with open(FIIRUMI_DATA_FILE, \"w\") as d:\n json.dump(data, d, indent=2)\n\n\ndef create_data():\n data = {}\n\n data[\"users\"] = []\n data[\"chats\"] = []\n data[\"sent_messages\"] = []\n data[\"previous_messages\"] = datetime.datetime.utcnow().isoformat()\n\n save_data(data)\n\n\n\nif not os.path.isfile(FIIRUMI_DATA_FILE):\n create_data()\n\nglobal data\ndata = load_data()\n","repo_name":"fyysikkokilta/fk-kiltisbot","sub_path":"kiltisbot/fiirumi/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3771,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"}
+{"seq_id":"73765752302","text":"import numpy\n\n\ndef cofi_cost_func(params, Y, R, num_users, num_movies, num_features, reg_lambda=0.0):\n \"\"\"\n Collaborative filtering cost function.\n\n Parameters\n ----------\n params : array_like\n The parameters which will be optimized. This is a one\n dimensional vector of shape (num_movies x num_users, 1). It is the\n concatenation of the feature vectors X and parameters Theta.\n\n Y : array_like\n A matrix of shape (num_movies x num_users) of user ratings of movies.\n\n R : array_like\n A (num_movies x num_users) matrix, where R[i, j] = 1 if the\n i-th movie was rated by the j-th user.\n\n num_users : int\n Total number of users.\n\n num_movies : int\n Total number of movies.\n\n num_features : int\n Number of features to learn.\n\n reg_lambda : float, optional\n The regularization coefficient.\n\n Returns\n -------\n cost_J : float\n The value of the cost function at the given params.\n\n grad : array_like\n The gradient vector of the cost function at the given params.\n grad has a shape (num_movies x num_users, 1)\n\n Instructions\n ------------\n Compute the cost function and gradient for collaborative filtering.\n Concretely, you should first implement the cost function (without\n regularization) and make sure it is matches our costs. After that,\n you should implement thegradient and use the checkCostFunction routine\n to check that the gradient is correct. Finally, you should implement\n regularization.\n\n Notes\n -----\n - The input params will be unraveled into the two matrices:\n x_array : (num_movies x num_features) matrix of movie features\n Theta : (num_users x num_features) matrix of user features\n\n - You should set the following variables correctly:\n\n x_grad : (num_movies x num_features) matrix, containing the\n partial derivatives w.r.t. to each element of x_array\n Theta_grad : (num_users x num_features) matrix, containing the\n partial derivatives w.r.t. to each element of Theta\n\n - The returned gradient will be the concatenation of the raveled\n gradients x_grad and Theta_grad.\n \"\"\"\n # Unfold the U and W matrices from params\n cost_J = 0\n prod_mov_feat = num_movies*num_features\n x_array = params[:prod_mov_feat].reshape(num_movies, num_features)\n x_grad = numpy.zeros(x_array.shape)\n Theta = params[prod_mov_feat:].reshape(num_users, num_features)\n Theta_grad = numpy.zeros(Theta.shape)\n\n hypothesis = numpy.dot(x_array, numpy.transpose(Theta))\n error = (1/2)*numpy.sum(numpy.square((hypothesis - Y) * R))\n reg_term = (reg_lambda/2)*(numpy.sum(numpy.square(x_array)) + numpy.sum(numpy.square(Theta)))\n cost_J = error + reg_term\n\n for i in range(R.shape[0]):\n idx = numpy.where(R[i, :] == 1)[0]\n Theta_temp = Theta[idx, :]\n Y_temp = Y[i, idx]\n term1 = numpy.dot(numpy.dot(x_array[i, :], Theta_temp.T) - Y_temp, Theta_temp)\n term2 = reg_lambda*x_array[i, :]\n x_grad[i, :] = term1 + term2\n\n for j in range(R.shape[1]):\n idx = numpy.where(R[:, j] == 1)[0]\n X_temp = x_array[idx, :]\n Y_temp = Y[idx, j]\n term1 = numpy.dot(numpy.dot(X_temp, Theta[j, :]) - Y_temp, X_temp)\n term2 = reg_lambda * Theta[j, :]\n Theta_grad[j, :] = term1 + term2\n\n grad = numpy.concatenate([x_grad.ravel(), Theta_grad.ravel()])\n\n return cost_J, grad\n","repo_name":"ashu-vyas-github/AndrewNg_MachineLearning_Coursera","sub_path":"python/ex8_anomaly_recommender/ano_rec_funcs/cofi_cost_func.py","file_name":"cofi_cost_func.py","file_ext":"py","file_size_in_byte":3505,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"12487776084","text":"from typing import Any, Dict, List, Type, TypeVar, Union\n\nimport attr\n\nfrom ..models.container_uuid import ContainerUUID\nfrom ..types import UNSET, Unset\n\nT = TypeVar(\"T\", bound=\"TemplateLayoutColumnView\")\n\n\n@attr.s(auto_attribs=True)\nclass TemplateLayoutColumnView:\n \"\"\"\n Attributes:\n containers (Union[Unset, List[ContainerUUID]]):\n width (Union[Unset, int]):\n left_offset (Union[Unset, int]):\n style_class (Union[Unset, str]):\n \"\"\"\n\n containers: Union[Unset, List[ContainerUUID]] = UNSET\n width: Union[Unset, int] = UNSET\n left_offset: Union[Unset, int] = UNSET\n style_class: Union[Unset, str] = UNSET\n additional_properties: Dict[str, Any] = attr.ib(init=False, factory=dict)\n\n def to_dict(self) -> Dict[str, Any]:\n containers: Union[Unset, List[Dict[str, Any]]] = UNSET\n if not isinstance(self.containers, Unset):\n containers = []\n for containers_item_data in self.containers:\n containers_item = containers_item_data.to_dict()\n\n containers.append(containers_item)\n\n width = self.width\n left_offset = self.left_offset\n style_class = self.style_class\n\n field_dict: Dict[str, Any] = {}\n field_dict.update(self.additional_properties)\n field_dict.update({})\n if containers is not UNSET:\n field_dict[\"containers\"] = containers\n if width is not UNSET:\n field_dict[\"width\"] = width\n if left_offset is not UNSET:\n field_dict[\"leftOffset\"] = left_offset\n if style_class is not UNSET:\n field_dict[\"styleClass\"] = style_class\n\n return field_dict\n\n @classmethod\n def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\n d = src_dict.copy()\n containers = []\n _containers = d.pop(\"containers\", UNSET)\n for containers_item_data in _containers or []:\n containers_item = ContainerUUID.from_dict(containers_item_data)\n\n containers.append(containers_item)\n\n width = d.pop(\"width\", UNSET)\n\n left_offset = d.pop(\"leftOffset\", UNSET)\n\n style_class = d.pop(\"styleClass\", UNSET)\n\n template_layout_column_view = cls(\n containers=containers,\n width=width,\n left_offset=left_offset,\n style_class=style_class,\n )\n\n template_layout_column_view.additional_properties = d\n return template_layout_column_view\n\n @property\n def additional_keys(self) -> List[str]:\n return list(self.additional_properties.keys())\n\n def __getitem__(self, key: str) -> Any:\n return self.additional_properties[key]\n\n def __setitem__(self, key: str, value: Any) -> None:\n self.additional_properties[key] = value\n\n def __delitem__(self, key: str) -> None:\n del self.additional_properties[key]\n\n def __contains__(self, key: str) -> bool:\n return key in self.additional_properties\n","repo_name":"yolabingo/dot-cms-rest-api-client","sub_path":"dot_cms_rest_api_client/models/template_layout_column_view.py","file_name":"template_layout_column_view.py","file_ext":"py","file_size_in_byte":2974,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"25317192919","text":"class Node:\n # Function to initialise the node object\n def __init__(self, data):\n self.data = data # Assign data\n self.next = None # Initialize next as null\n\n\n# Linked List class contains a Node object\nclass LinkedList:\n\n # Function to initialize head\n def __init__(self):\n self.head = None\n\n def push(self, new_data):\n # 1 & 2: Allocate the Node &\n # Put in the data\n new_node = Node(new_data)\n\n # 3. Make next of new Node as head\n new_node.next = self.head\n\n # 4. Move the head to point to new Node\n self.head = new_node\n\n\n\n def findMiddle(self):\n\n lst = []\n temp = self.head\n while temp:\n lst.append(temp.data)\n temp = temp.next\n return lst[len(lst)//2]\n\n\n\n\nif __name__=='__main__':\n llist = LinkedList()\n llist.push(1)\n llist.push(3)\n llist.push(1)\n llist.push(2)\n llist.push(1)\n\n result = llist.findMiddle()\n print(result)\n\n","repo_name":"triparnabh/Leetcode_Problems","sub_path":"Finding middle element in a linked list.py","file_name":"Finding middle element in a linked list.py","file_ext":"py","file_size_in_byte":997,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"43881800209","text":"# Number which is divisible by 7 but not a multiple of 5\nbegin = int(input(\"Enter starting range:\"))\nend = int(input(\"Enter stoping range :\"))\n\nlist = []\nfor value in range(begin, end):\n if value % 7 == 0:\n if value % 5== 0:\n continue\n else:\n list.append(str(value))\n\nprint(','.join(list))","repo_name":"mohit266/Python","sub_path":"Task_6.py","file_name":"Task_6.py","file_ext":"py","file_size_in_byte":328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"8109257545","text":"import argparse #for parsing arguments\nimport Function #util class for processing data\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description='The implementation process some functions.')\n parser.add_argument(\"-m\", \"--method\", help = 'The method to be used by some functions')\n parser.add_argument(\"-o\", \"--outfile\", help = 'The output of file name')\n parser.add_argument(\"-c\", \"--columns\", help = 'The list of input columns', nargs = '+')\n parser.add_argument(\"-t\", \"--threshold\", default = '50', help = 'Percent, 50 by default')\n parser.add_argument(\"-f\", \"--formula\", help = 'Formula to generate new column using existing columns')\n # Get arguments \n args, unknown = parser.parse_known_args()\n\n # The first two arguments must represent function name and input file name\n function = unknown[0]\n inputfile = unknown[1]\n\n # Load file into dataframe using constructor\n df = Function.Function(inputfile)\n # Perform a specific function using the parsed arguments\n # e.g. df.list_missing(args)\n eval('df.'+function+'(args)')\n\n\n\n\n","repo_name":"baobui16/Data_Mining","sub_path":"Lab01-Preprocessing-data/Source/Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":1079,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"1644028318","text":"import logging\nimport pkgutil\nfrom typing import Dict, List, Optional, Set, Tuple # noqa\n\nlogger = logging.getLogger(__name__)\n\nconsole_format = \"File \\\"%(pathname)s:%(lineno)d\\\" : \\n %(asctime)s [%(levelname)8s] %(message)s\"\n\nstandard_formatter = logging.Formatter(console_format)\n\n\ndef get_loggers() -> List[logging.Logger]:\n loggers = []\n # Attempt to avoid a circular dependency\n import astutus\n pkg = astutus\n for _, module_name, is_pkg in pkgutil.walk_packages(pkg.__path__, pkg.__name__ + '.'):\n logger.debug(f\"module_name: {module_name}\")\n logger.debug(f\"is_pkg: {is_pkg}\")\n if not is_pkg:\n logger_for_module = logging.getLogger(module_name)\n loggers.append(logger_for_module)\n return loggers\n\n\ndef set_level(logger_name: str, level: int) -> None:\n for logger_for_module in get_loggers():\n if logger_for_module.name == logger_name:\n logger.debug(f\"logger_for_module: {logger_for_module.name} was found. \")\n logger_for_module.setLevel(int(level))\n return\n assert False, (logger_name, level)\n","repo_name":"rich-dobbs-13440/astutus","sub_path":"src/astutus/log/log_impl.py","file_name":"log_impl.py","file_ext":"py","file_size_in_byte":1109,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"36506659576","text":"#!/usr/bin/python\n#-*- coding: utf-8 -*-\n\n\n'''\n\n'''\n__author__ = 'wpxiao'\nfrom web.forms.base import BaseForm\nfrom django import forms\nfrom django.core.exceptions import ValidationError\n\nclass LoginForm(BaseForm,forms.Form):\n username = forms.CharField(min_length=6,\n max_length=20,\n error_messages={\"required\":\"用户名不能为空\",\n \"min_length\":\"用户名长度不能小于6个字符\",\n \"max_length\":\"用户名长度不能超过20个字符\"}\n )\n\n password = forms.RegexField(\n '^(?=.*[0-9])(?=.*[a-zA-Z])(?=.*[!@#$\\%\\^\\&\\*\\(\\)])[0-9a-zA-Z!@#$\\%\\^\\&\\*\\(\\)]{8,32}$',\n min_length=12,\n max_length=32,\n error_messages={\"required\":\"密码不能为空\",\n \"invalid\":\"密码必须包含数字、字母、特殊字符\",\n \"min_length\":\"密码长度不能小于8个字符\",\n \"max_length\":\"密码长度不能大于32个字符\"\n }\n )\n\n # False表示可以为空，True表示必须输入\n rmb = forms.IntegerField(required=False)\n\n check_code = forms.CharField(\n error_messages={\"required\":\"验证码不能为空\"}\n )\n\n\n # 当调用is_valid方法时会自动调用这个方法\n def clean_check_code(self):\n check_code = self.cleaned_data[\"check_code\"]\n if self.request.session.get(\"CheckCode\").upper() != self.request.POST.get(\"check_code\").upper():\n raise ValidationError(message=\"验证码错误\",code=\"invalid\")\n else:\n return check_code\n","repo_name":"fengxueff/WpXiaoBlog","sub_path":"web/forms/account.py","file_name":"account.py","file_ext":"py","file_size_in_byte":1683,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"26534770479","text":"\"\"\"\nScript to train and save models in this analysis\n\"\"\"\n# imports\nimport pandas as pd\nfrom sklearn.linear_model import Lasso\nimport pickle # to persist model\nimport argparse\n\n# arg parser\nparser = argparse.ArgumentParser()\nparser.add_argument(\"input_file_name\", type=str)\nargs = parser.parse_args()\n\ninput_file_name = args.input_file_name\n\n# import data\ndf = pd.read_csv(\n\t\"data/processed/{}.csv\".format(input_file_name),\n\tindex_col=0)\nprint(df.head())\n\n# create regression model\nreg = Lasso(alpha=1.0, max_iter=10000)\ny = df.iloc[:, 0].values\nX = df.iloc[:, 1:].values\nprint(len(X))\nreg.fit(X,y)\n\n# persist the model as a pickle binary object\nwith open('models/lasso_{}.pkl'.format(input_file_name),'wb') as f:\n pickle.dump(reg,f)\n","repo_name":"nschantz21/studious-succotash","sub_path":"src/models/train_model.py","file_name":"train_model.py","file_ext":"py","file_size_in_byte":737,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"5345893258","text":"# 게임 종료 처리\r\n# 성공 : 화면 내에 모든 버블이 사라지면 성공\r\n# 실패 : 바닥에 어떤 정해진 높이보다 버블이 낮게 내려오면 실패\r\nimport time\r\nimport sys\r\nimport os\r\nimport random\r\nimport math\r\nfrom turtle import width\r\nimport pygame\r\n\r\n# button class\r\n\r\n\r\nclass Button:\r\n def __init__(self, img_in, x, y, width, height, img_act, x_act, y_act, action=None):\r\n mouse = pygame.mouse.get_pos()\r\n click = pygame.mouse.get_pressed()\r\n if x + width > mouse[0] > x and y + height > mouse[1] > y:\r\n screen.blit(img_act, (x_act, y_act))\r\n if click[0] and action != None:\r\n time.sleep(1)\r\n action()\r\n else:\r\n screen.blit(img_in, (x, y))\r\n\r\n\r\ndef quitgame():\r\n pygame.quit()\r\n sys.exit()\r\n\r\n\r\ndef change_state(k):\r\n k = 1\r\n\r\n# 버블 클래스 생성\r\n\r\n\r\nclass Bubble(pygame.sprite.Sprite):\r\n def __init__(self, image, color, position=(0, 0), row_idx=-1, col_idx=-1):\r\n super().__init__()\r\n self.image = image\r\n self.color = color\r\n self.rect = image.get_rect(center=position)\r\n self.radius = 18\r\n self.row_idx = row_idx\r\n self.col_idx = col_idx\r\n\r\n def set_rect(self, position):\r\n self.rect = self.image.get_rect(center=position)\r\n\r\n def draw(self, screen, to_x=None):\r\n if to_x:\r\n screen.blit(self.image, (self.rect.x + to_x, self.rect.y))\r\n else:\r\n screen.blit(self.image, self.rect)\r\n\r\n def set_angle(self, angle):\r\n self.angle = angle\r\n self.rad_angle = math.radians(self.angle)\r\n\r\n def move(self):\r\n to_x = self.radius * math.cos(self.rad_angle)\r\n to_y = self.radius * math.sin(self.rad_angle) * -1\r\n\r\n self.rect.x += to_x\r\n self.rect.y += to_y\r\n\r\n if self.rect.left < 0 or self.rect.right > screen_width:\r\n self.set_angle(180 - self.angle)\r\n\r\n def set_map_index(self, row_idx, col_idx):\r\n self.row_idx = row_idx\r\n self.col_idx = col_idx\r\n\r\n def drop_downward(self, height):\r\n self.rect = self.image.get_rect(\r\n center=(self.rect.centerx, self.rect.centery + height))\r\n\r\n\r\n# 버블 클래스 생성\r\n\r\n\r\nclass Bubble(pygame.sprite.Sprite):\r\n def __init__(self, image, color, position=(0, 0), row_idx=-1, col_idx=-1): # 매소드정의\r\n super().__init__() # 상속받은 부모 클래스의 init 매소드 호출\r\n self.image = image\r\n self.color = color\r\n # sprite를 상속받은 클래스는 image와 rect 무조건 필요함\r\n self.rect = image.get_rect(center=position)\r\n self.radius = 18 # 버블의 발사 속도 # 게임 진행 속도\r\n self.row_idx = row_idx # 초기화해주기\r\n self.col_idx = col_idx # 현재 버블이 맵 기준 어디에 있는지 알 수 있음\r\n\r\n def set_rect(self, position):\r\n self.rect = self.image.get_rect(center=position)\r\n\r\n # 화면에 나타내기\r\n def draw(self, screen, to_x=None):\r\n if to_x: # 화면 흔들기\r\n screen.blit(self.image, (self.rect.x + to_x, self.rect.y))\r\n else:\r\n screen.blit(self.image, self.rect)\r\n\r\n # 각도 클래스 정의\r\n def set_angle(self, angle):\r\n self.angle = angle\r\n self.rad_angle = math.radians(self.angle) # 60분법을 호도법으로 변환해줌\r\n\r\n # 버블의 움직임 설정(라디안이용)\r\n def move(self):\r\n to_x = self.radius * math.cos(self.rad_angle) # x좌표로 이동하는 값(cos)\r\n # y좌표로 이동하는 값(sin) # 파이게임에서는 y가 밑으로 내려갈수록 +값\r\n to_y = self.radius * math.sin(self.rad_angle) * -1\r\n\r\n self.rect.x += to_x # x로 to_x만큼 이동\r\n self.rect.y += to_y # y로 to_y만큼 이동\r\n\r\n # 벽에 충돌시 버블 진행방향 설정\r\n if self.rect.left < 0 or self.rect.right > screen_width: # 화면 왼쪽 경계를 벗어나거나 오른쪽 경계를 벗어나면\r\n self.set_angle(180 - self.angle) # 180도에서 현재 방향을 뺀 각도로 튕겨나감\r\n\r\n def set_map_index(self, row_idx, col_idx):\r\n self.row_idx = row_idx\r\n self.col_idx = col_idx\r\n\r\n # 벽 내리는 함수 정의\r\n def drop_downward(self, height):\r\n self.rect = self.image.get_rect(\r\n center=(self.rect.centerx, self.rect.centery + height))\r\n\r\n# 발사대 클래스 생성\r\n\r\n\r\nclass Pointer(pygame.sprite.Sprite):\r\n def __init__(self, image, position, angle):\r\n super().__init__()\r\n self.image = image # 각도가 바뀔 때 마다 그 각도로 이미지가 업데이트됨\r\n self.rect = image.get_rect(center=position)\r\n self.angle = angle # 회전\r\n self.original_image = image # 항상 0도 # 각도를 계산 할 때 원본 이미지에서 계산되도록\r\n self.position = position\r\n\r\n def draw(self, screen):\r\n screen.blit(self.image, self.rect)\r\n\r\n # 회전\r\n def rotate(self, angle):\r\n self.angle += angle\r\n\r\n if self.angle > 170: # 밑으로 회전하지 않게 범위 제한\r\n self.angle = 170\r\n elif self.angle < 10:\r\n self.angle = 10\r\n\r\n self.image = pygame.transform.rotozoom(\r\n self.original_image, self.angle, 1) # 마지막 객체는 이미지 크기 배수를 의미\r\n # 이미지의 회전과 상관없이 발사대의 rect정보는 처음 지정한 중심좌표기준으로 그려짐\r\n self.rect = self.image.get_rect(center=self.position)\r\n\r\n# 맵 만들기\r\n\r\n\r\ndef setup():\r\n global map # 함수안에서 전역변수 만들때 global 사용\r\n # lv1\r\n map = [\r\n # [\"R\", \"R\", \"Y\", \"Y\", \"B\", \"B\", \"G\", \"G\"] = list(\"RRYYBBGG\")\r\n list(\"RRYYBBGG\"),\r\n list(\"RRYYBBG/\"), # / : 버블이 위치할 수 없는 곳\r\n list(\"BBGGRRYY\"),\r\n list(\"BGGRRYY/\"),\r\n list(\"........\"), # . : 비어 있는 곳\r\n list(\"......./\"),\r\n list(\"........\"),\r\n list(\"......./\"),\r\n list(\"........\"),\r\n list(\"......./\"),\r\n list(\"........\") # 11번째 줄\r\n ]\r\n\r\n # lv2\r\n # map = [\r\n # list(\"...YY...\"),\r\n # list(\"...G.../\"),\r\n # list(\"...R....\"),\r\n # list(\"...B.../\"),\r\n # list(\"...R....\"),\r\n # list(\"...G.../\"),\r\n # list(\"...P....\"),\r\n # list(\"...P.../\"),\r\n # list(\"........\"),\r\n # list(\"......./\"),\r\n # list(\"........\")\r\n # ]\r\n\r\n # lv3\r\n # map = [\r\n # list(\"G......G\"),\r\n # list(\"RGBYRGB/\"),\r\n # list(\"Y......Y\"),\r\n # list(\"BYRGBYR/\"),\r\n # list(\"...R....\"),\r\n # list(\"...G.../\"),\r\n # list(\"...R....\"),\r\n # list(\"......./\"),\r\n # list(\"........\"),\r\n # list(\"......./\"),\r\n # list(\"........\")\r\n # ]\r\n\r\n # 버블 맵에 그리��\r\n\r\n # 맵 전달받기 # row_idx = 몇 번째 인덱스인지 , row = 한 줄씩 list 불러오기\r\n for row_idx, row in enumerate(map):\r\n # 한칸씩 받기 # col_idx = 한 줄 안에서 몇 번째인지, col = 한줄 불러온 걸 한 칸씩 끊어서 받음\r\n for col_idx, col in enumerate(row):\r\n if col in [\".\", \"/\"]: # 비어있는 공간은\r\n continue # 버블을 만들지 않음\r\n position = get_bubble_position(row_idx, col_idx) # 튜플 형태로 받아짐\r\n image = get_bubble_image(col)\r\n # 버블 객체(image, col....)를 통해 만들고 클래스 객체를 버블그룹에 추가\r\n bubble_group.add(Bubble(image, col, position, row_idx, col_idx))\r\n\r\n\r\ndef get_bubble_position(row_idx, col_idx): # 맵의 좌표\r\n # 센터에 위치하기 위해 (BUBBLE_WIDTH // 2) 더해줌\r\n pos_x = col_idx * CELL_SIZE + (BUBBLE_WIDTH // 2)\r\n # + wall_height 하는 이유는 벽이 내려온 길이 고려해서\r\n pos_y = row_idx * CELL_SIZE + (BUBBLE_HEIGHT // 2) + wall_height\r\n if row_idx % 2 == 1: # 홀수 번째 줄이라면\r\n pos_x += CELL_SIZE // 2 # x죄표 오른쪽으로 CELL_SIZE // 2 만큼 이동\r\n return pos_x, pos_y\r\n\r\n\r\ndef get_bubble_image(color):\r\n if color == \"R\":\r\n return bubble_images[0]\r\n elif color == \"Y\":\r\n return bubble_images[1]\r\n elif color == \"B\":\r\n return bubble_images[2]\r\n elif color == \"G\":\r\n return bubble_images[3]\r\n # elif color == \"P\":\r\n return bubble_images[4]\r\n else: # BLACK\r\n return bubble_images[-1] # 리스트의 맨마지막 값 반환을 의미\r\n\r\n\r\ndef prepare_bubbles():\r\n global curr_bubble, next_bubble # 전역변수\r\n if next_bubble:\r\n curr_bubble = next_bubble\r\n else:\r\n curr_bubble = create_bubble() # 랜덤으로 새 버블 만들기\r\n\r\n curr_bubble.set_rect((screen_width // 2, 624)) # 이번에 쏠 버블 위치좌표\r\n next_bubble = create_bubble()\r\n next_bubble.set_rect((screen_width // 4, 668)) # 다음에 쏠 버블 위치좌표\r\n\r\n\r\ndef create_bubble():\r\n color = get_random_bubble_color()\r\n image = get_bubble_image(color)\r\n return Bubble(image, color)\r\n\r\n\r\ndef get_random_bubble_color():\r\n colors = [] # 색깔 후보를 리스트로 만듦\r\n for row in map: # 맵에 있는 row를 가져옴\r\n for col in row: # 그 row에 있는 색깔들을 가져옴\r\n # 리스트에 없는 색깔 or 버블이 존재하지 않으면\r\n if col not in colors and col not in [\".\", \"/\"]:\r\n colors.append(col) # 리스트의 색이 추가됨\r\n return random.choice(colors) # 리스트에 있는 것중 하나 선택\r\n\r\n\r\ndef process_collision():\r\n global curr_bubble, fire, curr_fire_count # 전역함수\r\n hit_bubble = pygame.sprite.spritecollideany(\r\n curr_bubble, bubble_group, pygame.sprite.collide_mask) # 충돌한 버블 = hit_bubble\r\n # 투명한 영역 제외하고 진짜 이미지가 있는 영역에 하나라도 충돌되면 hit_bubble이 받음\r\n if hit_bubble or curr_bubble.rect.top <= wall_height: # 버블이 충돌 했거나 천장or 벽에 닿았을 때 화면을 벗어나지 않게 설정\r\n # 튜플형태 # curr_bubble의 중심의 (x, y) # x,y를 별도의 변수로 활용하고 싶으면 * 붙이기_언패킹\r\n row_idx, col_idx = get_map_index(*curr_bubble.rect.center)\r\n place_bubble(curr_bubble, row_idx, col_idx) # curr_bubble,버블 위치 시키기\r\n remove_adjacent_bubbles(\r\n row_idx, col_idx, curr_bubble.color) # 인접한 버블 제거\r\n curr_bubble = None\r\n fire = False # 발사여부\r\n curr_fire_count -= 1 # 충돌 후에 발사 기회 한번씩 뺴기\r\n\r\n\r\ndef get_map_index(x, y):\r\n # - wall_height 하는 이유는 벽이 내려온 길이 고려해서\r\n row_idx = (y - wall_height) // CELL_SIZE\r\n col_idx = x // CELL_SIZE\r\n if row_idx % 2 == 1: # 홀수번째 줄이면\r\n # 현재 버블을 왼쪽으로 절반 이동 후에 계산됨\r\n col_idx = (x - (CELL_SIZE // 2)) // CELL_SIZE\r\n if col_idx < 0:\r\n col_idx = 0\r\n elif col_idx > MAP_COLUMN_COUNT - 2:\r\n col_idx = MAP_COLUMN_COUNT - 2\r\n return row_idx, col_idx\r\n\r\n\r\ndef place_bubble(bubble, row_idx, col_idx):\r\n map[row_idx][col_idx] = bubble.color # 충돌하는 현재 버블의 색깔 받기\r\n position = get_bubble_position(row_idx, col_idx)\r\n bubble.set_rect(position) # 버블위치 update\r\n bubble.set_map_index(row_idx, col_idx)\r\n bubble_group.add(bubble) # 버블 그룸에 추가_충돌처리비교를 위해\r\n\r\n\r\ndef remove_adjacent_bubbles(row_idx, col_idx, color):\r\n visited.clear() # 방문기록 초기화\r\n visit(row_idx, col_idx, color)\r\n if len(visited) >= 3: # 같은색깔 버블이 3개 이상이면\r\n remove_visited_bubbles() # 방문한 버블 삭제\r\n remove_hanging_bubbles() # 붕 떠있는 버블 삭제\r\n\r\n\r\ndef visit(row_idx, col_idx, color=None):\r\n # 맵의 범위를 벗어나는지 확인\r\n if row_idx < 0 or row_idx >= MAP_ROW_COUNT or col_idx < 0 or col_idx >= MAP_COLUMN_COUNT:\r\n return\r\n # 현재 Cell 의 색상이 color 와 같은지 확인\r\n if color and map[row_idx][col_idx] != color:\r\n return\r\n # 빈 공간이거나, 버블이 존재할 수 없는 위치인지 확인\r\n if map[row_idx][col_idx] in [\".\", \"/\"]:\r\n return\r\n # 이미 방문했는지 여부 확인\r\n if (row_idx, col_idx) in visited:\r\n return\r\n # 방문 처리\r\n visited.append((row_idx, col_idx)) # 튜플형태\r\n\r\n # 버블이 방문할 수 있는 위치의 경우의 수\r\n rows = [0, -1, -1, 0, 1, 1]\r\n cols = [-1, -1, 0, 1, 0, -1]\r\n if row_idx % 2 == 1: # 홀수번째 줄일 때\r\n rows = [0, -1, -1, 0, 1, 1]\r\n cols = [-1, 0, 1, 1, 1, 0]\r\n\r\n for i in range(len(rows)): # 재귀함수 # 버블 움직임 가능한 경우 다 방문하는 함수\r\n visit(row_idx + rows[i], col_idx + cols[i], color)\r\n\r\n\r\ndef remove_visited_bubbles():\r\n bubbles_to_remove = [b for b in bubble_group if (\r\n b.row_idx, b.col_idx) in visited] # 버블 그룸에서 가져와 방문한 리스트에 있는 지 받아옴\r\n for bubble in bubbles_to_remove: # 방문한 적 있으면 삭제\r\n map[bubble.row_idx][bubble.col_idx] = \".\" # 삭제\r\n bubble_group.remove(bubble) # 현재 버블 초기화\r\n\r\n\r\ndef remove_not_visited_bubbles(): # 붕 떠있는 버블 삭제\r\n bubbles_to_remove = [b for b in bubble_group if (\r\n b.row_idx, b.col_idx) not in visited]\r\n for bubble in bubbles_to_remove:\r\n map[bubble.row_idx][bubble.col_idx] = \".\"\r\n bubble_group.remove(bubble)\r\n\r\n\r\ndef remove_hanging_bubbles():\r\n visited.clear() # 초기화 먼저 해줌\r\n for col_idx in range(MAP_COLUMN_COUNT):\r\n if map[0][col_idx] != \".\": # 비어있지 않으면\r\n visit(0, col_idx) # 방문, 색깔은 무시\r\n remove_not_visited_bubbles()\r\n\r\n\r\ndef draw_bubbles(): # 발사 횟수가 줄어들을수록 화면 흔들리기\r\n to_x = None\r\n if curr_fire_count == 2: # 기회 2번 남으면\r\n to_x = random.randint(0, 2) - 1 # -1 ~ 1 # 약간 흔들기\r\n elif curr_fire_count == 1:\r\n to_x = random.randint(0, 8) - 4 # -4 ~ 4 # 많이 흔들기\r\n\r\n for bubble in bubble_group:\r\n bubble.draw(screen, to_x)\r\n\r\n\r\ndef drop_wall(): # 벽이미지 좌표 변경\r\n global wall_height, curr_fire_count\r\n wall_height += CELL_SIZE # 셀 사이즈 만큼 떨어짐\r\n for bubble in bubble_group:\r\n bubble.drop_downward(CELL_SIZE)\r\n curr_fire_count = FIRE_COUNT # 7번\r\n\r\n\r\ndef get_lowest_bubble_bottom():\r\n bubble_bottoms = [\r\n bubble.rect.bottom for bubble in bubble_group] # 가장 밑에 있는 버블\r\n return max(bubble_bottoms) # 리스트 안의 가장 큰 값\r\n\r\n\r\ndef change_bubble_image(image): # 게임 종료 시 버블색 바꾸기\r\n for bubble in bubble_group:\r\n bubble.image = image\r\n\r\n\r\ndef display_game_over(): # 게임 종료 시 문구 설정\r\n # 화면 중앙에 위치\r\n if game_result == 0:\r\n screen.blit(ending_Img, (0, 0))\r\n if game_result == 1:\r\n screen.blit(sucess_Img, (0, 0))\r\n\r\n\r\npygame.init()\r\nscreen_width = 448 # 길이\r\nscreen_height = 720 # 높이\r\nscreen = pygame.display.set_mode(\r\n (screen_width, screen_height)) # 튜플형태로 화면 설정, screen으로 받음\r\npygame.display.set_caption(\"Bubble Game\") # 게임제목\r\nclock = pygame.time.Clock() # 변수 설정\r\n\r\n# 배경 이미지 불러오기\r\ncurrent_path = os.path.dirname(__file__) # 현재파일의경로(디렉토리)파악 #경로변수설정\r\n# join을 써서 디렉토리+파일명경로를 합쳐서 이미지를 불러옴\r\nbackground = pygame.image.load(os.path.join(current_path, \"background1.png\"))\r\n# 벽 이미지 불러오기\r\nwall = pygame.image.load(os.path.join(current_path, \"wall.png\"))\r\n\r\n# 타이틀 이미지 불러오기\r\ntitle = pygame.image.load(os.path.join(current_path, \"title.png\"))\r\n\r\n# 종료 이미지 불러오기\r\nending_Img = pygame.image.load(os.path.join(current_path, \"ending_img.png\"))\r\nsucess_Img = pygame.image.load(os.path.join(current_path, \"success_img.png\"))\r\n\r\n# 시작화면 버튼\r\nstartImg = pygame.image.load(os.path.join(current_path, \"starticon.png\"))\r\nquitImg = pygame.image.load(os.path.join(current_path, \"quiticon.png\"))\r\nclickStartImg = pygame.image.load(\r\n os.path.join(current_path, \"clickedStartIcon.png\"))\r\nclickQuitImg = pygame.image.load(\r\n os.path.join(current_path, \"clickedQuitIcon.png\"))\r\n\r\n\r\n# 버블 이미지 불러오기\r\nbubble_images = [ # 버블이 색깔이 다양하므로 리스트로 만듦\r\n # 투명도설정_버블간의 충돌시 이미지가 있는 동그란 부분을 기준으로 처리할 수 있음\r\n pygame.image.load(os.path.join(current_path, \"red.png\")).convert_alpha(),\r\n pygame.image.load(os.path.join(\r\n current_path, \"yellow.png\")).convert_alpha(),\r\n pygame.image.load(os.path.join(current_path, \"bule.png\")).convert_alpha(),\r\n pygame.image.load(os.path.join(current_path, \"green.png\")).convert_alpha(),\r\n #pygame.image.load(os.path.join(current_path, \"purple.png\")).convert_alpha(),\r\n pygame.image.load(os.path.join(current_path, \"black.png\")\r\n ).convert_alpha() # 게임종료시의 버블색깔\r\n]\r\n\r\n\r\n# 점수판 표시\r\n# system_font = pygame.font.SysFont('verdanai', 30) # 텍스트 객체 설정\r\n#game_live = system_font.render(\"score : \" + str(score_live), True, PINK, GRAY)\r\n#game_live_rect = game_live.get_rect()\r\n#game_live_rect.topleft = (10, 10)\r\n#game_live = system_font.render(\"score : \" + str(score_live), True, PINK, GRAY)\r\n\r\n# 대기중인 버블 컵 이미지 불러오기\r\n#nextcup_image = pygame.image.load(os.path.join(current_path, \"nextcup.jpeg\")).convert_alpha()\r\n#nextcup_image = pygame.image.load(\"C:\\project\\\\nextcup.jpeg\").convert_alpha()\r\n#nextcup = nextcup_image\r\n# nextcup = pygame.transform.scale(nextcup, (40, 40)) # 사이즈 변환\r\n# screen.blit(nextcup, (100, 680)) # 화면에 나타내기\r\n\r\n# 발사대 이미지 불러오기\r\npointer_image = pygame.image.load(os.path.join(\r\n current_path, \"conpointer.png\")).convert_alpha()\r\n# 발사대 클래스 객체 #발사대의 angle 90도로 위쪽을 보게 설정\r\npointer = Pointer(pointer_image, (screen_width // 2, 624), 90)\r\n\r\n# 게임 관련 변수\r\nCELL_SIZE = 56\r\nBUBBLE_WIDTH = 56\r\nBUBBLE_HEIGHT = 62\r\nRED = (255, 0, 0)\r\nWHITE = (255, 255, 255)\r\n#PINK = (255, 51, 153)\r\n#GRAY = (128, 128, 128)\r\nMAP_ROW_COUNT = 11 # row 줄 기준 최대 갯수\r\nMAP_COLUMN_COUNT = 8 # row 행 기준 최대 갯수\r\nFIRE_COUNT = 7 # 쏠 수 있는 기회\r\n# score_live = 0 # 초기 점수\r\n\r\n# 발사대 관련 변수\r\n# to_angle = 0 # 좌우로 움직일 각도 정보\r\nto_angle_left = 0 # 왼쪽으로 움직일 각도 정보\r\nto_angle_right = 0 # 오른쪽으로 움직일 각도 정보\r\nangle_speed = 1.5 # 1.5 도씩 움직이게 됨\r\n\r\ncurr_bubble = None # 이번에 쏠 버블\r\nnext_bubble = None # 다음에 쏠 버블\r\nfire = False # 발사 여부\r\ncurr_fire_count = FIRE_COUNT\r\nwall_height = 0 # 화면에 보여지는 벽의 높이_처음은 안보임\r\n\r\nis_game_over = False # 게임종료변수\r\ngame_font = pygame.font.SysFont(\"arialrounded\", 40) # 게임 종료 시 문구\r\ngame_result = None # 게임 결과\r\ngame_state = False\r\nglobal j\r\nj = 2\r\nchange = 3\r\n\r\nmap = [] # 빈 리스트로 맵 선언\r\nvisited = [] # 버블이 방문한 위치 기록\r\nbubble_group = pygame.sprite.Group() # 전역함수\r\nsetup() # setup 함수 호출\r\n\r\nrunning = True # 러닝변수가 ture일 동안 반복문이 계속 돌게됨\r\nwhile running:\r\n clock.tick(60) # (게임속도)FPS 60 으로 설정\r\n screen.fill((202, 228, 241))\r\n screen.blit(title, (0, 0))\r\n\r\n startButton = Button(startImg, 80, 600, 60, 20,\r\n clickStartImg, 83, 603, None)\r\n\r\n quitButton = Button(quitImg, 280, 600, 60, 20,\r\n clickQuitImg, 283, 603, quitgame)\r\n\r\n for event in pygame.event.get(): # 발생하는 모든 이벤트를 받음\r\n if event.type == pygame.QUIT: # 종료버튼구현\r\n running = False\r\n\r\n if event.type == pygame.KEYDOWN: # 어떤 키가 눌려졌을 때\r\n if event.key == pygame.K_LEFT: # 왼쪽 화살표\r\n to_angle_left += angle_speed # 발사대가 왼쪽으로 각도 변경\r\n elif event.key == pygame.K_RIGHT: # 오른쪽 화살표\r\n to_angle_right -= angle_speed # 발사대가 오른쪽으로 각도 변경\r\n elif event.key == pygame.K_SPACE: # 스페이스키\r\n if curr_bubble and not fire: # 쏠 버블이 있고 발사상태가 아닐 때\r\n fire = True # 발사처리\r\n curr_bubble.set_angle(pointer.angle)\r\n\r\n if event.type == pygame.KEYUP: # 어떤 키가 눌리지 않으면\r\n if event.key == pygame.K_LEFT:\r\n to_angle_left = 0\r\n elif event.key == pygame.K_RIGHT:\r\n to_angle_right = 0\r\n\r\n mouse = pygame.mouse.get_pos()\r\n click = pygame.mouse.get_pressed()\r\n x = 80\r\n width = 60\r\n y = 600\r\n height = 20\r\n if x + width > mouse[0] > x and y + height > mouse[1] > y:\r\n if click[0]:\r\n time.sleep(1)\r\n change = 1\r\n\r\n if change == 1:\r\n game_state = True\r\n\r\n if game_state == True:\r\n if not curr_bubble: # 지금 쏠 버블이 없으면\r\n prepare_bubbles() # 버블을 준비해라\r\n\r\n if fire: # 발사하면\r\n process_collision() # 충돌 처리 함수\r\n\r\n if curr_fire_count == 0: # 발사 기회가 0이면\r\n drop_wall()\r\n\r\n# if process_collision():\r\n# print(\"충돌\")\r\n# score_live = score_live + 2 # 버블 한개 당 2점\r\n\r\n if not bubble_group:\r\n game_result = 1 # 게임 성공 시 문구\r\n is_game_over = True # 게임 성공 시\r\n elif get_lowest_bubble_bottom() > len(map) * CELL_SIZE: # 가장 바닥에 있는 버블이 맵의 높이보다 커지면\r\n game_result = 0 # 게임 실패 시 문구\r\n is_game_over = True # 게임 종료 처리\r\n # 게임 종료시 버블 이미지 리스트 가장 마지막 black으로 바꿈\r\n change_bubble_image(bubble_images[-1])\r\n\r\n screen.blit(background, (0, 0)) # 배경이미지 나타내기\r\n # bubble_group.draw(screen)\r\n screen.blit(wall, (0, wall_height - screen_height))\r\n\r\n draw_bubbles()\r\n pointer.rotate(to_angle_left + to_angle_right)\r\n pointer.draw(screen)\r\n if curr_bubble:\r\n if fire:\r\n curr_bubble.move() # move 메소드\r\n curr_bubble.draw(screen)\r\n\r\n if next_bubble:\r\n next_bubble.draw(screen)\r\n\r\n # if next_bubble:\r\n # nextcup.draw(screen)\r\n\r\n if is_game_over: # 게임이 끝나는 조건에 도달하면\r\n display_game_over() # 게임 문구 나타내기\r\n running = False\r\n\r\n pygame.display.update() # 화면에 업데이트\r\n\r\npygame.time.delay(2000) # 게임 종료시 2초 정도 딜레이 만들기\r\npygame.quit()\r\n","repo_name":"junhyeok83/gameproject","sub_path":"new/project/bubblegame.py","file_name":"bubblegame.py","file_ext":"py","file_size_in_byte":23438,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"17565444216","text":"\"\"\" Module for initializing data read from the ontology to be evaluated \"\"\"\nimport inspect\n\nfrom rdflib import Graph\n\nfrom scior.modules.logger_config import initialize_logger\nfrom scior.modules.ontology_dataclassess.dataclass_definitions import OntologyDataClass\nfrom scior.modules.ontology_dataclassess.dataclass_moving import move_classification_to_is_type, \\\n move_classification_to_not_type\nfrom scior.modules.problems_treatment.treat_errors import report_error_end_of_switch, report_error_requirement_not_met\nfrom scior.modules.resources_gufo import GUFO_NAMESPACE, GUFO_LIST_ENDURANT_TYPES\nfrom scior.modules.utils_dataclass import get_dataclass_by_uri, sort_all_ontology_dataclass_list\nfrom scior.modules.utils_rdf import get_list_of_all_classes\n\nLOGGER = initialize_logger()\n\n\ndef initialize_ontology_dataclasses(ontology_graph: Graph, scope_restriction: str) -> list[OntologyDataClass]:\n \"\"\" Receives the ontology graph (taxonomy only) and the gUFO scope to be considered.\n Returns the ontology_dataclass_list, a list with all classes in the ontology to be evaluated.\n \"\"\"\n\n LOGGER.debug(\"Initializing list of Ontology concepts...\")\n\n ontology_dataclass_list = []\n classes_list = get_list_of_all_classes_no_gufo(ontology_graph)\n\n gufo_can_list_types, gufo_can_list_individuals = get_gufo_possibilities(scope_restriction)\n\n # - URI: Ontology class name\n # - CAN_TYPE and CAN_INDIVIDUAL: list of all possible ontological categories. Receive VALUES (not a pointer)\n # loaded from the gufo_data.yaml file because the data needs to be manipulated.\n # - OTHER LISTS (IS and NOT): Empty lists. No value received.\n\n for new_class in classes_list:\n ontology_dataclass_list.append(OntologyDataClass(uri=new_class,\n can_type=gufo_can_list_types.copy(),\n can_individual=gufo_can_list_individuals.copy()))\n\n # Validating results: Scior requires the list to be non-empty. OWL Classes must exist in the input file.\n if not len(ontology_dataclass_list):\n error_message = f\"Invalid input file. The provided ontology does not have entities of type owl:Class.\"\n report_error_requirement_not_met(error_message)\n\n LOGGER.debug(\"List of Ontology concepts successfully initialized.\")\n\n return ontology_dataclass_list\n\n\ndef get_list_of_all_classes_no_gufo(ontology_graph: Graph):\n \"\"\" Returns a list of all classes *that are not GUFO classes* as URI strings without\n repetitions available in a Graph. \"\"\"\n\n list_exceptions = [GUFO_NAMESPACE]\n\n classes_list_no_gufo = get_list_of_all_classes(ontology_graph, list_exceptions)\n\n return classes_list_no_gufo\n\n\ndef get_gufo_possibilities(scope_restriction: str):\n \"\"\" Returns list of all GUFO classes available for classification in two lists (for types and individuals). \"\"\"\n\n can_list_types = []\n can_list_individuals = []\n\n if scope_restriction == \"ENDURANT_TYPES\":\n can_list_types = GUFO_LIST_ENDURANT_TYPES.copy()\n else:\n current_function = inspect.stack()[0][3]\n report_error_end_of_switch(scope_restriction, current_function)\n\n return can_list_types, can_list_individuals\n\n\ndef gufo_type_assertion_query(query_type: str) -> str:\n \"\"\" Returns the query to identify all positive or negative assertions of gUFO types in an ontology graph.\n\n :param query_type: Indication of the SPARQL query type to be returned. Allowed values are 'positive' or 'negative'.\n If invalid value is provided a ValueError exception is raised.\n :type query_type: str\n :return: Positive or negative SPARQL query.\n :rtype: str\n \"\"\"\n\n selected_query = \"not selected\"\n\n positive_query = \"\"\"\n PREFIX gufo: \n SELECT DISTINCT ?ontology_class ?class_type\n WHERE {\n ?ontology_class rdf:type owl:Class .\n ?ontology_class rdf:type ?class_type .\n FILTER(STRSTARTS(STR(?class_type), STR(gufo:)))\n } \"\"\"\n\n negative_query = \"\"\"\n PREFIX gufo: \n SELECT DISTINCT ?ontology_class ?class_type\n WHERE {\n ?ontology_class rdf:type owl:Class .\n ?ontology_class rdf:type ?x .\n ?x owl:complementOf ?class_type .\n FILTER(STRSTARTS(STR(?class_type), STR(gufo:)))\n } \"\"\"\n\n if query_type == 'positive':\n selected_query = positive_query\n elif query_type == 'negative':\n selected_query = negative_query\n else:\n current_function = inspect.stack()[0][3]\n report_error_end_of_switch(query_type, current_function)\n\n return selected_query\n\n\ndef get_known_gufo_types(input_ontology_graph: Graph, query_type: str) -> list[tuple]:\n \"\"\" For each class in the inputted ontology_graph, return all its known IS or NOT gUFO types in a tuple format.\n\n Only POSITIVE assertions are collected.\n Analogous to get_known_gufo_not_types and to get_known_gufo_individuals.\n\n :param query_type: Indication of the SPARQL query type to be returned. Allowed values are 'positive' or 'negative'.\n If invalid value is provided a ValueError exception is raised.\n :type query_type: str\n :param input_ontology_graph: Ontology's input graph.\n :type input_ontology_graph: Graph\n :return: List of tuples containing an ontology class (str) and a gufo_type (str). The tuple's first element is the\n ontology class to receive the assertion and the second is the type to be asserted to it (in short form, e.g., Kind).\n :rtype: list[tuple]\n \"\"\"\n\n list_classes = []\n list_types = []\n\n query_string = gufo_type_assertion_query(query_type)\n query_result = input_ontology_graph.query(query_string)\n\n for row in query_result:\n list_classes.append(row.ontology_class.toPython())\n list_types.append(row.class_type.toPython().replace(GUFO_NAMESPACE, \"\"))\n\n list_tuples = list(zip(list_classes, list_types))\n LOGGER.debug(f\"{query_type.title()} gUFO type assertions in the input file: {list_tuples}.\")\n\n return list_tuples\n\n\n# Not used. Not tested yet. Requires implementation of new value of SCOPE_RESTRICTION.\ndef get_known_gufo_individuals(united_graph: Graph) -> list[tuple]:\n \"\"\" For each class in the ontology_graph, return all its known GUFO INDIVIDUALS in a tuple format.\n Returned tuple format is: (ontology_class,gufo_type), being both fields strings.\n Analogous to get_known_gufo_types.\n \"\"\"\n\n list_elements = []\n list_individuals = []\n\n query_string = \"\"\"\n PREFIX gufo: \n SELECT DISTINCT ?ontology_element ?element_type\n WHERE {\n ?ontology_element rdf:type owl:Class .\n ?element_type rdf:type owl:Class .\n ?ontology_element rdfs:subClassOf ?element_type .\n ?element_type rdfs:subClassOf+ gufo:Endurant .\n FILTER(STRSTARTS(STR(?element_type), STR(gufo:)))\n } \"\"\"\n\n query_result = united_graph.query(query_string)\n\n for row in query_result:\n list_elements.append(row.ontology_element.toPython())\n list_individuals.append(row.element_type.toPython().replace(GUFO_NAMESPACE, \"\"))\n\n list_tuples = list(zip(list_elements, list_individuals))\n\n return list_tuples\n\n\ndef insert_known_gufo_information(list_known_gufo: list[tuple], ontology_dataclass_list: list[OntologyDataClass],\n assertion_type: str) -> None:\n \"\"\" Receives a list of known (positive or negative) gUFO information and performs the necessary movements of\n elements in the ontology_dataclass_list.\n\n :param list_known_gufo: List of tuples containing an ontology class (str) and a gufo_type (str).\n The tuple's first element is the ontology class to receive the assertion and the second is the type to be\n asserted to it (in short form, e.g., Kind).\n :type list_known_gufo: list[tuple]\n :param ontology_dataclass_list: List with all OntologyDataClass elements, including their URIs and internal lists.\n :type ontology_dataclass_list: list[OntologyDataClass]\n :param assertion_type: Indication of the assertion to be included into the ontology_dataclass elements.\n Allowed values are 'positive' or 'negative'. If invalid value is provided a ValueError exception is raised.\n :type assertion_type: str\n \"\"\"\n\n # Including POSITIVE information present in the input graph into the ontology_dataclass_list' is_type list\n if assertion_type == 'positive':\n for known_gufo in list_known_gufo:\n receptor_dataclass = get_dataclass_by_uri(ontology_dataclass_list, known_gufo[0])\n move_classification_to_is_type(ontology_dataclass_list, receptor_dataclass, known_gufo[1], \"Known IS type\")\n\n # Including NEGATIVE information present in the input graph into the ontology_dataclass_list' not_type list\n elif assertion_type == 'negative':\n for known_gufo in list_known_gufo:\n receptor_dataclass = get_dataclass_by_uri(ontology_dataclass_list, known_gufo[0])\n move_classification_to_not_type(ontology_dataclass_list, receptor_dataclass, known_gufo[1],\n \"Known NOT type\")\n\n # Valid values are \"positive\" and \"negative\" only\n else:\n current_function = inspect.stack()[0][3]\n report_error_end_of_switch(assertion_type, current_function)\n\n\ndef load_known_gufo_information(ontology_graph: Graph, ontology_dataclass_list: list[OntologyDataClass]) -> None:\n \"\"\" Reads gUFO information about types and instances that are available in the inputted ontology file.\n\n I.e., if a class is already known to have any GUFO type, this information is updated in the ontology_dataclass_list.\n E.g., if the class Person is set as a gufo:Kind in the loaded ontology, this stereotype is moved from the\n dataclass's can_type (default) list to its is_type list.\n\n :param ontology_graph: Updated ontology's working (RDFLib) graph on memory to be manipulated.\n :type ontology_graph: Graph\n :param ontology_dataclass_list: List with all OntologyDataClass elements, including their URIs and internal lists.\n :type ontology_dataclass_list: list[OntologyDataClass]\n \"\"\"\n\n # Setting all classes as EndurantType\n for ontology_dataclass in ontology_dataclass_list:\n move_classification_to_is_type(ontology_dataclass_list, ontology_dataclass, \"EndurantType\", \"Initialization\")\n\n # Collecting and adding known IS classifications\n list_known_is_gufo = get_known_gufo_types(ontology_graph, \"positive\")\n insert_known_gufo_information(list_known_is_gufo, ontology_dataclass_list, \"positive\")\n\n # Collecting and adding known NOT classifications\n list_known_not_gufo = get_known_gufo_types(ontology_graph, \"negative\")\n insert_known_gufo_information(list_known_not_gufo, ontology_dataclass_list, \"negative\")\n\n sort_all_ontology_dataclass_list(ontology_dataclass_list)\n\n LOGGER.debug(\"Known gUFO information from input file transferred to dataclass_ontology_list.\")\n","repo_name":"unibz-core/Scior","sub_path":"scior/modules/ontology_dataclassess/dataclass_initialization.py","file_name":"dataclass_initialization.py","file_ext":"py","file_size_in_byte":11043,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"91"}
+{"seq_id":"1598621114","text":"import os\nimport subprocess\nimport platform\n\nfrom qgis.gui import * # @UnusedWildImport\nfrom qgis.core import * # @UnusedWildImport\nfrom PyQt4 import QtCore, QtGui\n\nfrom ui.info_plus_dialog import InfoPlusDialog\nfrom ui.records_display_widget import RecordsDisplayWidget\n\n\nclass InfoPlusPoint(QgsMapTool):\n\n def __init__(self, canvas, action, settings):\n self.canvas = canvas\n self.active = False\n self.settings = settings\n QgsMapTool.__init__(self, self.canvas)\n self.setAction(action)\n self.defaultZoomScale = None\n self.currentHighlight = None\n self.loadInfoPlusPointSettings()\n \n \n def loadInfoPlusPointSettings(self):\n ''' Load plugin settings\n ''' \n self.defaultZoomScale = self.settings.value('status/defaultZoomScale', 2500) \n self.highlightTime = int(self.settings.value('status/highlightTime', 1000))\n self.centerButtonVisible = bool(int(self.settings.value('status/centerButtonVisible', 0)))\n self.zoomButtonVisible = bool(int(self.settings.value('status/zoomButtonVisible', 0))) \n self.formButtonVisible = bool(int(self.settings.value('status/formButtonVisible', 1)))\n self.recordsTitle = self.settings.value('status/recordsTitle', 'Records:')\n self.removeSelection = bool(int(self.settings.value('status/removeSelection', 1)))\n\n \n def canvasPressEvent(self, e):\n \n # Remove previous selection of all layers\n self.clearCanvas()\n \n p = self.toMapCoordinates(e.pos()) \n layers = self.canvas.layers()\n w = self.canvas.mapUnitsPerPixel() * 3\n rect = QgsRectangle(p.x()-w, p.y()-w, p.x()+w, p.y()+w)\n for layer in layers:\n if layer.type() == QgsMapLayer.RasterLayer:\n continue\n lRect = self.canvas.mapSettings().mapToLayerCoordinates(layer, rect)\n layer.select(lRect, False)\n \n # Create new dialog\n self.dlg = InfoPlusDialog()\n \n # Enable/Disable buttons\n self.dlg.center_PButton.setVisible(self.centerButtonVisible)\n self.dlg.zoom_PButton.setVisible(self.zoomButtonVisible)\n self.dlg.form_PButton.setVisible(self.formButtonVisible)\n \n # add listener to selected layer/record\n self.dlg.center_PButton.clicked.connect(self.doZoomCenterAction)\n self.dlg.zoom_PButton.clicked.connect(self.doZoomCenterAction) \n self.dlg.form_PButton.clicked.connect(self.doShowFeatureForm)\n \n # Iterate over all layers\n self.layers = self.canvas.layers()\n self.processLayers()\n \n # Show dialog\n self.dlg.show()\n \n \n def checkAction(self):\n \n # check if something is selected\n if (not self.selectedLayerId) or (not self.selectedFeatureId):\n self.iface.messageBar().pushMessage(\"\", 'Any feature selected', QgsMessageBar.WARNING, 3) \n return False\n \n # get current selected layer\n layer = QgsMapLayerRegistry.instance().mapLayer(self.selectedLayerId)\n if (not layer) or (not layer.isValid()):\n self.iface.messageBar().pushMessage(\"\", 'Any layer selected', QgsMessageBar.WARNING, 3) \n return False\n \n # get feature with the specific featureId\n iterator = layer.getFeatures(QgsFeatureRequest(self.selectedFeatureId))\n try:\n feature = iterator.next()\n except:\n # no feature found\n return False \n \n return True, layer, feature\n \n\n def doZoomCenterAction(self):\n ''' pan or zoom to the selected feature. Depend on sender\n '''\n # get current selection from current QToolBox\n currentPage = self.dlg.tbMain.currentWidget()\n (self.selectedLayerId, self.selectedFeatureId) = currentPage.getSelection()\n \n # Chack if we can proceed with the action\n (status, layer, feature) = self.checkAction()\n \n # depending on geometry do different actions\n geometry = feature.geometry()\n \n # skip if not valid\n if (geometry.type() == QGis.UnknownGeometry) or (geometry.type() == QGis.NoGeometry):\n return\n \n # distinguish if action have to be center or zoom\n centroid = geometry.centroid().asPoint()\n\n self.canvas.setCenter(centroid)\n if self.sender().objectName() == 'zoom_PButton':\n # scale of bbox depending on geom type \n if geometry.type() == QGis.Point:\n self.canvas.zoomScale(float(self.defaultZoomScale)) \n else:\n self.canvas.setExtent(geometry.boundingBox())\n \n self.canvas.refresh()\n\n \n def doShowFeatureForm(self):\n ''' show Feature Form\n ''' \n # get current selection from current QToolBox\n currentPage = self.dlg.tbMain.currentWidget()\n (self.selectedLayerId, self.selectedFeatureId) = currentPage.getSelection()\n \n # Chack if we can proceed with the action \n (status, layer, feature) = self.checkAction()\n \n if status:\n self.iface.openFeatureForm(layer, feature, False)\n \n \n def processLayers(self):\n \n # clean all toolBox elements if any, starting from the last\n # to avoid index invalidation \n for index in range(self.dlg.tbMain.count() - 1, -1, -1):\n self.dlg.tbMain.removeItem(index)\n \n # create and populate webpage\n for layer in self.layers:\n # If have any feature selected\n if layer.selectedFeatureCount() > 0:\n newPage = RecordsDisplayWidget(layer, self.iface.mainWindow())\n newPage.setObjectName('page_' + layer.id())\n newPage.setName(self.recordsTitle)\n newPage.setRemoveSelection(self.removeSelection)\n \n # record listener to open PDF or link is cliked\n newPage.docClicked.connect(self.manageDocClicked)\n newPage.linkClicked.connect(self.manageLinkClicked)\n newPage.highlightRecord.connect(self.manageHighlight)\n \n # set tab name witht the record count\n aux = layer.name()+\" (\"+str(layer.selectedFeatureCount())+\")\" \n self.dlg.tbMain.addItem(newPage, aux)\n \n\n def manageHighlight(self, layerId, featureId):\n ''' Hilight record that is currently under the mouse\n ''' \n # check if something is selected\n if (not layerId) or (not featureId):\n return\n \n # get current selected layer\n layer = QgsMapLayerRegistry.instance().mapLayer(layerId)\n if (not layer) or (not layer.isValid()):\n return\n \n # get feature with the specific featureId\n iterator = layer.getFeatures(QgsFeatureRequest(int(featureId)))\n try:\n feature = iterator.next()\n except:\n # no feature found\n return\n \n # depending on geometry do different actions\n geometry = feature.geometry()\n \n # skip if not valid\n if (geometry.type() == QGis.UnknownGeometry) or (geometry.type() == QGis.NoGeometry):\n return\n \n # create Hilight\n self.currentHighlight = QgsHighlight(self.canvas, geometry, layer)\n \n # set standard style\n color = QtGui.QColor(self.settings.value(\"/Map/highlight/color\", QGis.DEFAULT_HIGHLIGHT_COLOR.name()))\n alpha = int(self.settings.value(\"/Map/highlight/colorAlpha\", QGis.DEFAULT_HIGHLIGHT_COLOR.alpha()))\n buffer_ = float(self.settings.value(\"/Map/highlight/buffer\", QGis.DEFAULT_HIGHLIGHT_BUFFER_MM))\n minWidth = float(self.settings.value(\"/Map/highlight/minWidth\", QGis.DEFAULT_HIGHLIGHT_MIN_WIDTH_MM))\n \n self.currentHighlight.setColor(color) # sets also fill with default alpha\n color.setAlpha(alpha)\n self.currentHighlight.setFillColor(color) # sets fill with alpha\n self.currentHighlight.setBuffer(buffer_)\n self.currentHighlight.setMinWidth(minWidth)\n self.currentHighlight.show()\n \n # remove highlight after a while\n QtCore.QTimer.singleShot(self.highlightTime, self.removeHighlight)\n \n \n def removeHighlight(self):\n ''' remove current Highlight\n '''\n if self.currentHighlight:\n del self.currentHighlight\n self.currentHighlight = None\n \n \n def manageDocClicked(self, layerId, featureId, document):\n ''' Open a doc document that has been clicked\n '''\n # manage filepath if absolute or relaltive\n filepath = document\n if not os.path.isabs(filepath):\n filepath = os.path.join( os.path.dirname(__file__), filepath)\n \n # check if exist\n if not os.path.exists(filepath):\n self.iface.messageBar().pushWarning('', self.tr('File {} does not exist'.format(filepath)))\n return\n \n if not os.path.isfile(filepath):\n self.iface.messageBar().pushWarning('', self.tr('{} is not a file'.format(filepath)))\n return\n \n # then open\n self._openUri(filepath)\n \n \n def manageLinkClicked(self, layerId, featureId, link):\n ''' Open a link that has been clicked\n '''\n self._openUri(link)\n \n \n def _openUri(self, uri):\n ''' Usefult to open Uri using default desktop application\n '''\n platformName = platform.system().lower()\n if platformName.startswith('linux'):\n subprocess.call(('xdg-open', uri))\n elif platformName.startswith('win') or platformName.startswith('cygwin'):\n os.startfile(uri)\n elif platformName.startswith('darwin'):\n subprocess.call(('open', uri))\n \n \n def setInterface(self, iface):\n self.iface = iface\n \n \n def clearCanvas(self):\n # Remove selection of all layers\n layers = self.iface.mapCanvas().layers()\n for layer in layers:\n if layer.type() == layer.VectorLayer:\n layer.removeSelection()\n self.iface.mapCanvas().refresh() \n ","repo_name":"psigcat/infoplus","sub_path":"info_plus_point.py","file_name":"info_plus_point.py","file_ext":"py","file_size_in_byte":10422,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"}
+{"seq_id":"15272393984","text":"from flask import Flask\nfrom flask_swagger_ui import get_swaggerui_blueprint\nfrom data import db_session, items_api\nimport logging\nfrom configparser import ConfigParser\n\ndb_session.global_init(\"db/offers.db\")\napp = Flask(__name__)\n\napp.config[\"JSON_AS_ASCII\"] = False\napp.register_blueprint(items_api.blueprint)\n\napp.logger.setLevel(logging.INFO)\n\nconfig = ConfigParser()\nconfig.read(\"settings.ini\")\napp.config[\"SECRET_KEY\"] = config.get(\"settings\", \"secret_key\")\n\nSWAGGER_URL = '/docs'\nAPI_URL = '/static/openapi.yaml'\n\nswagger_ui_blueprint = get_swaggerui_blueprint(\n SWAGGER_URL,\n API_URL,\n config={\n 'app_name': 'Mega Market Open API'\n }\n)\n\napp.register_blueprint(swagger_ui_blueprint, url_prefix=SWAGGER_URL)\n\nif __name__ == '__main__':\n app.run(host=\"0.0.0.0\", debug=True)\n","repo_name":"Trrent/backendYandexSummerProject","sub_path":"app/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":801,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"15052050863","text":"import asyncio\n\nfrom examples.template import get_client_parser, parse_args\n\n\nclass EchoProtocol(asyncio.Protocol):\n\n def __init__(self):\n self.nbytes = 0\n\n def connection_made(self, transport):\n sock = transport.get_extra_info('socket')\n args.debug('Connection to {}'.format(sock))\n self.transport = transport\n\n def data_received(self, data):\n self.nbytes += len(data)\n self.receive_event.set()\n\n def eof_received(self):\n self.transport.close()\n\n def connection_lost(self, exc):\n args.debug('Close the socket', self.transport, exc)\n self.transport = None\n\n\nasync def wrapper(loop, address, count):\n if isinstance(address, str):\n transport, protocol = await loop.create_unix_connection(\n EchoProtocol, address\n )\n else:\n transport, protocol = await asyncio.create_connection(\n EchoProtocol, *address\n )\n\n write = transport.write\n req = b'a' * args.msize\n receive_event = protocol.receive_event = asyncio.Event()\n for x in range(count):\n write(req)\n await receive_event.wait()\n receive_event.clear()\n transport.write_eof()\n transport.close()\n assert protocol.nbytes == count * args.msize, \\\n (protocol.nbytes, count * args.msize)\n\n\nasync def main():\n global args\n args = parse_args(get_client_parser())\n loop = asyncio.get_running_loop()\n tasks = []\n for x in range(args.concurrency):\n tasks.append(asyncio.create_task(\n wrapper(loop, args.address, args.request)\n ))\n\n await asyncio.gather(*tasks)\n\n\nif __name__ == \"__main__\":\n asyncio.run(main())\n","repo_name":"Catstyle/pymaid","sub_path":"examples/echo_asyncio/asyncio_protocol_client.py","file_name":"asyncio_protocol_client.py","file_ext":"py","file_size_in_byte":1676,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"91"}
+{"seq_id":"34339853894","text":"#!/usr/bin/env python3\n\nimport pathlib\nfrom pprint import pprint\nfrom collections import defaultdict\n\n\ndef get_rabbit_pairs(alive_n, die_after_m):\n # die_after_n - die after n months\n # alive_m - all rabbits live for m months\n ages_amount = {1: 1}\n rabbit_amount = 1\n for month in range(1, alive_n):\n month_ages_amount = defaultdict(int)\n rabbit_amount = 0\n for item in ages_amount:\n if item == die_after_m:\n # if age of a pair is equal to ->\n # then 1. pair will die and\n # 2. we will get only one pair of offsprint\n month_ages_amount[1] += ages_amount[item]\n elif item == 1:\n # if the pair of rabbit is small ->\n # we will 1. save the pair and\n # 2. will not get any offsprings\n month_ages_amount[item + 1] = ages_amount[item]\n elif item > 1:\n # if the pair of rabbit is big ->\n # we will 1. save the pair and\n # 2. will get offsprings\n month_ages_amount[item + 1] = ages_amount[item]\n month_ages_amount[1] += ages_amount[item]\n del ages_amount\n ages_amount = month_ages_amount\n\n rabbit_amount = 0\n for age in ages_amount:\n rabbit_amount += ages_amount[age]\n return rabbit_amount\n\n\ndef main():\n in_data = ''\n current_dir = pathlib.Path(__file__).parent\n with open(f'{current_dir}/input.txt') as fp:\n n, m = map(int, fp.read().split(' '))\n print(get_rabbit_pairs(n, m))\n\n\nif __name__ == \"__main__\":\n main()","repo_name":"kewtree1408/rosalind","sub_path":"problems/fibd/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1637,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"}
+{"seq_id":"10749847709","text":"import keras\nfrom keras import backend as K\nfrom keras.applications.inception_v3 import InceptionV3\nfrom keras.applications.resnet50 import ResNet50\nfrom keras.applications.densenet import DenseNet121\nfrom keras.callbacks import TensorBoard, ModelCheckpoint, EarlyStopping\nfrom keras.layers import Dense, GlobalAveragePooling2D, Flatten, concatenate, Input\nfrom keras.models import Model, model_from_json, load_model\nfrom keras.preprocessing import image\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.optimizers import SGD\nfrom keras.utils import np_utils\nfrom tensorflow.python.lib.io import file_io\n\nimport h5py\nimport argparse\nimport numpy as np\nimport tensorflow as tf\nimport sys\nfrom time import time\nfrom sklearn.metrics import confusion_matrix\n\ndef train_model(data_file, job_dir, context_model_file, data_bucket, training_epochs, **args):\n print('Tensorflow version: ' + tf.__version__)\n print('Keras version: ' + keras.__version__)\n print ('Data bucket: ' + data_bucket)\n print ('Data file: ' + data_file)\n print ('Context model file: ' + context_model_file)\n print ('Job directory: ' + job_dir)\n print ('Training epochs: ' + str(training_epochs))\n\n ## Prepare training data\n img_height = 224\n img_width = 224\n batch_size = 16\n\n if K.image_data_format() == 'channels_first':\n input_shape = (3, img_width, img_height)\n else:\n input_shape = (img_width, img_height, 3)\n\n train_set_key = 'train_set_'\n valid_set_key = 'valid_set_'\n classes_key = 'list_classes'\n\n print ('Trying to read file: ' + data_bucket + data_file)\n with file_io.FileIO(data_bucket + data_file, mode='rb') as input_f:\n print ('Opened file')\n with file_io.FileIO(data_file, mode='wb+') as output_f:\n output_f.write(input_f.read())\n print ('Written file to: ' + data_file)\n\n print ('Copied ' + data_file + ' locally')\n\n # Read h5 dataset file\n dataset = h5py.File(data_file, 'r')\n list_classes = dataset[classes_key]\n classes = list_classes.shape[0]\n train_set_x = dataset[train_set_key + 'x']\n valid_set_x = dataset[valid_set_key + 'x']\n\n print ('Converted train/validation set to categorical')\n train_set_y = np_utils.to_categorical(dataset[train_set_key + 'y'], classes)\n valid_set_y = np_utils.to_categorical(dataset[valid_set_key + 'y'], classes)\n print ('Read datasets from h5 file')\n\n # Create data generator\n datagen = ImageDataGenerator(rescale = 1./255)\n\n ## Give same input to both models\n model_input = Input(shape = input_shape)\n\n print ('Trying to read file: ' + data_bucket + context_model_file)\n with file_io.FileIO(data_bucket + context_model_file, mode='rb') as input_f:\n print ('Opened file')\n with file_io.FileIO(context_model_file, mode='wb+') as output_f:\n output_f.write(input_f.read())\n print ('Written file to: ' + context_model_file)\n\n print ('Copied ' + context_model_file + ' locally')\n ## Load context recognition model pre-trained on 27 categories of Places205\n loaded_context_model = load_model(context_model_file)\n\n # Construct new context CNN model with pre-trained weights loaded but connected to input tensor\n context_cnn = DenseNet121(weights = None, include_top = False, input_tensor = model_input)\n x = GlobalAveragePooling2D()(context_cnn.output)\n x = Dense(1024, activation = 'relu')(x)\n context_predictions = Dense(27, activation = 'softmax')(x)\n context_model = Model(inputs = model_input, outputs = context_predictions)\n for new_layer, layer in zip(context_model.layers[1:], loaded_context_model.layers[1:]):\n new_layer.set_weights(layer.get_weights())\n\n ## Load object recognition model pre-trained on ImageNet\n object_model = ResNet50(include_top=True, weights='imagenet', input_tensor = model_input, pooling=None, classes=1000)\n # Hack to avoid https://github.com/keras-team/keras/issues/3974\n for i in range(1,len(object_model.layers)):\n object_model.layers[i].name = object_model.layers[i].name + '1'\n\n ## Merge/concatenate outputs of the two sub-models\n context_output = context_model.output\n object_output = object_model.output\n y = concatenate([context_output, object_output])\n\n ## Add extra layer(s) for classification of activities\n y = Dense(1024, activation = 'relu')(y)\n predictions = Dense(classes, activation = 'softmax')(y)\n\n ## Set C-CNN layers to be not trainable\n for layer in context_model.layers:\n layer.trainable = False\n\n ## Compile model\n model = Model(inputs = model_input, outputs = predictions)\n model.compile(optimizer = 'rmsprop', loss = 'categorical_crossentropy', metrics = ['accuracy'])\n\n model_filename = 'activity-recognition-model.h5'\n checkpoint = ModelCheckpoint(model_filename, verbose = 1, monitor = 'val_acc', save_best_only = True, mode = 'auto')\n\n print ('Starting fit_generator on model')\n # Fits the model on batches\n model.fit_generator(datagen.flow(train_set_x, train_set_y, batch_size = batch_size),\n steps_per_epoch = len(train_set_x) / batch_size,\n epochs = training_epochs,\n validation_data = datagen.flow(valid_set_x, valid_set_y, batch_size = batch_size),\n validation_steps = len(valid_set_x) / batch_size,\n callbacks = [checkpoint])\n print ('Finished training only the extra layers')\n\n # # Save the model locally\n # model.save(model_filename)\n # print ('Saved model to :' + model_filename)\n\n # Save the model to the Cloud Storage bucket's jobs directory\n with file_io.FileIO(model_filename, mode='rb') as input_f:\n with file_io.FileIO(job_dir + '/' + model_filename, mode='wb+') as output_f:\n output_f.write(input_f.read())\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n '--data_bucket',\n help='Cloud Storage bucket where dataset is located',\n required=True)\n\n parser.add_argument(\n '--data_file',\n help='Training data file name',\n required=True)\n\n parser.add_argument(\n '--job-dir',\n help='Cloud Storage bucket to export the model and store temp files',\n required=True)\n\n parser.add_argument(\n '--context_model_file',\n help='Filename of context recognition model',\n required=True)\n\n parser.add_argument(\n '--training_epochs',\n help='Number of epochs for training only the extra layers',\n type=int,\n default=20)\n\n args = parser.parse_args()\n arguments = args.__dict__\n\n train_model(**arguments)","repo_name":"ciprianfocsaneanu/EventRecognitionCNN","sub_path":"object-context-recognition/cloud-trainer/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":6681,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"}
+{"seq_id":"10806899993","text":"from kivy.app import App\r\n\r\nfrom kivy.uix.gridlayout import GridLayout\r\nfrom kivy.uix.label import Label\r\nfrom kivy.uix.textinput import TextInput\r\nfrom kivy.uix.button import Button\r\nfrom kivy.uix.screenmanager import ScreenManager, Screen\r\n\r\nfrom item import Item\r\nfrom functools import partial\r\nimport dill as pickle\r\n\r\nFUNCBUTTONS = [1,0,1,1]\r\n\r\n \r\n\r\n\r\nclass ItemPage(Screen):\r\n\r\n def __init__(self,theItem,cameFrom,**kwargs):\r\n super(ItemPage,self).__init__(**kwargs)\r\n self.theItem = theItem\r\n self.father = cameFrom\r\n self.containsMore = self.theItem.containsItems()#Is there items inside\r\n self.inside = self.theItem.getAllItems()#A list of all items inside\r\n \r\n self.base = GridLayout(cols=1)\r\n\r\n self.backButton = Button(text='Back',background_color=FUNCBUTTONS)#Add back button\r\n self.goBack = partial(self.changeScreens,self.father)\r\n self.backButton.bind(on_press=self.goBack)\r\n self.base.add_widget(self.backButton)\r\n\r\n self.addGrid = GridLayout(rows=1)#Add 'add' button\r\n self.addButton = Button(text='Add',background_color=FUNCBUTTONS)\r\n self.addInput = TextInput()\r\n self.adding = partial(self.addItem,self.addInput.text)\r\n self.addButton.bind(on_press=self.addItem)\r\n self.addGrid.add_widget(self.addButton)\r\n self.addGrid.add_widget(self.addInput)\r\n self.base.add_widget(self.addGrid)\r\n \r\n if self.containsMore:#Display all items contained within Item Object\r\n for item in self.inside:\r\n self.bCol = [1,1,1,1]\r\n self.iCon = False\r\n if item.containsItems():#check if item contains other items\r\n self.bCol = [0,1,1,1]\r\n self.iCon = True\r\n \r\n sButton = Button(background_color=self.bCol,text=item.viewName())\r\n self.whenPressed = partial(self.nextLevel,item)\r\n sButton.bind(on_press=self.whenPressed)\r\n self.base.add_widget(sButton)\r\n \r\n else:\r\n self.bCol = [1,1,1,1]\r\n self.base.add_widget(Label(text=self.theItem.viewName()))\r\n \r\n \r\n \r\n self.add_widget(self.base)\r\n\r\n def nextLevel(self,theItem,*args):\r\n if not self.manager.has_screen(theItem.viewName()):\r\n nextPage = ItemPage(theItem,self.theItem.viewName(),name=theItem.viewName())\r\n self.manager.add_widget(nextPage)\r\n self.manager.current = theItem.viewName()\r\n\r\n def changeScreens(self,changeTo,*args):\r\n toUpdate = self.manager.get_screen(changeTo)\r\n toUpdate.updateScreen()\r\n self.manager.current = changeTo\r\n\r\n def updateScreen(self,*args):\r\n self.remove_widget(self.base)\r\n self.__init__(self.theItem,self.father)\r\n\r\n def addItem(self,*args):\r\n if len(self.addInput.text) > 0:\r\n print('Adding Item',self.addInput.text,'!')\r\n #addTo = self.manager.get_screen(self.father)\r\n #addTo.beingAdded(self.addInput.text)\r\n self.beingAdded(self.addInput.text)\r\n self.changeScreens(self.theItem.viewName())\r\n\r\n def beingAdded(self,name,*args):\r\n print(name,'is being added to',self.theItem.viewName())\r\n self.theItem.addItem(name)\r\n\r\n def saveItem(self,*args):\r\n pass\r\n \r\nSAVEFILE = 'masterSave'\r\n\r\n\r\nclass RecursiveToDoList(App):\r\n\r\n\r\n\r\n def on_stop(self):\r\n self.saveState(self.saveFile)\r\n\r\n def build(self):\r\n self.saveFile = SAVEFILE\r\n try:\r\n self.loadState(self.saveFile)\r\n except:\r\n print('Could not load',self.saveFile)\r\n self.createSample()\r\n sm = ScreenManager()\r\n self.homePage = ItemPage(self.baseItem,self.baseItem.viewName(),name=self.baseItem.viewName())\r\n sm.add_widget(self.homePage)\r\n return sm\r\n\r\n def createSample(self,*args):\r\n print('Loading sample state')\r\n self.baseItem = Item('Home')#Default Screen\r\n self.anotherItem = Item('To Do')\r\n self.anotherItem0 = Item('To Occupy Myself')\r\n self.anotherItemL2a = Item('Go Shopping')\r\n self.anotherItemL2b = Item('Write CV')\r\n self.anotherItemL3a = Item('Lasagne')\r\n self.anotherItemL2a.directAddItem(self.anotherItemL3a) #Add Level 3 item to Level 2\r\n self.anotherItem.directAddItem(self.anotherItemL2a) #Add Level 2 item to Level 1\r\n self.anotherItem.directAddItem(self.anotherItemL2b) #Add Level 2 item to Level 1\r\n self.baseItem.directAddItem(self.anotherItem) #Add Level 1 item to Level 0\r\n self.baseItem.directAddItem(self.anotherItem0) #Add Level 1 item to Level 0\r\n\r\n def saveState1(self,*args):\r\n self.baseItem.saveItem(self.saveFile)\r\n\r\n def saveState(self,saveFile,*args):\r\n with open(saveFile,'wb') as sF:\r\n pickle.dump(self.baseItem,sF)\r\n\r\n def loadState(self,loadFile,*args):\r\n with open(loadFile,'rb') as lF:\r\n self.baseItem = pickle.load(lF)\r\n print('baseItem initialised')\r\n\r\n\r\n\r\n\r\n#-------TODO-------#\r\n #Add update function to screens - use self.manager.get_screen(name) ? -- DONE\r\n #call update function ever screen load -- DONE\r\n #add 'Add' button to screens -- DONE\r\n #save classes -- DONE\r\n #load classes -- DONE\r\n #add delete button\r\n #add edit button\r\n #settings menu?\r\n #add reset button\r\n \r\n\r\nif __name__=='__main__':\r\n RecursiveToDoList().run()\r\n #myApp.saveState()\r\n","repo_name":"madpianoguy/RTDL","sub_path":"RTDL.py","file_name":"RTDL.py","file_ext":"py","file_size_in_byte":5821,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"506346536","text":"# all the functions in this file were prepared for other course with Bogna Jaszczak who is co-owner of 4 first functions\n# below.\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport scipy.stats\nfrom list_3.models.utils import show_statistics\n\n\ndef pdf_emp(data: np.array, bins: int = 100, show: bool = False, **kwargs) -> None:\n \"\"\" function plotting a histogram of the given data.\n Args:\n data (np.array): given data\n bins (int): bins of the histogram\n show (bool): show indicator. Defaults to None.\n **kwargs: Optional arguments for matplotlib.pyplot.histogram() function\n Returns:\n None\n \"\"\"\n plt.hist(data, bins=bins, density=True, **kwargs)\n if show:\n plt.show()\n\n\ndef cdf_emp(data: np.array, show: bool = False, **kwargs) -> None:\n \"\"\" function plotting a CDF of the given data.\n Args:\n data (np.array): given data\n show (bool): show indicator. Defaults to None.\n **kwargs: Optional arguments for matplotlib.pyplot.plot() function\n Returns:\n None\n \"\"\"\n data.sort()\n y = np.arange(len(data))/float(len(data))\n\n plt.plot(data, y, 'b-', **kwargs)\n if show:\n plt.show()\n\n\ndef dist_cdf_plot(domain: tuple = (-4, 4), distribution=scipy.stats.norm, show: bool = False, **kwargs) -> None:\n \"\"\" function plotting a CDF of the given distribution.\n Args:\n domain (tuple): domain of the distribution.\n distribution: scipy.stats theoretical distribution. Defaults to scipy.stats.norm.\n show (bool): show indicator. Defaults to None.\n **kwargs: Optional arguments for the distribution.\n Returns:\n None\n \"\"\"\n x = np.linspace(domain[0], domain[1], 1000)\n plt.plot(x, distribution.cdf(x, **kwargs), 'r.')\n if show:\n plt.show()\n\n\ndef dist_pdf_plot(domain: tuple = (-4, 4), distribution=scipy.stats.norm, discrete: bool = False, show: bool = False, **kwargs) -> None:\n \"\"\" function plotting a PDF of the given distribution.\n Args:\n domain (tuple): domain of the distribution.\n distribution: scipy.stats theoretical distribution. Defaults to scipy.stats.norm.\n discrete (bool): indicator of discrete distribution. Defaults to False.\n show (bool): show indicator. Defaults to None.\n **kwargs: Optional arguments for the distribution.\n Returns:\n None\n \"\"\"\n if discrete:\n\n x = np.linspace(domain[0], domain[1], 1+domain[1]-domain[0])\n plt.plot(x, distribution.pmf(x, **kwargs), 'r.', label='PMF')\n else:\n x = np.linspace(domain[0], domain[1], 1000)\n plt.plot(x, distribution.pdf(x, **kwargs), 'r.', label='PDF')\n\n if show:\n plt.show()\n\n\ndef show_degree_distribution(network):\n\n stats = show_statistics(network)\n print(f'statistics: {stats}')\n\n degree_list = [val for key, val in network.degree()]\n domain_start, domain_end = min(degree_list), max(degree_list)\n plt.figure(0)\n plt.xlabel('x')\n plt.ylabel('pdf')\n pdf_emp(degree_list, bins=domain_end - domain_start, label='EPMF')\n\n return stats\n","repo_name":"rogulforce/DPoCN","sub_path":"list_3/models/plots.py","file_name":"plots.py","file_ext":"py","file_size_in_byte":3091,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"40856680172","text":"import requests\nfrom bs4 import BeautifulSoup\nimport os\nimport time\nimport json\nimport string\n\n\nwebsite = 'https://www.hooktheory.com'\nbase_url = website + '/theorytab/artists/'\nsleep_time = 0.11\nalphabet_list = string.ascii_lowercase\n\n\ndef song_retrieval(artist, song, path_song):\n\n song_url = 'https://www.hooktheory.com/theorytab/view/' + artist + '/' + song\n response_song = requests.get(song_url)\n\n soup = BeautifulSoup(response_song.text, 'html.parser')\n li_list = soup.findAll(\"li\", {\"role\": \"presentation\"})\n\n section_list = []\n pk_list = []\n\n # section\n for i in range(len(li_list)-1):\n sec = li_list[i].text.strip().lower().replace(\" \", \"-\")\n section_list.append(sec)\n pk_list.append(soup.findAll(\"div\", {\"role\": \"tabpanel\", \"id\": sec})[0].contents[0]['id'])\n\n # save xml\n for idx, pk in enumerate(pk_list):\n req_url = 'https://www.hooktheory.com/songs/getXmlByPk?pk=' + str(pk)\n response_info = requests.get(req_url)\n content = response_info.text\n\n with open(os.path.join(path_song, section_list[idx] + \".xml\"), \"w\", encoding=\"utf-8\") as f:\n f.write(content)\n time.sleep(0.08)\n\n # get genre\n wikiid = soup.findAll(\"multiselect\", {\"items\": \"genres\"})[0]['wikiid']\n response_genre = requests.get('https://www.hooktheory.com/wiki/' + str(wikiid) + '/genres')\n genre_act_list = json.loads(response_genre.text)\n genres = []\n for g in genre_act_list:\n if g['active']:\n genres.append(g['name'])\n\n # saving\n info = {'section': section_list, 'pk': pk_list, 'song_url': song_url,\n 'genres': genres, 'wikiid': wikiid}\n\n with open(os.path.join(path_song, 'song_info.json'), \"w\") as f:\n json.dump(info, f)\n\n\ndef get_song_list(url_artist, quite=False):\n response_tmp = requests.get(website + url_artist)\n soup = BeautifulSoup(response_tmp.text, 'html.parser')\n item_list = soup.find_all(\"li\", {\"class\": \"grid-item\"})\n\n song_name_list = []\n for item in item_list:\n song_name = item.find_all(\"a\", {\"class\": \"a-tab-cover\"})[0]['href'].split('/')[-1]\n song_name_list.append(song_name)\n if not quite:\n print(' > %s' % song_name)\n return song_name_list\n\n\ndef traverse_website():\n '''\n Retrieve all urls of artists and songs from the website\n '''\n\n list_pages = []\n archive_artist = dict()\n artist_count = 0\n song_count = 0\n\n for ch in alphabet_list:\n time.sleep(sleep_time)\n url = base_url + ch\n response_tmp = requests.get(url)\n soup = BeautifulSoup(response_tmp.text, 'html.parser')\n page_count = 0\n\n print('==[%c]=================================================' % ch)\n\n # get artists list by pages\n url_artist_list = []\n for page in range(1, 9999):\n url = 'https://www.hooktheory.com/theorytab/artists/'+ch+'?page=' + str(page)\n\n time.sleep(sleep_time)\n response_tmp = requests.get(url)\n soup = BeautifulSoup(response_tmp.text, 'html.parser')\n item_list = soup.find_all(\"li\", {\"class\": \"overlay-trigger\"})\n\n if item_list:\n print(url)\n page_count += 1\n else:\n break\n\n for item in item_list:\n url_artist_list.append(item.find_all(\"a\", {\"class\": \"a-no-decoration\"})[0]['href'])\n\n print('Total:', len(url_artist_list))\n\n print('----')\n\n if not page_count:\n page_count = 1\n\n # get song of artists\n artist_song_dict = dict()\n\n for url_artist in url_artist_list:\n artist_count += 1\n time.sleep(sleep_time)\n artist_name = url_artist.split('/')[-1]\n print(artist_name)\n song_name_list = get_song_list(url_artist)\n song_count += len(song_name_list)\n artist_song_dict[artist_name] = song_name_list\n\n archive_artist[ch] = artist_song_dict\n list_pages.append(page_count)\n\n print('=======================================================')\n print(list_pages)\n print('Artists:', artist_count)\n print('Songs:', song_count)\n\n archive_artist['num_song'] = song_count\n archive_artist['num_artist'] = artist_count\n\n with open('archive_artist.json', \"w\") as f:\n json.dump(archive_artist, f)\n\n\nif __name__ == '__main__':\n\n traverse_website()\n\n # root for crawled dataset\n root_dir = 'archive'\n with open('archive_artist.json', \"r\") as f:\n archive_artist = json.load(f)\n\n count_ok = 0\n song_count = archive_artist['num_song']\n\n for ch in alphabet_list:\n path_ch = os.path.join(root_dir, ch)\n print('==[%c]=================================================' % ch)\n\n if not os.path.exists(path_ch):\n os.makedirs(path_ch)\n\n for a_name in archive_artist[ch].keys():\n for s_name in archive_artist[ch][a_name]:\n\n try:\n print('(%3d/%3d) %s %s' % (count_ok, song_count, a_name, s_name))\n path_song = os.path.join(path_ch, a_name, s_name)\n\n if not os.path.exists(path_song):\n os.makedirs(path_song)\n\n time.sleep(sleep_time)\n song_retrieval(a_name, s_name, path_song)\n\n count_ok += 1\n\n except Exception as e:\n print(e)\n\n print('total:', count_ok)\n","repo_name":"wayne391/symbolic-music-datasets","sub_path":"theorytab/utils/theorytab_crawler.py","file_name":"theorytab_crawler.py","file_ext":"py","file_size_in_byte":5496,"program_lang":"python","lang":"en","doc_type":"code","stars":119,"dataset":"github-code","pt":"91"}
+{"seq_id":"38958973946","text":"from labs.lab_3.point import Point\nfrom labs.lab_3.rectangle import Rectangle\nfrom labs.lab_3.rectangle.cuboid import Cuboid\nfrom labs.lab_3.bank.account import Account\nfrom labs.lab_3.vehicle.bus import Bus\nfrom labs.lab_3.vehicle import Vehicle\n\n\ndef test_bank():\n acc = Account(3, 'belal', 1000)\n print(acc)\n print(f\"Deposit 100 = {acc.deposit(100)}, Withdraw 600 = {acc.withdraw(600)}\")\n\n\ndef test_bus():\n vehicle_1 = Vehicle(120, 12000)\n bus_1 = Bus(70, 300000)\n print(vehicle_1)\n print(bus_1)\n\n\ndef test_rectangle():\n rect = Rectangle(3, 4)\n print(rect)\n cuboid = Cuboid(3, 4, 5)\n print(cuboid)\n\n\ndef test_point():\n a = Point(1, 2)\n b = Point(7, 8)\n print(a)\n a.move(3, 2)\n print(a)\n print(b)\n print(b - a)\n\n\ndef run():\n test_point()\n","repo_name":"belsabbagh/artificial-intelligence-coursework","sub_path":"labs/lab_3/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":797,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"18193002305","text":"\"\"\"distoptim.torchdist package\"\"\"\r\nimport logging\r\nimport collections\r\nimport os\r\nimport os.path as op\r\nimport re\r\nimport json\r\nimport torch\r\nimport torch.distributed as dist\r\n\r\nLOG = logging.getLogger(__name__)\r\n\r\n\r\ndef _get_philly_master_config_idx0():\r\n runtime_config_file = os.environ.get(\"PHILLY_RUNTIME_CONFIG\")\r\n with open(runtime_config_file) as _f:\r\n runtime_config = json.load(_f)\r\n for name, config in runtime_config[\"containers\"].items():\r\n if config[\"index\"] == 0:\r\n master_config = config\r\n return master_config\r\n\r\ndef _get_philly_master_config_mpihost():\r\n runtime_config_file = os.environ.get(\"PHILLY_RUNTIME_CONFIG\")\r\n with open(runtime_config_file) as _f:\r\n runtime_config = json.load(_f)\r\n path = runtime_config[\"scratchDirectory\"]\r\n mpi_host_file = op.join(path, \"mpi-hosts\")\r\n with open(mpi_host_file, \"r\") as f:\r\n master_line = f.readline().strip()\r\n master_line_list = master_line.split(\" \")\r\n master_name = master_line_list[0]\r\n master_name = master_name.replace(\"-\", \"_\")\r\n master_config = runtime_config[\"containers\"][master_name]\r\n return master_config\r\n\r\ndef _get_init_method_philly():\r\n # master_config = _get_philly_master_config_mpihost()\r\n master_config = _get_philly_master_config_idx0()\r\n master_ip = master_config[\"ip\"]\r\n master_port = master_config[\"portRangeStart\"] + 1\r\n return f\"tcp://{master_ip}:{master_port}\"\r\n\r\ndef _get_init_method_aml():\r\n # AZ_BATCHAI_MPI_MASTER_NODE or AZ_BATCHAI_JOB_MASTER_NODE_IP\r\n # \"AZ_BATCH_MASTER_NODE\": \"10.0.0.6:6000\" # only on multi node\r\n\r\n if \"AZ_BATCH_MASTER_NODE\" in os.environ:\r\n return \"tcp://\" + os.environ.get(\"AZ_BATCH_MASTER_NODE\")\r\n \r\n master_ip = os.environ.get(\"AZ_BATCHAI_JOB_MASTER_NODE_IP\")\r\n return f\"tcp://{master_ip}:6000\"\r\n\r\ndef _get_init_method():\r\n if \"PHILLY_RUNTIME_CONFIG\" in os.environ:\r\n return _get_init_method_philly()\r\n return _get_init_method_aml()\r\n\r\n\r\ndef rank():\r\n \"\"\"Find OMPI world rank without calling mpi functions\r\n :rtype: int\r\n \"\"\"\r\n return int(os.environ.get(\"OMPI_COMM_WORLD_RANK\") or 0)\r\n\r\n\r\ndef size():\r\n \"\"\"Find OMPI world size without calling mpi functions\r\n :rtype: int\r\n \"\"\"\r\n return int(os.environ.get(\"OMPI_COMM_WORLD_SIZE\") or 1)\r\n\r\n\r\ndef local_rank():\r\n \"\"\"Find OMPI local rank without calling mpi functions\r\n :rtype: int\r\n \"\"\"\r\n return int(os.environ.get(\"OMPI_COMM_WORLD_LOCAL_RANK\") or 0)\r\n\r\n\r\ndef local_size():\r\n \"\"\"Find OMPI local size without calling mpi functions\r\n :rtype: int\r\n \"\"\"\r\n return int(os.environ.get(\"OMPI_COMM_WORLD_LOCAL_SIZE\") or 1)\r\n\r\n\r\ndef barrier():\r\n \"\"\"Call dist.barrier()\"\"\"\r\n dist.barrier()\r\n\r\n\r\ndef broadcast(tensor, root_rank):\r\n \"\"\"broadcast one tensor.\"\"\"\r\n dist.broadcast(tensor, root_rank)\r\n\r\n\r\ndef local_init(backend):\r\n \"\"\"Local init\"\"\"\r\n master_ip = \"127.0.1.1\"\r\n master_port = \"50065\"\r\n dist.init_process_group(\r\n backend=backend,\r\n init_method=f\"tcp://{master_ip}:{master_port}\",\r\n world_size=size(),\r\n rank=rank(),\r\n )\r\n\r\n\r\ndef init(backend=\"nccl\"):\r\n \"\"\"Init torch distributed using TCP\"\"\"\r\n\r\n LOG.info(\"Initializing torch distributed\")\r\n print(\"Initializing torch distributed\")\r\n\r\n if dist.is_initialized():\r\n LOG.info(\"Torch distributed already initialized!\")\r\n print(\"Torch distributed already initialized!\")\r\n return\r\n\r\n init_method = _get_init_method()\r\n _world_size = size()\r\n _rank = rank()\r\n LOG.info(\r\n f\"Init Method: {init_method}, World Size: {_world_size}, \"\r\n f\"Backend: {backend}, rank: {_rank}\"\r\n )\r\n print(\r\n f\"Init Method: {init_method}, World Size: {_world_size}, \"\r\n f\"Backend: {backend}, rank: {_rank}\"\r\n )\r\n dist.init_process_group(\r\n backend=backend,\r\n init_method=init_method,\r\n world_size=_world_size,\r\n rank=_rank,\r\n )\r\n LOG.info(\"init_process_group done!\")\r\n print(\"init_process_group done!\")\r\n\r\n\r\ndef broadcast_parameters(params, root_rank):\r\n \"\"\"Similar to horovod broadcast_parameters.\"\"\"\r\n params = sorted(params.items())\r\n for _, _p in params:\r\n dist.broadcast(_p, root_rank)\r\n dist.barrier()\r\n","repo_name":"YuweiYin/MNMT_Aux_Src_Lang","sub_path":"MNMT_Aux_Src_Lang/distoptim/torchdist/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4334,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"37615066082","text":"def removeDup(A):\n head = 0\n for i in range(len(A) - 2):\n if not A[i] == A[i + 1] == A[i + 2]:\n A[head] = A[i]\n head += 1\n if len(A) > 2:\n A[head] = A[-2]\n A[head + 1] = A[-1]\n return head + 2\n else:\n return len(A)\n print\n A\n\n\nA = [1]\nprint\nremoveDup(A)\n","repo_name":"gtxmobile/leetcode","sub_path":"80.删除排序数组中的重复项II.py","file_name":"80.删除排序数组中的重复项II.py","file_ext":"py","file_size_in_byte":329,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"38774864802","text":"import heapq\nimport numpy as np\nimport time\nimport copy\n\n# 0 - hot_food\n# 1 - sandwitch\n# 2 - drink\n# 3 - cash\n\nmu = 30\nnum_of_server = []\ntotal_sim_time = 5400\n\ngroup_size = [1, 2, 3, 4]\ngroup_size_probabilities = [0.5, 0.3, 0.1, 0.1]\n\nrouting = [[0, 2, 3], [1, 2, 3], [2, 3]]\nroutes_probabilities = [0.80, 0.15, 0.05]\n\nact = [[20, 40], [5, 15], [0, 0], [5, 10]]\nst = [[50, 120], [60, 180], [0, 0], [5, 20]]\n\nexpansion_possibilities = [[1, 1, 5, 2], [1, 1, 5, 3], [1, 2, 5, 2], [1, 2, 5, 3], [2, 1, 5, 2], [2, 1, 5, 3], [2, 2, 5, 2], [2, 2, 5, 3]]\n\n\nclass States:\n def __init__(self):\n self.total_num_of_group = 0\n self.num_of_customers_in_system = 0\n self.is_new_group = {}\n self.queue = [[[]], [[]], [[]], []]\n self.num_of_server = num_of_server\n\n for i in range(self.num_of_server[3]):\n self.queue[3].append([])\n\n self.avg_route_delay = [0.0, 0.0, 0.0]\n self.avg_queue_delay = [0.0, 0.0, 0.0, 0.0]\n self.avg_queue_length = [0.0, 0.0, 0.0, 0.0]\n self.total_customer_served_by_this_counter = [0, 0, 0, 0]\n\n self.max_customer_in_the_system = 0\n self.max_route_delay = [0.0, 0.0, 0.0]\n self.max_queue_delay = [0.0, 0.0, 0.0, 0.0]\n self.max_queue_length = [0.0, 0.0, 0.0, 0.0]\n self.total_customer_served_by_this_route = [0, 0, 0]\n\n self.time_last_event = 0\n self.overall_avg_delay = 0\n self.avg_customer_in_system = 0\n\n def update(self, event):\n time_since_last_event = event.event_time - self.time_last_event\n self.time_last_event = event.event_time\n\n for i in range(4):\n num_of_customer_in_queue = 0\n for j in range(len(self.queue[i])):\n num_of_customer_in_queue += len(self.queue[i][j])\n self.max_queue_length[i] = max(self.max_queue_length[i], len(self.queue[i][j]))\n if len(self.queue[i]) > 0:\n self.avg_queue_length[i] += round(num_of_customer_in_queue / len(self.queue[i]), 3) * time_since_last_event\n\n self.avg_customer_in_system += time_since_last_event * self.num_of_customers_in_system\n self.max_customer_in_the_system = max(self.max_customer_in_the_system, self.num_of_customers_in_system)\n\n def finish(self, sim):\n for i in range(4):\n self.max_queue_delay[i] = round(self.max_queue_delay[i] / 60, 3)\n self.avg_queue_length[i] /= sim.now()\n if self.total_customer_served_by_this_counter[i] > 0:\n self.avg_queue_delay[i] = round(self.avg_queue_delay[i] / (60 * self.total_customer_served_by_this_counter[i]), 3)\n\n for i in range(3):\n if self.total_customer_served_by_this_route[i] > 0:\n self.avg_route_delay[i] = round(self.avg_route_delay[i] / (60 * self.total_customer_served_by_this_route[i]), 3)\n self.overall_avg_delay += (routes_probabilities[i] * self.avg_route_delay[i])\n self.max_route_delay[i] = round(self.max_route_delay[i] / 60, 3)\n\n self.avg_customer_in_system = round(self.avg_customer_in_system / sim.now(), 3)\n\n def report(self):\n print(\"Customer served by each counter \", self.total_customer_served_by_this_counter)\n print(\"Average delays in queue: \", self.avg_queue_length)\n print(\"Max delays in the queue: \", self.max_queue_delay)\n\n print(\"Average queue length: \", self.avg_queue_length)\n print(\"Max queue length: \", self.max_queue_length)\n\n print(\"Average delay for each route: \", self.avg_route_delay)\n print(\"Max delay for each route: \", self.max_route_delay)\n\n print(\"Overall delay: \", self.overall_avg_delay)\n print(\"Maximum customer at any time instance: \", self.max_customer_in_the_system)\n print(\"Average customer: \", self.avg_customer_in_system)\n print(\"Total customer served: \", self.total_customer_served_by_this_counter[3])\n print(\"\\n\\n\\n\")\n\n\nclass Event:\n def __init__(self, sim):\n self.eventType = None\n self.sim = sim\n self.event_time = None\n\n def process(self):\n raise Exception('Unimplemented process method for the event!')\n\n def __repr__(self):\n return self.eventType\n\n def __lt__(self, other):\n return True\n\n\nclass StartEvent(Event):\n def __init__(self, event_time, sim):\n super().__init__(sim)\n self.event_time = event_time\n self.eventType = 'START'\n self.sim = sim\n\n def process(self):\n self.sim.states.total_num_of_group += 1\n arrival_time = self.sim.now() + np.random.exponential(mu)\n num_of_people_in_group = np.random.choice(group_size, p=group_size_probabilities)\n\n for i in range(num_of_people_in_group):\n route_index = np.random.choice([0, 1, 2], p=routes_probabilities)\n self.sim.states.total_customer_served_by_this_route[route_index] += 1\n self.sim.schedule_event(ArrivalEvent(arrival_time, self.sim, self.sim.states.total_num_of_group, route_index, 0, 0))\n\n self.sim.schedule_event(ExitEvent(total_sim_time, self.sim))\n\n\nclass ExitEvent(Event):\n def __init__(self, event_time, sim):\n super().__init__(sim)\n self.event_time = event_time\n self.eventType = 'EXIT'\n self.sim = sim\n\n def process(self):\n None\n\n\nclass ArrivalEvent(Event):\n def __init__(self, event_time, sim, group_no, route_idx, counter_idx, queue_idx):\n super().__init__(sim)\n self.event_time = event_time\n self.eventType = 'ARRIVAL'\n self.sim = sim\n self.group_no = group_no\n self.route_idx = route_idx\n self.counter_idx = counter_idx\n self.queue_idx = queue_idx\n\n def process(self):\n current_counter = routing[self.route_idx][self.counter_idx]\n\n if self.counter_idx == 0:\n self.sim.states.num_of_customers_in_system += 1\n\n if self.counter_idx == 0 and self.group_no not in self.sim.states.is_new_group:\n self.sim.states.is_new_group[self.group_no] = True\n self.sim.states.total_num_of_group += 1\n arrival_time = self.sim.now() + np.random.exponential(mu)\n num_of_people_in_group = np.random.choice(group_size, p=group_size_probabilities)\n\n for i in range(num_of_people_in_group):\n route_index = np.random.choice([0, 1, 2], p=routes_probabilities)\n self.sim.states.total_customer_served_by_this_route[route_index] += 1\n self.sim.schedule_event(ArrivalEvent(arrival_time, self.sim, self.sim.states.total_num_of_group, route_index, 0, 0))\n\n if self.sim.states.num_of_server[current_counter] == 0:\n min_queue_length = np.inf\n for i in range(len(self.sim.states.queue[current_counter])):\n if len(self.sim.states.queue[current_counter][i]) < min_queue_length:\n min_queue_length = len(self.sim.states.queue[current_counter][i])\n self.queue_idx = i\n self.sim.states.queue[current_counter][self.queue_idx].append(self)\n else:\n queue_idx = 0\n service_time = 0\n\n if current_counter != 2:\n self.sim.states.num_of_server[current_counter] -= 1\n\n if current_counter != 3:\n service_time = np.random.uniform(st[current_counter][0], st[current_counter][1])\n else:\n for i in routing[self.route_idx]:\n service_time += np.random.uniform(act[i][0], act[i][1])\n queue_idx = self.sim.states.num_of_server[current_counter]\n\n self.sim.states.total_customer_served_by_this_counter[current_counter] += 1\n self.sim.schedule_event(DepartureEvent(self.event_time + service_time, self.sim, self.group_no, self.route_idx, self.counter_idx, queue_idx))\n\n\nclass DepartureEvent(Event):\n def __init__(self, event_time, sim, group_no, route_idx, counter_idx, queue_idx):\n super().__init__(sim)\n self.event_time = event_time\n self.eventType = 'DEPARTURE'\n self.sim = sim\n self.group_no = group_no\n self.route_idx = route_idx\n self.counter_idx = counter_idx\n self.queue_idx = queue_idx\n\n def process(self):\n current_counter = routing[self.route_idx][self.counter_idx]\n\n if len(self.sim.states.queue[current_counter][self.queue_idx]) == 0:\n self.sim.states.num_of_server[current_counter] += 1\n else:\n service_time = 0\n\n if current_counter != 3:\n service_time = np.random.uniform(st[current_counter][0], st[current_counter][1])\n else:\n for i in routing[self.route_idx]:\n service_time += np.random.uniform(act[i][0], act[i][1])\n\n front_event = self.sim.states.queue[current_counter][self.queue_idx].pop(0)\n delay = self.event_time - front_event.event_time\n\n self.sim.states.avg_route_delay[front_event.route_idx] += delay\n self.sim.states.max_route_delay[front_event.route_idx] = max(self.sim.states.max_route_delay[front_event.route_idx], delay)\n\n if current_counter != 2:\n self.sim.states.avg_queue_delay[current_counter] += delay\n self.sim.states.max_queue_delay[current_counter] = max(self.sim.states.max_queue_delay[current_counter], delay)\n\n self.sim.states.total_customer_served_by_this_counter[current_counter] += 1\n self.sim.schedule_event(DepartureEvent(self.event_time + service_time, self.sim, front_event.group_no, front_event.route_idx, front_event.counter_idx, front_event.queue_idx))\n\n if self.counter_idx < len(routing[self.route_idx]) - 1:\n self.sim.schedule_event(ArrivalEvent(self.event_time, self.sim, self.group_no, self.route_idx, self.counter_idx + 1, 0))\n else:\n self.sim.states.num_of_customers_in_system -= 1\n\n\nclass Simulator:\n def __init__(self):\n self.eventQ = []\n self.simulator_clock = 0\n self.states = States()\n\n def initialize(self):\n self.simulator_clock = 0\n self.schedule_event(StartEvent(0, self))\n\n def now(self):\n return self.simulator_clock\n\n def schedule_event(self, event):\n heapq.heappush(self.eventQ, (event.event_time, event))\n\n def run(self):\n self.initialize()\n while len(self.eventQ) > 0:\n current_time, event = heapq.heappop(self.eventQ)\n if event.eventType == 'EXIT':\n break\n if self.states is not None:\n self.states.update(event)\n self.simulator_clock = event.event_time\n event.process()\n self.states.finish(self)\n self.states.report()\n\n\ndef init(idx):\n global num_of_server, st, act\n num_of_server = expansion_possibilities[idx]\n temp_st = copy.deepcopy(st)\n temp_act = copy.deepcopy(act)\n\n for i in range(2):\n for j in range(2):\n st[i][j] = st[i][j] / num_of_server[i]\n act[i][j] = act[i][j] / num_of_server[i]\n\n st = copy.deepcopy(temp_st)\n act = copy.deepcopy(temp_act)\n\n\nif __name__ == \"__main__\":\n for i in range(8):\n print(\"---------------RUN---------------\", i)\n print(\"Expansion Posibilitis: \", expansion_possibilities[i])\n init(i)\n seed = 101\n np.random.seed(seed)\n sim = Simulator()\n sim.run()\n","repo_name":"return-100/Simulation-and-Modeling","sub_path":"Offline_2-Cafeteria Simulation.py","file_name":"Offline_2-Cafeteria Simulation.py","file_ext":"py","file_size_in_byte":11452,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"22108501830","text":"from django.db import models\nimport uuid\nfrom account.models import User\n\n\nstateList = (\n (\"\", \"Select...\"),(\"Andhra Pradesh\", \"Andhra Pradesh\"), (\"Arunachal Pradesh\", \"Arunachal Pradesh\"), (\"Assam\", \"Assam\"),\n (\"Bihar\", \"Bihar\"), (\"Chhattisgarh\", \"Chhattisgarh\"), (\"Delhi\", \"Delhi\"), (\"Goa\", \"Goa\"), \n (\"Gujarat\", \"Gujarat\"), (\"Haryana\", \"Haryana\"), (\"Himachal Pradesh\", \"Himachal Pradesh\"),\n (\"Jharkhand\", \"Jharkhand\"), (\"Karnataka\", \"Karnataka\"), (\"Kerala\", \"Kerala\"),\n (\"Madhya Pradesh\", \"Madhya Pradesh\"), (\"Maharashtra\", \"Maharashtra\"), (\"Manipur\", \"Manipur\"),\n (\"Meghalaya\", \"Meghalaya\"), (\"Mizoram\", \"Mizoram\"), (\"Nagaland\", \"Nagaland\"), (\"Odisha\", \"Odisha\"),\n (\"Punjab\", \"Punjab\"), (\"Rajasthan\", \"Rajasthan\"), (\"Sikkim\", \"Sikkim\"), (\"Tamil Nadu\", \"Tamil Nadu\"),\n (\"Telangana\", \"Telangana\"), (\"Tripura\", \"Tripura\"), (\"Uttar Pradesh\", \"Uttar Pradesh\"),\n (\"Uttarakhand\", \"Uttarakhand\"), (\"West Bengal\", \"West Bengal\"),\n )\n\n#Election Model\nclass Election(models.Model):\n election_uid = models.CharField(max_length=36, default=uuid.uuid4)\n election_state = models.CharField(max_length=100, verbose_name=\"Election State\", choices=stateList)\n election_name = models.CharField(max_length=150, verbose_name=\"Election Name\")\n start_date = models.DateField(verbose_name=\"Start Date\")\n end_date = models.DateField(verbose_name=\"End Date\")\n is_active = models.BooleanField(default=True)\n\n def __str__(self):\n return self.election_state + \" - \" + self.election_name\n\n#Candidate Model\nclass Candidate(models.Model):\n candidate_uid = models.CharField(max_length=36, default=uuid.uuid4)\n cand_election = models.ForeignKey(Election, on_delete = models.CASCADE, related_name='cand_election')\n cand_name = models.CharField(max_length=150, verbose_name=\"Candidate Name\")\n cand_email = models.EmailField(unique=True, verbose_name=\"Candidate Email\")\n cand_state = models.CharField(max_length=100, verbose_name=\"Candidate State\", choices=stateList)\n cand_const = models.CharField(max_length=200, verbose_name=\"Candidate Constituency\")\n cand_party = models.CharField(max_length=150, verbose_name=\"Party Name\")\n cand_about = models.TextField(verbose_name=\"About\")\n total_votes = models.IntegerField(default=0)\n is_active = models.BooleanField(default=True)\n\n#Result Model\nclass Result(models.Model):\n voter = models.ForeignKey(User, on_delete = models.CASCADE, related_name='voter_result')\n candidate = models.ForeignKey(Candidate, on_delete = models.CASCADE, related_name='candidate_result')\n election = models.ForeignKey(Election, on_delete = models.CASCADE, related_name='election_result')\n is_voted = models.BooleanField(default=False)\n","repo_name":"ianuragg/online-voting-system","sub_path":"core/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2759,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"}
+{"seq_id":"4348758674","text":"import re\r\n\r\nfile1 = open('input.txt', 'r')\r\nLines = file1.readlines()\r\n\r\nstacks = [[],[],[],[],[],[],[],[],[]]\r\n\r\nfor line in Lines:\r\n string = []\r\n for sub in line:\r\n string.append(sub.replace(\"\\n\", \"\"))\r\n if Lines.index(line) < 8:\r\n temp = 0\r\n for x in range(1, 37, 4):\r\n temp += 1\r\n if string[x] != ' ':\r\n stacks[temp-1].append(string[x])\r\n if string[0] == 'm':\r\n instructions = re.findall(r'\\d+', line)\r\n #print( '\\n' , stacks)\r\n startpos = (int(instructions[1]))-1\r\n endpos = (int(instructions[2]))-1\r\n numCrates = (int(instructions[0]))\r\n \r\n temp2 = stacks[startpos][:numCrates]\r\n del stacks[startpos][:numCrates]\r\n #print(temp2)\r\n temp2.reverse()\r\n for crates in temp2:\r\n stacks[endpos].insert(0, crates)\r\n #print(stacks , '\\n' )\r\n #print(stacks)\r\n #break\r\n \r\nprint(stacks)\r\nprint(stacks[0][0], stacks[1][0], stacks[2][0], stacks[3][0], stacks[4][0], stacks[5][0], stacks[6][0], stacks[7][0], stacks[8][0], )","repo_name":"Zubinatub/AOC2022","sub_path":"advent of code/day5/aoc5p2.py","file_name":"aoc5p2.py","file_ext":"py","file_size_in_byte":1098,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"21870073593","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\n In this example shows a QCalendarWidget widget\n\n author: madison\n date: 2020.9.3\n\n\"\"\"\n\nimport sys\nfrom PyQt5.QtWidgets import QWidget, QCalendarWidget, QLabel, QApplication, QVBoxLayout\nfrom PyQt5.QtCore import QDate\n\n\nclass Example(QWidget):\n def __init__(self):\n super().__init__()\n\n self.initUI()\n\n def initUI(self):\n\n vbox = QVBoxLayout(self)\n\n cal = QCalendarWidget(self) # 创建一个QCalendarWidget\n cal.setGridVisible(True)\n cal.clicked[QDate].connect(self.showDate) # 选择一个日期时，QDate的点击信号就触发了，把这个信号和我们自定义的showDate()方法关联起来\n\n vbox.addWidget(cal)\n\n self.lbl = QLabel(self)\n date = cal.selectedDate() # 使用selectedDate()方法获取选中的日期\n self.lbl.setText(date.toString()) # 把日期对象转换成字符串，在标签里面显示出来\n\n vbox.addWidget(self.lbl)\n self.setLayout(vbox)\n\n self.setGeometry(300, 300, 350, 300)\n self.setWindowTitle('Calendar')\n self.show()\n\n\n def showDate(self, date):\n\n self.lbl.setText(date.toString()) # 把日期对象转换成字符串，在标签里面显示出来\n\n\nif __name__ == '__main__':\n\n app = QApplication(sys.argv)\n ex = Example()\n sys.exit(app.exec_())\n\n","repo_name":"madisonma/pyqt5","sub_path":"test/日历.py","file_name":"日历.py","file_ext":"py","file_size_in_byte":1382,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"22978119646","text":"import pandas as pd\r\nimport matplotlib.pyplot as plt\r\nfrom PyQt5 import QtCore, QtGui\r\nfrom PyQt5.QtWidgets import (QApplication, QLabel, QLineEdit, QAction, QPushButton, QTableWidgetItem, QComboBox,\r\n QTableWidget, QMainWindow, QTabWidget, QWidget, QFileDialog, QListWidget, QGroupBox)\r\n\r\nfrom PyQt5 import QtWidgets\r\nimport seaborn as sns\r\nfrom matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\r\nimport numpy as np\r\nfrom sklearn.linear_model import LinearRegression\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\r\nfrom sklearn.neighbors import KNeighborsClassifier\r\nfrom sklearn.metrics import (roc_curve, auc, accuracy_score, precision_score,recall_score, f1_score, adjusted_rand_score,\r\n adjusted_mutual_info_score, fowlkes_mallows_score, homogeneity_score, v_measure_score,\r\n completeness_score, silhouette_score)\r\n\r\nfrom scipy.cluster.hierarchy import dendrogram, linkage\r\nfrom sklearn.cluster import KMeans\r\n\r\ndf = pd.read_csv('indian_liver_patient.csv')\r\ndf = df.dropna()\r\ndf['Gender'] = df['Gender'].map({'Male': 2, 'Female': 1})\r\ndf['Dataset'] = df['Dataset'].map({1: 1, 2: 0})\r\n\r\ndf1 = pd.read_csv('column_2C_weka.csv')\r\ndf1 = pd.merge(df1[df1['class'] == 'Normal'].sample(n=15), df1[df1['class'] == 'Abnormal'].sample(n=15), how='outer')\r\n\r\nclass MplCanvas(FigureCanvas):\r\n\r\n def __init__(self, parent=None, width=5, height=4, dpi=100):\r\n fig = plt.figure(figsize=(width, height), dpi=dpi)\r\n self.axes = fig.add_subplot()\r\n super(MplCanvas, self).__init__(fig)\r\n\r\nclass AddColumnWindow(QWidget):\r\n def __init__(self):\r\n super(AddColumnWindow, self).__init__()\r\n self.setWindowTitle('AddColumnWindow')\r\n\r\nclass MainWindow(QMainWindow):\r\n\r\n label = []\r\n\r\n def __init__(self):\r\n QMainWindow.__init__(self)\r\n\r\n # Название приложения\r\n self.setWindowTitle(\"Приложение\")\r\n\r\n self._createMenuBar()\r\n\r\n self._createTabs()\r\n\r\n def _createMenuBar(self):\r\n\r\n self.openAction = QAction(\"&Открыть...\", self)\r\n self.saveAction = QAction(\"&Сохранить\", self)\r\n self.helpContentAction = QAction(\"&Инструкция\", self)\r\n self.aboutAction = QAction(\"&About\", self)\r\n\r\n menuBar = self.menuBar()\r\n\r\n fileMenu = menuBar.addMenu(\"&Файл\")\r\n fileMenu.addAction(self.openAction)\r\n fileMenu.addAction(self.saveAction)\r\n\r\n helpMenu = menuBar.addMenu(\"&Помощь\")\r\n helpMenu.addAction(self.helpContentAction)\r\n helpMenu.addAction(self.aboutAction)\r\n\r\n self.openAction.triggered.connect(self.openFile)\r\n self.saveAction.triggered.connect(self.saveFile)\r\n\r\n def _createTabs(self):\r\n # Создание объектов для виджета с вкладками\r\n self.tabs = QTabWidget(self) # Основной виджет для вкладок\r\n self.tabs.setGeometry(QtCore.QRect(5, 30, 815, 915)) # Расположение и разме��\r\n self._createTabTable()\r\n self._createTabAnalysis()\r\n self._createTabRegressions()\r\n self._createTabClassification()\r\n self._createTabClusterization()\r\n\r\n def _createTabTable(self):\r\n self.tab_table = QWidget(self) # Вкладка для радектора таблицы\r\n self.tabs.addTab(self.tab_table, 'Таблица')\r\n self.tab_table.setStyleSheet('QWidget {background-color: #D0E1F9}')\r\n\r\n # Добавление таблицы\r\n self.table = QTableWidget(self.tab_table) # Создание обьекта таблицы\r\n self.table.setStyleSheet(\"background-color:#ffffff\") # Цвет таблицы\r\n self.table.setColumnCount(6) # Количество столбцов\r\n self.table.setRowCount(16) # Количество строк\r\n self.table.setGeometry(QtCore.QRect(5, 140, 800, 730)) # Расположение и размеры таблицы\r\n\r\n self.label_size = QLabel(self.tab_table)\r\n self.label_size.setGeometry(QtCore.QRect(5, 110, 300, 30))\r\n self.label_size.setStyleSheet('QLabel {color: #2D4262;}')\r\n self.update_label_size()\r\n\r\n self.input_table = QLineEdit(self.tab_table)\r\n self.input_table.setGeometry(QtCore.QRect(320, 85, 215, 30))\r\n self.input_table.setStyleSheet('QLineEdit {background-color: #ffffff; color: #2D4262;}')\r\n\r\n self.label_table = QLabel('Изменение данных', self.tab_table)\r\n self.label_table.setGeometry(QtCore.QRect(5, 0, 260, 30))\r\n self.label_table.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n self.label_table_2 = QLabel('Изменить название', self.tab_table)\r\n self.label_table_2.setGeometry(QtCore.QRect(320, 0, 260, 30))\r\n self.label_table_2.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n self.label_table_3 = QLabel('Работа с таблицей', self.tab_table)\r\n self.label_table_3.setGeometry(QtCore.QRect(655, 0, 260, 30))\r\n self.label_table_3.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n self.label_table_4 = QLabel('Введите новое название', self.tab_table)\r\n self.label_table_4.setGeometry(QtCore.QRect(320, 55, 270, 30))\r\n self.label_table_4.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n button_upload_file = QPushButton('Очистить данные', self.tab_table)\r\n button_upload_file.setGeometry(QtCore.QRect(655, 30, 150, 25))\r\n button_upload_file.setStyleSheet('QPushButton {background-color: #2D4262; color: #ffffff;}')\r\n\r\n self.combo_box_table = QComboBox(self.tab_table)\r\n self.combo_box_table.setGeometry(QtCore.QRect(320, 30, 325, 30))\r\n self.combo_box_table.setStyleSheet('QLineEdit {background-color: #ffffff; color: #2D4262;}')\r\n\r\n button_clear_nan = QPushButton('Убрать пропуски', self.tab_table)\r\n button_clear_nan.setGeometry(QtCore.QRect(655, 60, 150, 25))\r\n button_clear_nan.setStyleSheet('QPushButton {background-color: #2D4262; color: #ffffff;}')\r\n\r\n button_add_row = QPushButton('Добавить строку', self.tab_table)\r\n button_add_row.setGeometry(QtCore.QRect(5, 30, 150, 40))\r\n button_add_row.setStyleSheet('QPushButton {background-color: #2D4262; color: #ffffff;}')\r\n\r\n button_add_column = QPushButton('Добавить столбец', self.tab_table)\r\n button_add_column.setGeometry(QtCore.QRect(5, 75, 150, 40))\r\n button_add_column.setStyleSheet('QPushButton {background-color: #2D4262; color: #ffffff;}')\r\n\r\n button_delete_row = QPushButton('Удалить строку', self.tab_table)\r\n button_delete_row.setGeometry(QtCore.QRect(160, 30, 150, 40))\r\n button_delete_row.setStyleSheet('QPushButton {background-color: #2D4262; color: #ffffff;}')\r\n\r\n button_delete_column = QPushButton('Удалить столбец', self.tab_table)\r\n button_delete_column.setGeometry(QtCore.QRect(160, 75, 150, 40))\r\n button_delete_column.setStyleSheet('QPushButton {background-color: #2D4262; color: #ffffff;}')\r\n\r\n button_change_column = QPushButton('Изменить', self.tab_table)\r\n button_change_column.setGeometry(QtCore.QRect(535, 85, 110, 30))\r\n button_change_column.setStyleSheet('QPushButton {background-color: #2D4262; color: #ffffff;}')\r\n\r\n button_change_column = QPushButton('Сохранить данные', self.tab_table)\r\n button_change_column.setGeometry(QtCore.QRect(655, 90, 150, 25))\r\n button_change_column.setStyleSheet('QPushButton {background-color: #2D4262; color: #ffffff;}')\r\n\r\n button_upload_file.clicked.connect(self.on_click_clear_table)\r\n button_add_column.clicked.connect(self.on_click_add_column)\r\n button_add_row.clicked.connect(self.on_click_add_row)\r\n button_clear_nan.clicked.connect(self.on_click_clear_nan)\r\n button_delete_row.clicked.connect(self.on_click_delete_row)\r\n button_delete_column.clicked.connect(self.on_click_delete_column)\r\n\r\n def _createTabAnalysis(self):\r\n tab_analysis = QWidget(self) # Вкладка для радектора таблицы\r\n self.tabs.addTab(tab_analysis, 'Анализ данных')\r\n\r\n self.list_analysis = QListWidget(tab_analysis)\r\n self.list_analysis.setSelectionBehavior(QtWidgets.QAbstractItemView.SelectRows)\r\n self.list_analysis.setSelectionMode(QtWidgets.QAbstractItemView.MultiSelection)\r\n self.list_analysis.setGeometry(QtCore.QRect(5, 35, 260, 80))\r\n self.list_analysis.setStyleSheet('QListWidget {background-color: #ffffff; color: #2D4262;}')\r\n\r\n self.combo_box_analysis = QComboBox(tab_analysis)\r\n self.combo_box_analysis.setGeometry(QtCore.QRect(275, 35, 255, 50))\r\n self.combo_box_analysis.setStyleSheet('QLineEdit {background-color: #ffffff; color: #2D4262;}')\r\n\r\n self.input_title_analysis = QLineEdit(tab_analysis)\r\n self.input_title_analysis.setGeometry(QtCore.QRect(540, 35, 210, 50))\r\n self.input_title_analysis.setStyleSheet('QLineEdit {background-color: #ffffff; color: #2D4262;}')\r\n\r\n self.label_analysis = QLabel('Введите название графика', tab_analysis)\r\n self.label_analysis.setGeometry(QtCore.QRect(540, 5, 260, 30))\r\n self.label_analysis.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n button_corr_analysis = QPushButton('Корреляционный анализ', tab_analysis)\r\n button_corr_analysis.setGeometry(QtCore.QRect(540, 95, 265, 50))\r\n button_corr_analysis.setStyleSheet('QPushButton {background-color: #2D4262; color: #ffffff;}')\r\n\r\n button_composite_graph = QPushButton('Построить графики', tab_analysis)\r\n button_composite_graph.setGeometry(QtCore.QRect(275, 95, 255, 50))\r\n button_composite_graph.setStyleSheet('QPushButton {background-color: #2D4262; color: #ffffff;}')\r\n\r\n button_analysis_clear = QPushButton('Очистить', tab_analysis)\r\n button_analysis_clear.setGeometry(QtCore.QRect(5, 120, 128, 25))\r\n button_analysis_clear.setStyleSheet('QPushButton {background-color: #2D4262; color: #ffffff;}')\r\n\r\n button_select_all = QPushButton('Выбрать все', tab_analysis)\r\n button_select_all.setGeometry(QtCore.QRect(135, 120, 128, 25))\r\n button_select_all.setStyleSheet('QPushButton {background-color: #2D4262; color: #ffffff;}')\r\n\r\n self.label_analysis = QLabel('Выберите столбцы', tab_analysis)\r\n self.label_analysis.setGeometry(QtCore.QRect(5, 5, 250, 30))\r\n self.label_analysis.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n self.label_combo_box_analysis = QLabel('Выберите класс', tab_analysis)\r\n self.label_combo_box_analysis.setGeometry(QtCore.QRect(275, 5, 250, 30))\r\n self.label_combo_box_analysis.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n button_analysis_save = QPushButton(tab_analysis)\r\n button_analysis_save.setIcon(QtGui.QIcon('save.jpg'))\r\n button_analysis_save.setGeometry(QtCore.QRect(755, 35, 50, 50))\r\n button_analysis_save.setStyleSheet('QPushButton {background-color: #2D4262; color: #ffffff;}')\r\n\r\n # canvas = FigureCanvas()\r\n self.layout_analysis = QtWidgets.QVBoxLayout()\r\n # self.layout_analysis.addWidget(canvas)\r\n\r\n #############################################\r\n fig_analysis = sns.pairplot(df1[['pelvic_radius', 'lumbar_lordosis_angle', 'pelvic_incidence', 'class']],\r\n hue='class',\r\n markers=[\"^\", \"s\"],\r\n diag_kind=\"kde\"\r\n ).map_lower(sns.kdeplot, levels=4, color=\".2\").fig\r\n plt.grid()\r\n canvas_analysis = FigureCanvas(fig_analysis)\r\n self.layout_analysis.addWidget(canvas_analysis)\r\n ###############################################\r\n\r\n widget = QtWidgets.QWidget(tab_analysis)\r\n widget.setLayout(self.layout_analysis)\r\n widget.setGeometry(-7, 145, 825, 740)\r\n\r\n button_composite_graph.clicked.connect(self.on_click_composite_graph)\r\n button_corr_analysis.clicked.connect(self.on_click_corr_analysis)\r\n button_analysis_save.clicked.connect(self.on_click_save_analysis)\r\n button_analysis_clear.clicked.connect(self.on_click_analysis_clear)\r\n button_select_all.clicked.connect(self.on_click_button_select_all)\r\n\r\n def _createTabRegressions(self):\r\n tab_regressions = QWidget(self) # Вкладка для радектора таблицы\r\n self.tabs.addTab(tab_regressions, 'Регрессия')\r\n\r\n self.combo_box_regressions = QComboBox(tab_regressions)\r\n self.combo_box_regressions.setGeometry(QtCore.QRect(650, 35, 155, 30))\r\n self.combo_box_regressions.setStyleSheet('QLineEdit {background-color: #ffffff; color: #2D4262;}')\r\n self.combo_box_regressions.addItems(['Линейная', 'Полименальная'])\r\n\r\n self.label_regression = QLabel('Выберите вид', tab_regressions)\r\n self.label_regression.setGeometry(QtCore.QRect(650, 5, 300, 30))\r\n self.label_regression.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n button_building_regression = QPushButton('Построить\\nмодель', tab_regressions)\r\n button_building_regression.setGeometry(QtCore.QRect(650, 70, 90, 60))\r\n button_building_regression.setStyleSheet('QPushButton {background-color: #2D4262; color: #ffffff;}')\r\n\r\n self.list_regression = QListWidget(tab_regressions)\r\n self.list_regression.setGeometry(QtCore.QRect(5, 30, 255, 100))\r\n self.list_regression.setStyleSheet('QListWidget {background-color: #ffffff; color: #2D4262;}')\r\n\r\n self.label_regression = QLabel('Выберите X и Y', tab_regressions)\r\n self.label_regression.setGeometry(QtCore.QRect(5, 5, 300, 30))\r\n self.label_regression.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n button_add_x = QPushButton('Добавить X', tab_regressions)\r\n button_add_x.setGeometry(QtCore.QRect(265, 35, 120, 30))\r\n button_add_x.setStyleSheet('QPushButton {background-color: #2D4262; color: #ffffff;}')\r\n\r\n button_add_y = QPushButton('Добавить Y', tab_regressions)\r\n button_add_y.setGeometry(QtCore.QRect(265, 70, 120, 30))\r\n button_add_y.setStyleSheet('QPushButton {background-color: #2D4262; color: #ffffff;}')\r\n\r\n self.list_regression_X = QListWidget(tab_regressions)\r\n self.list_regression_X.setGeometry(QtCore.QRect(390, 35, 255, 55))\r\n self.list_regression_X.setStyleSheet('QListWidget {background-color: #ffffff; color: #2D4262;}')\r\n\r\n self.list_regression_Y = QListWidget(tab_regressions)\r\n self.list_regression_Y.setGeometry(QtCore.QRect(390, 100, 255, 30))\r\n self.list_regression_Y.setStyleSheet('QListWidget {background-color: #ffffff; color: #2D4262;}')\r\n\r\n ################\r\n self.list_regression_X.addItems(['Total_Bilirubin', 'Alkaline_Phosphotase'])\r\n\r\n self.list_regression_Y.addItems(['Direct_Bilirubin'])\r\n ######################\r\n\r\n button_clear_regression = QPushButton('Очистить', tab_regressions)\r\n button_clear_regression.setGeometry(QtCore.QRect(265, 105, 120, 25))\r\n button_clear_regression.setStyleSheet('QPushButton {background-color: #2D4262; color: #ffffff;}')\r\n\r\n button_analysis_save = QPushButton(tab_regressions)\r\n button_analysis_save.setIcon(QtGui.QIcon('save.jpg'))\r\n button_analysis_save.setGeometry(QtCore.QRect(745, 70, 60, 60))\r\n button_analysis_save.setStyleSheet('QPushButton {background-color: #2D4262; color: #ffffff;}')\r\n\r\n button_building_regression.clicked.connect(self.on_click_building_regression)\r\n button_add_x.clicked.connect(self.on_click_add_x)\r\n button_add_y.clicked.connect(self.on_click_add_y)\r\n button_clear_regression.clicked.connect(self.on_click_clear_regression)\r\n\r\n self.canvas_regressions = MplCanvas(self, width=5, height=4, dpi=100)\r\n layout = QtWidgets.QVBoxLayout()\r\n layout.addWidget(self.canvas_regressions)\r\n\r\n widget = QtWidgets.QWidget(tab_regressions)\r\n widget.setLayout(layout)\r\n widget.setGeometry(-7, 130, 665, 750)\r\n\r\n #############################################\r\n self.canvas_regressions.axes.cla()\r\n sns.scatterplot(x=df['Total_Bilirubin'], y=df['Direct_Bilirubin'], ax=self.canvas_regressions.axes)\r\n X = np.array(df['Total_Bilirubin']).reshape((-1, 1))\r\n y = np.array(df['Direct_Bilirubin'])\r\n\r\n model = LinearRegression().fit(X, y)\r\n\r\n self.canvas_regressions.axes.plot(X, model.predict(X), color='red', linewidth=2)\r\n plt.grid()\r\n self.canvas_regressions.draw()\r\n print(model.predict(np.array([50]).reshape((-1, 1))))\r\n #############################################\r\n\r\n groupbox_regression = QGroupBox('Метрики', tab_regressions)\r\n groupbox_regression.setGeometry(QtCore.QRect(650, 140, 155, 105))\r\n\r\n self.label = QLabel('MAE: ' + str(mean_absolute_error(model.predict(X), y)), groupbox_regression)\r\n self.label.setGeometry(QtCore.QRect(5, 30, 145, 20))\r\n self.label.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n self.label = QLabel('MSE: ' + str(mean_squared_error(model.predict(X), y)), groupbox_regression)\r\n self.label.setGeometry(QtCore.QRect(5, 55, 145, 20))\r\n self.label.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n self.label = QLabel('R²: ' + str(r2_score(model.predict(X), y)), groupbox_regression)\r\n self.label.setGeometry(QtCore.QRect(5, 80, 145, 20))\r\n self.label.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n self.label_regression_2 = QLabel('Введите значение X', tab_regressions)\r\n self.label_regression_2.setGeometry(QtCore.QRect(650, 260, 155, 30))\r\n self.label_regression_2.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n self.input_x_regression = QLineEdit(tab_regressions)\r\n self.input_x_regression.setGeometry(QtCore.QRect(650, 290, 155, 30))\r\n self.input_x_regression.setStyleSheet('QLineEdit {background-color: #ffffff; color: #2D4262;}')\r\n\r\n button_building = QPushButton('Построить', tab_regressions)\r\n button_building.setGeometry(QtCore.QRect(650, 325, 155, 30))\r\n button_building.setStyleSheet('QPushButton {background-color: #2D4262; color: #ffffff;}')\r\n\r\n label_regression_3 = QLabel('Результат модели', tab_regressions)\r\n label_regression_3.setGeometry(QtCore.QRect(650, 365, 155, 30))\r\n label_regression_3.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n self.output_y_regression = QLineEdit(tab_regressions)\r\n self.output_y_regression.setGeometry(QtCore.QRect(650, 395, 155, 30))\r\n self.output_y_regression.setStyleSheet('QLineEdit {background-color: #ffffff; color: #2D4262;}')\r\n\r\n def _createTabClassification(self):\r\n tab_classification = QWidget(self) # Вкладка для радектора таблицы\r\n self.tabs.addTab(tab_classification, 'Классификация')\r\n\r\n self.label_classification = QLabel('Выберите класс', tab_classification)\r\n self.label_classification.setGeometry(QtCore.QRect(275, 5, 300, 30))\r\n self.label_classification.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n self.label_classification_1 = QLabel('Выберите X', tab_classification)\r\n self.label_classification_1.setGeometry(QtCore.QRect(5, 5, 150, 30))\r\n self.label_classification_1.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n self.combo_box_classification = QComboBox(tab_classification)\r\n self.combo_box_classification.setGeometry(QtCore.QRect(275, 95, 255, 35))\r\n self.combo_box_classification.setStyleSheet('QLineEdit {background-color: #ffffff; color: #2D4262;}')\r\n self.combo_box_classification.addItems(['K-ближайших соседей', 'Логическая легрессия',\r\n 'Дерево решений', 'Случайный лес', 'Наивный баесовский'])\r\n\r\n self.combo_box_classification_class = QComboBox(tab_classification)\r\n self.combo_box_classification_class.setGeometry(QtCore.QRect(275, 30, 255, 35))\r\n self.combo_box_classification_class.setStyleSheet('QLineEdit {background-color: #ffffff; color: #2D4262;}')\r\n\r\n self.label_classification = QLabel('Выберите тип', tab_classification)\r\n self.label_classification.setGeometry(QtCore.QRect(275, 65, 255, 30))\r\n self.label_classification.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n button_building_classification = QPushButton('Построить\\nмодель', tab_classification)\r\n button_building_classification.setGeometry(QtCore.QRect(545, 30, 90, 100))\r\n button_building_classification.setStyleSheet('QPushButton {background-color: #2D4262; color: #ffffff;}')\r\n\r\n self.list_classification = QListWidget(tab_classification)\r\n self.list_classification.setGeometry(QtCore.QRect(5, 30, 255, 100))\r\n self.list_classification.setSelectionBehavior(QtWidgets.QAbstractItemView.SelectRows)\r\n self.list_classification.setSelectionMode(QtWidgets.QAbstractItemView.MultiSelection)\r\n self.list_classification.setStyleSheet('QListWidget {background-color: #ffffff; color: #2D4262;}')\r\n\r\n self.canvas_classification = MplCanvas(self, width=5, height=4, dpi=100)\r\n layout = QtWidgets.QVBoxLayout()\r\n layout.addWidget(self.canvas_classification)\r\n\r\n ###############################\r\n collist = ['Age', 'Total_Bilirubin',\r\n 'Alamine_Aminotransferase',\r\n 'Total_Protiens',\r\n 'Albumin_and_Globulin_Ratio']\r\n priznaki = df[collist].values\r\n # что предсказываем\r\n response = df['Dataset']\r\n\r\n x_train, x_test, y_train, y_test = train_test_split(priznaki,\r\n response,\r\n random_state=0,\r\n stratify=response)\r\n\r\n knn = KNeighborsClassifier(n_neighbors=30)\r\n\r\n mod_knn = knn.fit(x_train, y_train).predict_proba(x_test)\r\n fpr_knn, tpr_knn, thresholds_knn = roc_curve(y_test, mod_knn[:, 1])\r\n roc_auc = auc(fpr_knn, tpr_knn)\r\n plt.plot(fpr_knn, tpr_knn, label='%s (AUC = %0.2f)' % ('Метод kNN',roc_auc), color = 'red',linestyle = ':')\r\n\r\n plt.plot([0, 1], [0, 1], 'k--')\r\n plt.legend(loc=0, fontsize=17)\r\n plt.title(\"ROC Curve and AUC\", fontsize=18)\r\n plt.ylabel('True Positive Rate', fontsize=18)\r\n plt.xlabel('False Positive Rate', fontsize=18)\r\n ##############################\r\n\r\n widget = QtWidgets.QWidget(tab_classification)\r\n widget.setLayout(layout)\r\n widget.setGeometry(-7, 130, 665, 750)\r\n\r\n groupbox_classification = QGroupBox('Метрики', tab_classification)\r\n groupbox_classification.setGeometry(QtCore.QRect(650, 140, 155, 130))\r\n\r\n ###############\r\n knn.fit(x_train, y_train)\r\n knn_predictions = knn.predict(x_test)\r\n #################\r\n\r\n self.label = QLabel('Accuracy: ' + str(accuracy_score(y_test, knn_predictions))[:5], groupbox_classification)\r\n self.label.setGeometry(QtCore.QRect(5, 30, 145, 20))\r\n self.label.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n self.label = QLabel('Precision: ' + str(precision_score(y_test, knn_predictions))[:5], groupbox_classification)\r\n self.label.setGeometry(QtCore.QRect(5, 55, 145, 20))\r\n self.label.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n self.label = QLabel('Recall: ' + str(recall_score(y_test, knn_predictions))[:5], groupbox_classification)\r\n self.label.setGeometry(QtCore.QRect(5, 80, 145, 20))\r\n self.label.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n self.label = QLabel('F1 score: ' + str(f1_score(y_test, knn_predictions))[:5], groupbox_classification)\r\n self.label.setGeometry(QtCore.QRect(5, 105, 145, 20))\r\n self.label.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n self.label_classification_2 = QLabel('Введите значение X', tab_classification)\r\n self.label_classification_2.setGeometry(QtCore.QRect(650, 5, 155, 30))\r\n self.label_classification_2.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n self.input_x_classification = QLineEdit(tab_classification)\r\n self.input_x_classification.setGeometry(QtCore.QRect(650, 30, 155, 30))\r\n self.input_x_classification.setStyleSheet('QLineEdit {background-color: #ffffff; color: #2D4262;}')\r\n\r\n button_building = QPushButton('Построить', tab_classification)\r\n button_building.setGeometry(QtCore.QRect(650, 65, 155, 30))\r\n button_building.setStyleSheet('QPushButton {background-color: #2D4262; color: #ffffff;}')\r\n\r\n self.output_y_classification = QLineEdit(tab_classification)\r\n self.output_y_classification.setGeometry(QtCore.QRect(650, 100, 155, 30))\r\n self.output_y_classification.setStyleSheet('QLineEdit {background-color: #ffffff; color: #2D4262;}')\r\n\r\n def _createTabClusterization(self):\r\n tab_clusterization = QWidget(self) # Вкладка для радектора таблицы\r\n self.tabs.addTab(tab_clusterization, 'Кластеризация')\r\n\r\n self.label_clusterization = QLabel('Выберите класс', tab_clusterization)\r\n self.label_clusterization.setGeometry(QtCore.QRect(275, 5, 300, 30))\r\n self.label_clusterization.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n self.label_clusterization_1 = QLabel('Выберите X', tab_clusterization)\r\n self.label_clusterization_1.setGeometry(QtCore.QRect(5, 5, 150, 30))\r\n self.label_clusterization_1.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n self.combo_box_clusterization = QComboBox(tab_clusterization)\r\n self.combo_box_clusterization.setGeometry(QtCore.QRect(275, 95, 255, 35))\r\n self.combo_box_clusterization.setStyleSheet('QLineEdit {background-color: #ffffff; color: #2D4262;}')\r\n self.combo_box_clusterization.addItems(['K-средних', 'Распростроения близости',\r\n 'Спектральная кластеризация', 'Алгомеративная кластеризация'])\r\n\r\n self.combo_box_clusterization_class = QComboBox(tab_clusterization)\r\n self.combo_box_clusterization_class.setGeometry(QtCore.QRect(275, 30, 255, 35))\r\n self.combo_box_clusterization_class.setStyleSheet('QLineEdit {background-color: #ffffff; color: #2D4262;}')\r\n\r\n self.label_clusterization = QLabel('Выберите тип', tab_clusterization)\r\n self.label_clusterization.setGeometry(QtCore.QRect(275, 65, 255, 30))\r\n self.label_clusterization.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n button_building_clusterization = QPushButton('Построить\\nмодель', tab_clusterization)\r\n button_building_clusterization.setGeometry(QtCore.QRect(545, 30, 90, 100))\r\n button_building_clusterization.setStyleSheet('QPushButton {background-color: #2D4262; color: #ffffff;}')\r\n\r\n self.list_clusterization = QListWidget(tab_clusterization)\r\n self.list_clusterization.setGeometry(QtCore.QRect(5, 30, 255, 100))\r\n self.list_clusterization.setSelectionBehavior(QtWidgets.QAbstractItemView.SelectRows)\r\n self.list_clusterization.setSelectionMode(QtWidgets.QAbstractItemView.MultiSelection)\r\n self.list_clusterization.setStyleSheet('QListWidget {background-color: #ffffff; color: #2D4262;}')\r\n\r\n self.canvas_clusterization = MplCanvas(self, width=5, height=4, dpi=100)\r\n layout = QtWidgets.QVBoxLayout()\r\n layout.addWidget(self.canvas_clusterization)\r\n\r\n ######################\r\n collist = ['pelvic_incidence', 'lumbar_lordosis_angle',\r\n 'sacral_slope', 'pelvic_radius', 'degree_spondylolisthesis']\r\n x = df1[collist[:2]].values[:20]\r\n algo = KMeans(n_clusters=3, random_state=42).fit(x)\r\n y = df1[\"class\"][:20]\r\n\r\n varieties = list(df1.pop('class'))[:30]\r\n samples = df1[collist[:2]].values[:30]\r\n\r\n mergings = linkage(samples, method='complete')\r\n\r\n plt.title(\"Customer Dendograms\")\r\n dend = dendrogram(mergings,\r\n labels=varieties,\r\n leaf_rotation=90,\r\n leaf_font_size=10,\r\n ax=self.canvas_clusterization.axes\r\n )\r\n\r\n widget = QtWidgets.QWidget(tab_clusterization)\r\n widget.setLayout(layout)\r\n widget.setGeometry(-7, 130, 665, 750)\r\n\r\n groupbox_clusterization = QGroupBox('Метрики', tab_clusterization)\r\n groupbox_clusterization.setGeometry(QtCore.QRect(650, 140, 155, 205))\r\n\r\n self.label = QLabel('ARI: 0.4264' , groupbox_clusterization)\r\n self.label.setGeometry(QtCore.QRect(5, 30, 145, 20))\r\n self.label.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n self.label = QLabel('AMI: ' + str(adjusted_mutual_info_score(y, algo.labels_))[:5], groupbox_clusterization)\r\n self.label.setGeometry(QtCore.QRect(5, 55, 145, 20))\r\n self.label.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n self.label = QLabel('FMI: ' + str(fowlkes_mallows_score(y, algo.labels_))[:5], groupbox_clusterization)\r\n self.label.setGeometry(QtCore.QRect(5, 80, 145, 20))\r\n self.label.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n self.label = QLabel('Homogeneity: ' + str(homogeneity_score(y, algo.labels_))[:4], groupbox_clusterization)\r\n self.label.setGeometry(QtCore.QRect(5, 105, 145, 20))\r\n self.label.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n self.label = QLabel('Completeness: ' + str(completeness_score(y, algo.labels_))[:4], groupbox_clusterization)\r\n self.label.setGeometry(QtCore.QRect(5, 130, 145, 20))\r\n self.label.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n self.label = QLabel('V-measure: ' + str(v_measure_score(y, algo.labels_))[0:5], groupbox_clusterization)\r\n self.label.setGeometry(QtCore.QRect(5, 155, 145, 20))\r\n self.label.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n self.label = QLabel('Silhouette: ' + str(silhouette_score(x, algo.labels_))[0:5], groupbox_clusterization)\r\n self.label.setGeometry(QtCore.QRect(5, 180, 145, 20))\r\n self.label.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n self.label_clusterization_2 = QLabel('Введите значение X', tab_clusterization)\r\n self.label_clusterization_2.setGeometry(QtCore.QRect(650, 5, 155, 30))\r\n self.label_clusterization_2.setStyleSheet('QLabel {color: #2D4262;}')\r\n\r\n self.input_x_clusterization = QLineEdit(tab_clusterization)\r\n self.input_x_clusterization.setGeometry(QtCore.QRect(650, 30, 155, 30))\r\n self.input_x_clusterization.setStyleSheet('QLineEdit {background-color: #ffffff; color: #2D4262;}')\r\n\r\n button_building = QPushButton('Построить', tab_clusterization)\r\n button_building.setGeometry(QtCore.QRect(650, 65, 155, 30))\r\n button_building.setStyleSheet('QPushButton {background-color: #2D4262; color: #ffffff;}')\r\n\r\n self.output_y_clusterization = QLineEdit(tab_clusterization)\r\n self.output_y_clusterization.setGeometry(QtCore.QRect(650, 100, 155, 30))\r\n self.output_y_clusterization.setStyleSheet('QLineEdit {background-color: #ffffff; color: #2D4262;}')\r\n\r\n def on_click_button_select_all(self):\r\n for item in range(len(self.label)):\r\n self.list_analysis.setCurrentItem(item)\r\n\r\n def on_click_analysis_clear(self):\r\n for i in reversed(range(self.layout_analysis.count())):\r\n self.layout_analysis.itemAt(i).widget().setParent(None)\r\n\r\n canvas = FigureCanvas()\r\n self.layout_analysis.addWidget(canvas)\r\n\r\n self.list_analysis.clear()\r\n self.list_analysis.addItems(self.label[0:])\r\n\r\n def on_click_save_analysis(self):\r\n plt.savefig(self.input_title_analysis.text() + '.png')\r\n\r\n def on_click_corr_analysis(self):\r\n index_col = [self.label.index(self.list_analysis.selectedItems()[i].text())\r\n for i in range(len(self.list_analysis.selectedItems()))\r\n ]\r\n\r\n data = []\r\n for col in index_col:\r\n temp = []\r\n for row in range(self.table.rowCount()):\r\n temp.append(float(self.table.item(row, col).text()))\r\n data.append(temp)\r\n\r\n df = pd.DataFrame()\r\n for i in range(len(data)):\r\n df[self.label[index_col[i]]] = data[i]\r\n\r\n for i in reversed(range(self.layout_analysis.count())):\r\n self.layout_analysis.itemAt(i).widget().setParent(None)\r\n\r\n fig = plt.figure()\r\n ax = fig.add_subplot(sns.heatmap(df.corr(), annot=True))\r\n if len(data) > 3:\r\n plt.xticks(rotation=360)\r\n plt.yticks(rotation=90)\r\n plt.title(self.input_title_analysis.text())\r\n canvas_analysis = FigureCanvas(fig)\r\n self.layout_analysis.addWidget(canvas_analysis)\r\n\r\n def on_click_composite_graph(self):\r\n index_col = [self.label.index(self.list_analysis.selectedItems()[i].text())\r\n for i in range(len(self.list_analysis.selectedItems()))\r\n ]\r\n index_col.append(self.label.index(self.combo_box_analysis.currentText()))\r\n\r\n data = []\r\n for col in index_col:\r\n temp = []\r\n for row in range(self.table.rowCount()):\r\n temp.append(float(self.table.item(row, col).text()))\r\n data.append(temp)\r\n\r\n df = pd.DataFrame()\r\n for i in range(len(data)):\r\n df[self.label[index_col[i]]] = data[i]\r\n\r\n for i in reversed(range(self.layout_analysis.count())):\r\n self.layout_analysis.itemAt(i).widget().setParent(None)\r\n\r\n fig_analysis = sns.pairplot(df, hue=self.combo_box_analysis.currentText()).fig\r\n canvas_analysis = FigureCanvas(fig_analysis)\r\n\r\n self.layout_analysis.addWidget(canvas_analysis)\r\n\r\n def on_click_clear_regression(self):\r\n self.list_regression.clear()\r\n self.list_regression.addItems(self.label[0:])\r\n\r\n self.canvas_regressions.axes.cla()\r\n\r\n self.input_regression_X.clear()\r\n self.input_regression_Y.clear()\r\n\r\n def on_click_add_x(self):\r\n try:\r\n self.list_regression_X.addItems([self.list_regression.selectedItems()[0].text()])\r\n self.list_regression.takeItem(self.list_regression.currentRow())\r\n except:\r\n pass\r\n\r\n def on_click_add_y(self):\r\n try:\r\n self.list_regression_Y.addItems([self.list_regression.selectedItems()[0].text()])\r\n self.list_regression.takeItem(self.list_regression.currentRow())\r\n except:\r\n pass\r\n\r\n def on_click_building_regression(self):\r\n x_index = [self.label.index(i) for i in self.input_regression_X.text().split('; ')]\r\n\r\n X = []\r\n for col in x_index:\r\n for row in range(self.table.rowCount()):\r\n X.append(float(self.table.item(row, col).text()))\r\n\r\n y_index = [self.label.index(i) for i in self.input_regression_Y.text().split('; ')]\r\n\r\n y = []\r\n for col in y_index:\r\n for row in range(self.table.rowCount()):\r\n y.append(float(self.table.item(row, col).text()))\r\n\r\n self.canvas_regressions.axes.cla()\r\n sns.scatterplot(x=X, y=y, ax=self.canvas_regressions.axes)\r\n\r\n X = np.array(X).reshape((-1, 1))\r\n y = np.array(y)\r\n\r\n X_train, X_test, y_train, y_test = train_test_split(\r\n X, y, test_size=0.3, random_state=0)\r\n\r\n model = LinearRegression().fit(X_train, y_train)\r\n\r\n y_train_pred = model.predict(X_train)\r\n y_test_pred = model.predict(X_test)\r\n print('MSE {:.3f}'.format(\r\n mean_squared_error(y_test, y_test_pred)))\r\n print('R^2 test: {:.3f}'.format(\r\n r2_score(y_test, y_test_pred)))\r\n\r\n self.canvas_regressions.axes.plot(X, model.predict(X), color='red', linewidth=2)\r\n self.canvas_regressions.draw()\r\n\r\n # data = []\r\n # for i in range(self.ui.tableWidget.rowCount()):\r\n # data.append(self.ui.tableWidget.item(i, column).text())\r\n\r\n def on_click_delete_row(self):\r\n try:\r\n index = self.table.selectionModel().selectedRows()[0].row()\r\n for _ in range(len(self.table.selectionModel().selectedRows())):\r\n try:\r\n self.table.removeRow(index)\r\n except:\r\n pass\r\n except:\r\n pass\r\n\r\n self.update_label_size()\r\n\r\n def on_click_delete_column(self):\r\n try:\r\n index = self.table.selectionModel().selectedColumns()[0].column()\r\n for _ in range(len(self.table.selectionModel().selectedColumns())):\r\n try:\r\n self.table.removeColumn(index)\r\n self.label.pop(index)\r\n except:\r\n pass\r\n except:\r\n pass\r\n\r\n self.update_label_size()\r\n\r\n def on_click_clear_nan(self):\r\n for row in range(self.table.rowCount()):\r\n for col in range(self.table.columnCount()):\r\n try:\r\n if self.table.item(row, col).text() == \"NaN\":\r\n self.table.removeRow(row)\r\n continue\r\n except:\r\n pass\r\n\r\n self.update_label_size()\r\n\r\n def on_click_add_column(self):\r\n try:\r\n self.table.insertColumn(int(self.input_column.text()) - 1)\r\n except:\r\n pass\r\n\r\n self.input_column.setText(\"\")\r\n self.update_label_size()\r\n\r\n def on_click_add_row(self):\r\n try:\r\n index = self.table.selectionModel().selectedRows()[0].row()\r\n self.table.insertRow(index + 1)\r\n except:\r\n pass\r\n\r\n def update_label_size(self):\r\n size = \"Размер таблицы: \" + str(self.table.columnCount()) + 'x' + str(self.table.rowCount())\r\n self.label_size.setText(size)\r\n\r\n def on_click_clear_table(self):\r\n self.table.setRowCount(16)\r\n self.table.setColumnCount(6)\r\n\r\n for row in range(self.table.rowCount()):\r\n for col in range(self.table.columnCount()):\r\n self.table.setItem(row, col, QTableWidgetItem(\"\"))\r\n\r\n self.label = ['1', '2', '3', '4', '5', '6']\r\n self.table.setColumnCount(len(self.label))\r\n self.table.setHorizontalHeaderLabels(self.label)\r\n\r\n for row in range(self.table.rowCount()):\r\n self.table.setColumnWidth(row, 150)\r\n\r\n self.update_label_size()\r\n\r\n self.list_regression.clear()\r\n\r\n def openFile(self):\r\n self.on_click_clear_table()\r\n\r\n fname = QFileDialog.getOpenFileName(self, 'Open file')[0]\r\n try:\r\n with open(fname) as file:\r\n row_count = 0\r\n for _ in file:\r\n row_count += 1\r\n self.table.setRowCount(row_count - 1)\r\n\r\n with open(fname) as file:\r\n for i, line in enumerate(file):\r\n line = str.replace(line, '\\n', '')\r\n if i == 0:\r\n self.label = line.split(',')\r\n self.table.setColumnCount(len(self.label))\r\n self.table.setHorizontalHeaderLabels(self.label)\r\n else:\r\n for j, cell in enumerate(line.split(',')):\r\n self.table.setItem(i - 1, j, QTableWidgetItem(cell))\r\n\r\n self.table.resizeColumnsToContents() # ширина столцов подогнать по ширине текста\r\n except:\r\n pass\r\n\r\n self.list_regression.addItems(self.label[0:])\r\n self.list_classification.addItems(self.label[0:])\r\n self.list_analysis.addItems(self.label[0:])\r\n self.list_clusterization.addItems(self.label[0:])\r\n self.combo_box_analysis.addItems(self.label[0:])\r\n self.combo_box_table.addItems(self.label[0:])\r\n self.combo_box_classification_class.addItems(self.label[0:])\r\n self.combo_box_clusterization_class.addItems(self.label[0:])\r\n\r\n self.update_label_size()\r\n\r\n def saveFile(self):\r\n data = ','.join(self.label)\r\n for row in range(self.table.rowCount()):\r\n tmp = \"\"\r\n for col in range(self.table.columnCount()):\r\n if tmp != \"\":\r\n try:\r\n tmp = \",\".join((tmp, self.table.item(row, col).text()))\r\n except:\r\n tmp = \",\".join((tmp, 'NaN'))\r\n else:\r\n try:\r\n tmp = self.table.item(row, col).text()\r\n except:\r\n tmp = 'NaN'\r\n data = \"\\n\".join((data, tmp))\r\n\r\n name = QFileDialog.getExistingDirectory(self, \"Выбрать папку\", \".\")\r\n\r\n try:\r\n f = open(name + '/file.csv', 'w')\r\n f.write(data)\r\n except:\r\n pass\r\n\r\n self.list_regression.clear()\r\n\r\n self.update_label_size()\r\n\r\nif __name__ == \"__main__\":\r\n app = QApplication([]) # Создание объекта класса QApplication\r\n app.setStyle('Fusion') #\r\n window = MainWindow() #\r\n window.resize(830, 950) # Изменение размера главного окна приложения\r\n window.setStyleSheet(\"background-color:#D0E1F9\") #\r\n window.show() # Отображение виджета на экране монитора\r\n app.exec_() #","repo_name":"CactysFedya/kurs2","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":43054,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"74733231664","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nAuthor: Mario Königbauer (mkoenigb@gmx.de)\r\n(C) 2022 - today by Mario Koenigbauer\r\nLicense: GNU General Public License v3.0\r\n/***************************************************************************\r\n * *\r\n * This program is free software; you can redistribute it and/or modify *\r\n * it under the terms of the GNU General Public License as published by *\r\n * the Free Software Foundation; either version 3 of the License, or *\r\n * any later version. *\r\n * *\r\n ***************************************************************************/\r\n\"\"\"\r\n\r\nimport processing\r\nimport math\r\nfrom PyQt5.QtCore import QCoreApplication, QVariant, QDateTime\r\nfrom qgis.core import (QgsField, QgsFeature, QgsProcessing, QgsExpression,\r\n QgsFeatureSink, QgsFeatureRequest, QgsProcessingAlgorithm, QgsExpressionContext, QgsExpressionContextUtils, QgsGeometry, QgsPointXY,\r\n QgsProcessingParameterVectorLayer, QgsProcessingParameterFeatureSink, QgsProcessingParameterFeatureSource, QgsProcessingParameterExpression)\r\n\r\nclass InterpolateDateTimeAlongLine(QgsProcessingAlgorithm):\r\n METHOD = 'METHOD'\r\n SOURCE_LYR = 'SOURCE_LYR'\r\n SOURCE_START_TIME_EXPR = 'SOURCE_START_TIME_EXPR'\r\n SOURCE_END_TIME_EXPR = 'SOURCE_END_TIME_EXPR'\r\n SOURCE_INTERPOLATION_DENSITY_EXPR = 'SOURCE_INTERPOLATION_DENSITY_EXPR'\r\n OUTPUT = 'OUTPUT'\r\n\r\n def initAlgorithm(self, config=None):\r\n self.addParameter(\r\n QgsProcessingParameterFeatureSource(\r\n self.SOURCE_LYR, self.tr('Source Layer (must be in metric CRS!)'),[QgsProcessing.TypeVectorLine]))\r\n self.addParameter(\r\n QgsProcessingParameterExpression(\r\n self.SOURCE_START_TIME_EXPR, self.tr('Expression, field or datetime representing start-datetime of features (must be in datetime format!)'), parentLayerParameterName = 'SOURCE_LYR', optional = False))\r\n self.addParameter(\r\n QgsProcessingParameterExpression(\r\n self.SOURCE_END_TIME_EXPR, self.tr('Expression, field or datetime representing end-datetime of features (must be in datetime format!)'), parentLayerParameterName = 'SOURCE_LYR', optional = False))\r\n self.addParameter(\r\n QgsProcessingParameterExpression(\r\n self.SOURCE_INTERPOLATION_DENSITY_EXPR, self.tr('Expression, field or number representing maximum length of segments (integer or double; must be in meters!)'), parentLayerParameterName = 'SOURCE_LYR', optional = False, defaultValue = 'length($geometry) / 10'))\r\n self.addParameter(\r\n QgsProcessingParameterFeatureSink(\r\n self.OUTPUT, self.tr('Interpolated DateTime Along Line')))\r\n\r\n def processAlgorithm(self, parameters, context, feedback):\r\n feedback.setProgressText('Prepare processing...')\r\n source_layer = self.parameterAsSource(parameters, self.SOURCE_LYR, context)\r\n source_layer_vl = self.parameterAsLayer(parameters, self.SOURCE_LYR, context)\r\n source_start_time_expr = self.parameterAsExpression(parameters, self.SOURCE_START_TIME_EXPR, context)\r\n source_start_time_expr = QgsExpression(source_start_time_expr)\r\n source_end_time_expr = self.parameterAsExpression(parameters, self.SOURCE_END_TIME_EXPR, context)\r\n source_end_time_expr = QgsExpression(source_end_time_expr)\r\n source_interpolation_density_expr = self.parameterAsExpression(parameters, self.SOURCE_INTERPOLATION_DENSITY_EXPR, context)\r\n source_interpolation_density_expr = QgsExpression(source_interpolation_density_expr)\r\n \r\n if source_layer_vl.crs().mapUnits() != 0:\r\n #feedback.pushWarning('Layer is not in a metric CRS! Calculations will be incorrect! Reproject your layer to a metric CRS and retry!')\r\n feedback.reportError('Layer is not in a metric CRS! Calculations will be incorrect! Reproject your layer to a metric CRS and retry!', fatalError=True)\r\n \r\n source_layer_fields = source_layer.fields()\r\n output_layer_fields = source_layer_fields\r\n field_name_dict = {\r\n 'line_id_field_name' : 'line_id',\r\n 'part_id_field_name' : 'part_id',\r\n 'segment_id_field_name' : 'segment_id',\r\n 'interpolated_starttime_field_name' : 'interpolated_start_datetime',\r\n 'interpolated_endtime_field_name' : 'interpolated_end_datetime',\r\n 'interpolated_speed_field_name' : 'interpolated_speed_meters_per_second',\r\n 'seconds_from_linestart_field_name' : 'seconds_from_line_start',\r\n 'distance_from_partstart_field_name' : 'distance_meters_from_part_start',\r\n 'distance_from_linestart_field_name' : 'distance_meters_from_line_start',\r\n 'segment_length_field_name' : 'segment_length_meters'\r\n }\r\n whilecounter = 0\r\n while any(elem in field_name_dict.values() for elem in output_layer_fields.names()):\r\n whilecounter += 1\r\n for var,name in field_name_dict.items():\r\n field_name_dict[var] = name + '_2'\r\n if whilecounter > 9:\r\n break\r\n \r\n output_layer_fields.append(QgsField(field_name_dict['line_id_field_name'], QVariant.Int))\r\n output_layer_fields.append(QgsField(field_name_dict['part_id_field_name'], QVariant.Int))\r\n output_layer_fields.append(QgsField(field_name_dict['segment_id_field_name'], QVariant.Int))\r\n output_layer_fields.append(QgsField(field_name_dict['interpolated_starttime_field_name'], QVariant.DateTime))\r\n output_layer_fields.append(QgsField(field_name_dict['interpolated_endtime_field_name'], QVariant.DateTime))\r\n output_layer_fields.append(QgsField(field_name_dict['interpolated_speed_field_name'], QVariant.Double, len = 20, prec = 8))\r\n output_layer_fields.append(QgsField(field_name_dict['seconds_from_linestart_field_name'], QVariant.Double, len = 20, prec = 8))\r\n output_layer_fields.append(QgsField(field_name_dict['distance_from_partstart_field_name'], QVariant.Double, len = 20, prec = 8))\r\n output_layer_fields.append(QgsField(field_name_dict['distance_from_linestart_field_name'], QVariant.Double, len = 20, prec = 8))\r\n output_layer_fields.append(QgsField(field_name_dict['segment_length_field_name'], QVariant.Double, len = 20, prec = 8))\r\n \r\n (sink, dest_id) = self.parameterAsSink(parameters, self.OUTPUT, context,\r\n output_layer_fields, source_layer.wkbType(),\r\n source_layer.sourceCrs())\r\n \r\n total = 100.0 / source_layer_vl.featureCount() if source_layer_vl.featureCount() else 0\r\n \r\n feedback.setProgressText('Start processing...')\r\n source_start_time_expr_context = QgsExpressionContext()\r\n source_start_time_expr_context.appendScopes(QgsExpressionContextUtils.globalProjectLayerScopes(source_layer_vl))\r\n source_end_time_expr_context = QgsExpressionContext()\r\n source_end_time_expr_context.appendScopes(QgsExpressionContextUtils.globalProjectLayerScopes(source_layer_vl))\r\n source_interpolation_density_expr_context = QgsExpressionContext()\r\n source_interpolation_density_expr_context.appendScopes(QgsExpressionContextUtils.globalProjectLayerScopes(source_layer_vl))\r\n for current, source_feat in enumerate(source_layer.getFeatures()):\r\n if feedback.isCanceled():\r\n break\r\n segment_id = 0\r\n part_id = 0\r\n source_geom = source_feat.geometry()\r\n source_length = source_geom.length()\r\n \r\n source_start_time_expr_context.setFeature(source_feat)\r\n source_start_time_expr_result = source_start_time_expr.evaluate(source_start_time_expr_context)\r\n \r\n source_end_time_expr_context.setFeature(source_feat)\r\n source_end_time_expr_result = source_end_time_expr.evaluate(source_end_time_expr_context)\r\n \r\n if not 'QDateTime' in str(type(source_start_time_expr_result)) or not 'QDateTime' in str(type(source_end_time_expr_result)):\r\n feedback.pushWarning('Given start-datetime or end-datetime of Feature ' + str(source_feat.id()) + ' is not in QDateTime format! Skipping feature...')\r\n continue\r\n if not source_start_time_expr_result.isValid() or not source_end_time_expr_result.isValid():\r\n feedback.pushWarning('Feature ' + str(source_feat.id()) + ' does not have a valid QDateTime! Skipping feature...')\r\n continue\r\n \r\n source_interpolation_density_expr_context.setFeature(source_feat)\r\n source_interpolation_density_expr_result = source_interpolation_density_expr.evaluate(source_interpolation_density_expr_context)\r\n \r\n seconds_needed = source_start_time_expr_result.secsTo(source_end_time_expr_result)\r\n speed_m_per_s = source_length / seconds_needed\r\n #speed_km_per_h = speed_m_per_s * 3.6\r\n \r\n for source_part in source_geom.parts():\r\n if feedback.isCanceled():\r\n break\r\n part_id += 1\r\n source_part_geom = QgsGeometry.fromPolyline(source_part)\r\n source_part_length = source_part_geom.length()\r\n nsegments = math.ceil(source_part_length / source_interpolation_density_expr_result)\r\n segment_startdistance = 0\r\n segment_enddistance = source_interpolation_density_expr_result\r\n for segment in range(0,nsegments):\r\n if feedback.isCanceled():\r\n break\r\n segment_id += 1\r\n segment_linestring = source_part.curveSubstring(segment_startdistance,segment_enddistance)\r\n segment_geom = QgsGeometry.fromPolyline(segment_linestring)\r\n \r\n segment_startpoint = QgsGeometry.fromPointXY(QgsPointXY(segment_linestring[0]))\r\n segment_endpoint = QgsGeometry.fromPointXY(QgsPointXY(segment_linestring[-1]))\r\n segment_start_distance_from_line_start = source_geom.lineLocatePoint(segment_startpoint)\r\n segment_end_distance_from_line_start = source_geom.lineLocatePoint(segment_endpoint)\r\n interpolated_starttime = source_start_time_expr_result.addSecs(segment_start_distance_from_line_start / speed_m_per_s)\r\n interpolated_endtime = source_start_time_expr_result.addSecs(segment_end_distance_from_line_start / speed_m_per_s)\r\n\r\n new_feat = QgsFeature(output_layer_fields)\r\n new_feat.setGeometry(segment_geom)\r\n attridx = 0\r\n for attr in source_feat.attributes():\r\n new_feat[attridx] = attr\r\n attridx += 1\r\n new_feat[field_name_dict['line_id_field_name']] = source_feat.id()\r\n new_feat[field_name_dict['part_id_field_name']] = part_id\r\n new_feat[field_name_dict['segment_id_field_name']] = segment_id\r\n new_feat[field_name_dict['interpolated_starttime_field_name']] = interpolated_starttime\r\n new_feat[field_name_dict['interpolated_endtime_field_name']] = interpolated_endtime\r\n new_feat[field_name_dict['interpolated_speed_field_name']] = speed_m_per_s\r\n new_feat[field_name_dict['seconds_from_linestart_field_name']] = segment_start_distance_from_line_start / speed_m_per_s\r\n new_feat[field_name_dict['distance_from_partstart_field_name']] = segment_startdistance\r\n new_feat[field_name_dict['distance_from_linestart_field_name']] = segment_start_distance_from_line_start\r\n new_feat[field_name_dict['segment_length_field_name']] = segment_geom.length()\r\n \r\n segment_startdistance += source_interpolation_density_expr_result\r\n segment_enddistance += source_interpolation_density_expr_result\r\n \r\n sink.addFeature(new_feat, QgsFeatureSink.FastInsert)\r\n \r\n feedback.setProgress(int(current * total))\r\n \r\n\r\n return {self.OUTPUT: dest_id}\r\n\r\n\r\n def tr(self, string):\r\n return QCoreApplication.translate('Processing', string)\r\n\r\n def createInstance(self):\r\n return InterpolateDateTimeAlongLine()\r\n\r\n def name(self):\r\n return 'InterpolateDateTimeAlongLine'\r\n\r\n def displayName(self):\r\n return self.tr('Interpolate DateTime Along Line')\r\n\r\n def group(self):\r\n return self.tr(self.groupId())\r\n\r\n def groupId(self):\r\n return 'Vector - Interpolation'\r\n\r\n def shortHelpString(self):\r\n return self.tr('This Algorithm segmentizes a given linelayer into pieces by a given maximum distance and interpolates the start-datetime and end-datetime on these segments linear. \\n'\r\n 'The layer must be in a metric CRS and needs attributes for start-datetime and end-datetime in QDateTime format where both datetime attributes must be in the same timezone. \\n'\r\n 'You can use to_datetime(), datetime_from_epoch() or make_datetime() expressions to use datetimes stored e.g. as string or unixtime for usage in this algorithm. \\n'\r\n 'This algorithm is designed for animating lines with Temporal Controller. \\n'\r\n )","repo_name":"mkoenigb/ProcessX","sub_path":"algorithms/vector_interpolation/InterpolateDateTimeAlongLine.py","file_name":"InterpolateDateTimeAlongLine.py","file_ext":"py","file_size_in_byte":13926,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"}
+{"seq_id":"15990793457","text":"__author__=\"Daniel Berenguer\"\n__date__ =\"$Oct 4, 2012$\"\n#########################################################################\n\nimport xml.etree.ElementTree as xml\n\n\nclass XmlSettings(object):\n \"\"\"\n Main configuration settings\n \"\"\"\n def read(self):\n \"\"\"\n Read configuration file file\n \"\"\"\n # Parse XML file\n tree = xml.parse(self.file_name)\n if tree is None:\n return\n # Get the root node\n root = tree.getroot()\n # Recording command\n elem = root.find(\"recordcmd\")\n if elem is not None:\n self.record_command = elem.text\n # Playing command\n elem = root.find(\"playcmd\")\n if elem is not None:\n self.play_command = elem.text\n # Speech recognition command\n elem = root.find(\"language\")\n if elem is not None:\n self.language = elem.text\n # Keyword\n elem = root.find(\"keyword\")\n if elem is not None:\n self.keyword = elem.text\n # Server reply\n elem = root.find(\"reply\")\n if elem is not None:\n self.reply = elem.text\n # Welcome message\n elem = root.find(\"welcomemsg\")\n if elem is not None:\n self.welcomemsg = elem.text\n\n \n def save(self):\n \"\"\"\n Save serial port settings in disk\n \"\"\"\n f = open(self.file_name, 'w')\n f.write(\"\\n\")\n f.write(\"\\n\")\n f.write(\"\\t\" + self.record_command + \"\\n\")\n f.write(\"\\t\" + self.play_command + \"\\n\")\n f.write(\"\\t\" + self.language + \"\\n\")\n f.write(\"\\t\" + self.keyword + \"\\n\")\n f.write(\"\\t\" + self.reply + \"\\n\")\n f.write(\"\\t\" + self.welcomemsg + \"\\n\")\n f.write(\"\\n\")\n f.close()\n\n\n def __init__(self, file_name=None):\n \"\"\"\n Class constructor\n \n @param filename: Path to the configuration file\n \"\"\"\n # Name/path of the current configuration file\n if file_name is None:\n file_name = \"settings.xml\"\n\n ## Name/path of the current configuration file\n self.file_name = file_name\n ## Recording command\n self.record_command = \"sox -r 16000 -t alsa default ${audio_file} silence 1 0.1 5% 1 1.5 5%\"\n ## Playing command\n self.play_command = \"mplayer ${audio_file}\"\n ## User language\n self.language = \"en-us\"\n ## Name of the recorded audio file\n self.record_file = \"recording.flac\"\n ## Name of the output audio file\n self.play_file = \"output.mp3\"\n ## Keyword string\n self.keyword = \"lagarto\"\n ## Server reply after receiving keyword\n self.reply = \"Yes Sir\"\n ## Welcome message\n self.welcomemsg = \"Ready for your commands\"\n \n # Read XML file\n self.read()\n","repo_name":"ntruchsess/panstamp","sub_path":"python/lagarto/lagarto-speech/xmltools.py","file_name":"xmltools.py","file_ext":"py","file_size_in_byte":3035,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"91"}
+{"seq_id":"73465286064","text":"import argparse\n\nimport peewee\nimport waitress\n\nfrom sliver.asset import Asset\nfrom sliver.trade_engine import TradeEngine\nfrom sliver.price import Price\nfrom sliver.exchange import Exchange\nfrom sliver.market import Market\nfrom sliver.exchange_asset import ExchangeAsset\nfrom sliver.credential import Credential\nfrom sliver.balance import Balance\nfrom sliver.indicator import Indicator\nfrom sliver.strategy import BaseStrategy\nfrom sliver.user_strategy import UserStrategy\nfrom sliver.user import User\nfrom sliver.order import Order\nfrom sliver.position import Position\n\nfrom sliver.watchdog import Watchdog\nfrom sliver.config import Config\n\nfrom sliver.api.app import api\n\nimport sliver.database as db\n\n__all__ = [\n \"Asset\",\n \"TradeEngine\",\n \"Price\",\n \"Exchange\",\n \"Market\",\n \"ExchangeAsset\",\n \"Credential\",\n \"Balance\",\n \"Indicator\",\n \"BaseStrategy\",\n \"UserStrategy\",\n \"User\",\n \"Order\",\n \"Position\",\n]\n\n\ndef create_tables():\n db.init()\n\n db.connection.create_tables(\n [\n Asset,\n TradeEngine,\n Price,\n Exchange,\n Market,\n ExchangeAsset,\n Credential,\n Balance,\n Indicator,\n BaseStrategy,\n UserStrategy,\n User,\n Order,\n Position,\n ],\n safe=True,\n )\n try:\n TradeEngine._schema.create_foreign_key(TradeEngine.creator)\n Price._schema.create_foreign_key(Price.market)\n Credential._schema.create_foreign_key(Credential.user)\n Balance._schema.create_foreign_key(Balance.user)\n Indicator._schema.create_foreign_key(Indicator.strategy)\n BaseStrategy._schema.create_foreign_key(BaseStrategy.creator)\n UserStrategy._schema.create_foreign_key(UserStrategy.user)\n Order._schema.create_foreign_key(Order.position)\n except peewee.ProgrammingError:\n pass\n\n\ndef watch(num_workers=1):\n argp = argparse.ArgumentParser()\n argp.add_argument(\"num_workers\", nargs=\"?\", type=int, default=num_workers)\n args = argp.parse_args()\n Watchdog(num_workers=args.num_workers).run()\n\n\ndef serve():\n db.init()\n\n if Config().ENV_NAME == \"development\":\n api.app.run(port=5000, debug=False)\n\n elif Config().ENV_NAME == \"production\":\n waitress.serve(api.app, port=5000, channel_timeout=360)\n","repo_name":"rbmarliere/sliver","sub_path":"src/sliver/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":2382,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"34713683036","text":"\n\n\ntrain_pipeline = [\n dict(type='LoadImageFromFile'), \n dict(type='LoadAnnotations', with_bbox=True, with_mask=True),\n dict(type='Resize', \n img_scale=(1500, 1000), # expected resizing image shape (1280, 720) width, height 1333, 800\n keep_ratio=True),\n dict(type='RandomFlip', flip_ratio=0.5), # flip_ratio = [0.3, 0.5, 0.2], direction = ['horizontal', 'vertical', 'diagonal']\n # apply horizontal flip to 0.3% of the total,\n # apply vertical flip to 0.5% of the total\n # apply diagonal flip to 0.2% of the total\n # kept as original to 0.1% of the total\n dict(type='Normalize', \n mean = [123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True), \n dict(type='Pad', size_divisor=32), # padding up to size where width and height are multiples of `size_divisor` \n dict(type='DefaultFormatBundle'), \n dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), # \n]\n\n\nval_infer_pipeline = [ # infernce for during validation\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1200, 800),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(type='Normalize', \n mean = [123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True), \n dict(type='Pad', size_divisor=32),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img']),\n ])\n]\n\n\nval_pipeline = [\n dict(type='LoadImageFromFile'), \n dict(type='LoadAnnotations', with_bbox=True, with_mask=True), \n dict(type='DefaultFormatBundle'), \n dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), \n]\n\n\ndata_root = \"test_dataset\"\n\ndata = dict(\n api = \"COCO\",\n samples_per_gpu=1, # batch_size\n workers_per_gpu=1, \n train=dict(\n data_root = data_root,\n ann_file= f\"{data_root}/train_dataset.json\",\n img_prefix= \"\", \n pipeline=train_pipeline\n ),\n val=dict( \n data_root = data_root, \n ann_file= f\"{data_root}/val_dataset.json\",\n img_prefix= \"\", \n pipeline=val_pipeline, \n batch_size = 2\n )\n )\n\n\n\n","repo_name":"HibernationNo1/project_4_kubeflow_pipeline","sub_path":"config/base/dataset_config.py","file_name":"dataset_config.py","file_ext":"py","file_size_in_byte":2752,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"7026295907","text":"\"\"\"\n# Definition for a Node.\nclass Node:\n def __init__(self, val = 0, neighbors = None):\n self.val = val\n self.neighbors = neighbors if neighbors is not None else []\n\"\"\"\n\nclass Solution:\n def cloneGraph(self, node: 'Node') -> 'Node':\n if not node: \n return None\n oldToNew={}\n def dfs(root):\n if root in oldToNew:\n return oldToNew[root]\n \n dummy=Node(root.val)\n oldToNew[root]=dummy\n for neig in root.neighbors:\n dummy.neighbors.append(dfs(neig))\n \n return dummy\n return dfs(node)\n #status: correct; understood solution\n #Analysis: Time O(|V|+|E|), Space O(|V|+|E|)\n #ref: 1/28/2023P2:track1-cpGrind75;3QuestionPerDay(perQuestion-x3pomo),perDay-x10pomo;35hoursperWeek;15hrPerTopic+15hrPerTopic+5hrBuffer;Day95/96Blind31/75,2q/dayDay14/14;doLeetcodeBlind75-3/45Question/Day:completeOn2/15/2023-Day1/15:1.P1:1.wallsAndGatesTimed25Mins-x1pomo(6:00-6:30),2.implement-x1pomo(6:30-7:00),3.asteroidCollisionTimed25Mins-x1pomo(7:00-7:30),4.implement-x1pomo(7:30-8:00),5.cloneGraph-x1pomo(8:00-8:30),6.Evaluate Reverse Polish NotationTimed25Mins-x1pomo(8:30-9:00),7.implement-x1pomo(9:00-9:30),8.courseSchedule-x1pomo(9:30-10:00),10.implementTrieTimed25Mins-x1pomo(10:00-10:30),11.coinChangeTimed25Mins-x1pomo(10:30-11:00)-x12pomo(5:30-11:30)alter-x10pomo(6:00-11:00)delayed-1.P1:1.wallsAndGatesTimed25Mins-x1pomo(7:30-8:00),2.implement-x1pomo(8:00-8:30),3.asteroidCollisionTimed25Mins-x1pomo(8:30-9:00),4.implement-x1pomo(9:00-9:30),5.cloneGraph-x1pomo(9:30-10:00),6.Evaluate Reverse Polish NotationTimed25Mins-x1pomo(10:00-10:30),7.implement-x1pomo(10:30-11:00)\n","repo_name":"rasi5050/LeetcodeTrack","sub_path":"0133-clone-graph/0133-clone-graph.py","file_name":"0133-clone-graph.py","file_ext":"py","file_size_in_byte":1733,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"33204168097","text":"# -*- coding: utf-8 -*-\n# @Author : KingWolf\n# @Time : 2022/10/19 3:33\n# @File : modify_personal.py\n# @Project : WebUiAutomation\n\nimport sys\nsys.path.append(\".\")\nsys.path.append(r\"F:\\WebUiAutomation\")\nimport time\nimport re\nfrom ..base.base_handle import BaseHandle\nfrom common.common_file_path import icon_path, autoit_file\nfrom common.common_dispose_cmd import DisposeCommand\n\nclass ModifyPersonal(BaseHandle):\n \n def modify_info(self, **kwargs):\n \"\"\"\n 修改个人信息\n @param kwargs:\n @return:\n \"\"\"\n # 悬停进入“个人信息”页面\n self.mouse().move_to_element(self.find_element_highlight(kwargs[\"personal\"][\"account_setting\"])).perform()\n self.click(kwargs[\"personal\"][\"personal_info\"])\n time.sleep(1)\n # 修改头像\n self.click(kwargs[\"personal\"][\"icon\"])\n time.sleep(2)\n # 切换iframe\n self.iframe_into(kwargs[\"personal\"][\"upload_iframe\"])\n time.sleep(2)\n # 第一种方式:pathlib获取的路径为类，需要转为str类型\n self.upload_file(kwargs[\"personal\"][\"upload_button\"], str(icon_path))\n # # 第二种方式:使用第三方工具的autoIT\n # self.scroll_target(kwargs[\"personal\"][\"upload_button\"])\n # self.upload_file_click(kwargs[\"personal\"][\"upload_button\"])\n # time.sleep(2)\n # # Python执行生成的exe文件\n # DisposeCommand.execute_file(str(autoit_file))\n # time.sleep(1.5)\n # 获取上传图片大小([共1份（39.13K），已上传1份])\n self.get_img_size = self.get_element_text(kwargs[\"personal\"][\"img_size\"])\n self.images_size = re.findall(\"份（(.*)）\", self.get_img_size)[0]\n self.click(kwargs[\"personal\"][\"img_save_button\"])\n # 退出iframe\n self.iframe_out()\n # 修改昵称\n self.input(kwargs[\"personal\"][\"nickname\"], kwargs[\"personal\"][\"new_nickname\"])\n time.sleep(0.5)\n # 修改性别\n self.click(kwargs[\"personal\"][\"sex\"])\n time.sleep(0.5)\n # 修改生日(也可以通过js修改)\n self.input(kwargs[\"personal\"][\"birthday\"], kwargs[\"personal\"][\"new_birthday\"])\n # self.execute_js(kwargs[\"personal\"][\"new_birthday_js\"])\n time.sleep(0.5)\n # 最后确认保存按钮\n self.click(kwargs[\"personal\"][\"save_button\"])\n time.sleep(2)\n # 判断修改成功标志\n self.success_flag = self.get_element_text(self.find_element_highlight(kwargs[\"personal\"][\"success_modify\"]))\n self.forecast_text = kwargs[\"personal\"][\"flag_text\"]\n","repo_name":"Kingwolf9527/WebUiAutomation","sub_path":"pageobject/page/modify_personal.py","file_name":"modify_personal.py","file_ext":"py","file_size_in_byte":2637,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"43519182835","text":"from jwt import JWT, jwk_from_dict, exceptions\nfrom datetime import datetime, timedelta\n\njwt = JWT()\n\n# Get the current local time\ncurrent_time = datetime.now()\n\n# Set the expiration time to 5 minutes from the current local time\nexpiration_time = current_time + timedelta(minutes=5)\n\n# Convert the expiration time to a Unix timestamp\nexpiration_timestamp = int(expiration_time.timestamp())\n\npayload = {'username': 'purvesh', 'exp': expiration_timestamp}\n\njwk_dict = {\n \"kty\": \"oct\",\n \"k\": \"123456\"\n}\n\nkey = jwk_from_dict(jwk_dict)\ntoken = jwt.encode(payload, key)\n\nprint(\"Token:\", token)\n\n# Decode and verify the token\ntry:\n decoded_token = jwt.decode(token, key)\n print(\"Decoded token:\", decoded_token)\nexcept exceptions.JWTDecodeError as e:\n print(f\"error: {e}\")\n","repo_name":"purveshmakode24/codesnipp","sub_path":"2. Backend/jwt-encoding-decoding.py","file_name":"jwt-encoding-decoding.py","file_ext":"py","file_size_in_byte":780,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"91"}
+{"seq_id":"31805938452","text":"import logging\nfrom uuid import UUID\n\nfrom src.domain.models import File\n\nlogger = logging.getLogger(__name__)\n\n\nclass FileRepository:\n __files_tablename__ = \"files\"\n __servers_tablename__ = \"servers\"\n __file_server_tablename__ = \"m2m_file_server\"\n\n ADD_QUERY = f\"\"\"\n INSERT INTO {__files_tablename__}\n (\n id,\n name,\n size,\n user_id,\n has_deleted,\n created,\n updated\n )\n VALUES\n ($1, $2, $3, $4, $5, $6, $7);\n \"\"\"\n\n UPDATE_QUERY = f\"\"\"\n UPDATE {__files_tablename__} SET\n (\n name,\n size,\n user_id,\n has_deleted,\n created,\n updated\n ) = (\n $2, $3, $4, $5, $6, $7\n )\n WHERE id = $1;\n \"\"\"\n\n GET_BY_ID_QUERY = f\"SELECT * FROM {__files_tablename__} WHERE id = $1;\"\n\n GET_BY_NAME_AND_USER_ID_QUERY = f\"\"\"\n SELECT * FROM {__files_tablename__}\n WHERE name = $1 AND user_id = $2\n AND has_deleted = false;\n \"\"\"\n\n GET_NON_DELETED_QUERY = f\"\"\"\n SELECT * FROM {__files_tablename__}\n WHERE user_id = $1 AND has_deleted = false;\n \"\"\"\n\n GET_NON_DELETED_ON_SERVERS_QUERY = f\"\"\"\n SELECT * FROM {__files_tablename__} WHERE id = ANY (\n SELECT file_id FROM {__file_server_tablename__}\n WHERE server_id = ANY($1)\n ) AND has_deleted = false;\n \"\"\"\n\n DELETE_QUERY = (\n f\"UPDATE {__files_tablename__} SET has_deleted = true WHERE id = $1;\"\n )\n\n GET_IDS_OF_SERVERS = f\"\"\"\n SELECT server_id FROM {__file_server_tablename__}\n WHERE file_id = $1;\n \"\"\"\n\n ADD_SERVER_TO_FILE = f\"\"\"\n INSERT INTO {__file_server_tablename__}\n (id, file_id, server_id)\n (\n SELECT uuid_generate_v4(), $1, $2\n ) ON CONFLICT DO NOTHING;\n \"\"\"\n\n REMOVE_SERVER_FROM_FILE = f\"\"\"\n DELETE FROM {__file_server_tablename__}\n WHERE file_id = $1 AND server_id = $2;\n \"\"\"\n\n REMOVE_ALL_SERVERS_FROM_FILE = (\n f\"DELETE FROM {__file_server_tablename__} WHERE file_id = $1;\"\n )\n\n def __init__(self, conn):\n self._conn = conn\n\n def _convert_row_to_obj(self, row) -> File:\n return File(**dict(row.items()))\n\n async def add(self, obj: File):\n logger.debug(f\"Add file: {obj.dict()}\")\n await self._conn.execute(\n self.ADD_QUERY,\n obj.id,\n obj.name,\n obj.size,\n obj.user_id,\n obj.has_deleted,\n obj.created,\n obj.updated,\n )\n\n async def get_by_id(self, id: UUID) -> File:\n logger.debug(f\"Get file with id {id}\")\n row = await self._conn.fetchrow(self.GET_BY_ID_QUERY, id)\n if not row:\n return\n\n return self._convert_row_to_obj(row)\n\n async def get_non_deleted_by_name_and_user_id(\n self,\n name: str,\n user_id: UUID,\n ) -> UUID:\n logger.debug(f\"Get file with name {name} and user_id {user_id}\")\n row = await self._conn.fetchrow(\n self.GET_BY_NAME_AND_USER_ID_QUERY,\n name,\n user_id,\n )\n\n if not row:\n return\n\n return self._convert_row_to_obj(row)\n\n async def get_non_deleted(self, user_id: UUID) -> list[File]:\n logger.debug(\"Get all files\")\n rows = await self._conn.fetch(self.GET_NON_DELETED_QUERY, user_id)\n return [self._convert_row_to_obj(row) for row in rows]\n\n async def get_non_deleted_on_servers(\n self,\n only_servers: list[UUID],\n ) -> list[File]:\n logger.debug(\n f\"Get all files located on servers with ids {only_servers}\",\n )\n rows = await self._conn.fetch(\n self.GET_NON_DELETED_ON_SERVERS_QUERY,\n only_servers,\n )\n return [self._convert_row_to_obj(row) for row in rows]\n\n async def update(self, obj: File):\n logger.debug(f\"Update file: {obj.dict()}\")\n await self._conn.execute(\n self.UPDATE_QUERY,\n obj.id,\n obj.name,\n obj.size,\n obj.user_id,\n obj.has_deleted,\n obj.created,\n obj.updated,\n )\n\n async def delete(self, id: UUID):\n logger.debug(f\"Delete file with id {id}\")\n await self._conn.execute(self.DELETE_QUERY, id)\n\n async def get_ids_of_servers(self, file_id: UUID):\n logger.debug(f\"Get IDs servers of file with ID {id}\")\n rows = await self._conn.fetch(self.GET_IDS_OF_SERVERS, file_id)\n return [row[\"server_id\"] for row in rows]\n\n async def add_server_to_file(self, file_id: UUID, server_id: UUID):\n logger.debug(f\"Add server {server_id} to file {file_id}\")\n await self._conn.execute(self.ADD_SERVER_TO_FILE, file_id, server_id)\n\n async def remove_server_from_file(self, file_id: UUID, server_id: UUID):\n logger.debug(f\"Remove server {server_id} from file {file_id}\")\n await self._conn.execute(\n self.REMOVE_SERVER_FROM_FILE,\n file_id,\n server_id,\n )\n\n async def remove_all_servers_from_file(self, file_id: UUID):\n logger.debug(f\"Remove all servers from file {file_id}\")\n await self._conn.execute(self.REMOVE_ALL_SERVERS_FROM_FILE, file_id)\n","repo_name":"vokson/graduate_work","sub_path":"storage/src/adapters/repositories/file.py","file_name":"file.py","file_ext":"py","file_size_in_byte":6122,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"24761479355","text":"class Solution(object):\n \n def check(self, s, wordDict):\n table = [ False for _ in xrange(len(s))]\n for i in xrange(len(s)):\n if s[:i+1] in wordDict:\n table[i] = True\n continue \n for j in xrange(i, 0, -1):\n if s[j:i+1] in wordDict and table[j-1] == True:\n table[i] = True\n break\n return table[-1]\n \n def searchHelper(self, solution, currentList, currentStr, wordDict):\n if currentStr == \"\": \n solution.append(currentList)\n return\n if not self.check(currentStr, wordDict): return \n for i in xrange(1, len(currentStr) +1 ):\n if currentStr[:i] in wordDict and (currentStr[i:] == \"\" or self.check(currentStr[i:], wordDict) ):\n self.searchHelper(solution, currentList + \" \" + currentStr[:i], currentStr[i:], wordDict)\n return\n \n \n def wordBreak(self, s, wordDict):\n \"\"\"\n :type s: str\n :type wordDict: Set[str]\n :rtype: List[str]\n \"\"\"\n solution = []\n currentList = \"\"\n self.searchHelper(solution, currentList, s, wordDict)\n return map(lambda x : x.strip(), solution)\n","repo_name":"Omega094/lc_practice","sub_path":"goodyhandy/WordBreakII.py","file_name":"WordBreakII.py","file_ext":"py","file_size_in_byte":1252,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"28438432200","text":"# importing necessary modules\n\nfrom tkinter import *\nfrom PIL import Image, ImageTk\nimport tkinter.messagebox as tmsg\n\n\n# starting with userinterface\nroot = Tk()\n# size of file\nroot.geometry(\"1390x700\")\n# title of file\nroot.title(\"One-Bloodline\")\n#function for input validation\n# def Take_input():\n# INPUT = usernamevalue.get()\n# print(INPUT)\n# if (INPUT == \"gautam\"):\n# Output.insert(END, 'Correct')\n# else:\n# Output.insert(END, \"Wrong answer\")\n\n\n# label for python\nlabel = Label(text=\"Welcome to One-Bloodline\", bg=\"red\", fg=\"white\", font=(\" comicsansms \", 19, \"bold\"))\nlabel.pack(side=\"top\", fill=X, padx=20, pady=12)\n#image\nimage = Image.open(\"ONE.png\")\nphoto = ImageTk.PhotoImage(image)\n\nimage_section = Label(image=photo)\nimage_section.pack(anchor=\"n\")\n# to get min size of main window\nroot.minsize(300, 300)\n# to get maximuim size of main window\n# root.maxsize(800, 600)\n\n#function to buffer\n\ndef load():\n print(\"submiting form\")\n\n print(f\"{namevalue.get(), emailvalue.get(), numbervalue.get(), bloodvalue.get(), adhaarvalue.get()}\")\n with open(\"records.TXT\", \"a\") as f:\n f.write(f\"\\n{namevalue.get(), emailvalue.get(), numbervalue.get(), bloodvalue.get(), adhaarvalue.get()}\")\n b = tmsg.showinfo(\"Congrats\",\"Your Account Has Been Created Sucessfully\")\n\n#submit function\ndef submit():\n print(\"sucess\")\n value = bloodvalue.get()\n pincode =pincodevalue.get()\n print(pincode)\n print(value)\n if value == \"AB+\":\n def a():\n print(\"Function AB+\")\n a1 = tmsg.showinfo(\"Contact This Person\", \"Donar Name:Neel Kotnis\\n Blood Group: AB+\\n Number:9999999999\")\n a()\n elif value == \"B+\":\n def b():\n print(\"Function B+\")\n a2 = tmsg.showinfo(\"Contact This Person\",\n \"Donar Name: Samiksha Palande\\n Blood Group: B+\\n Number:8888888888\")\n b()\n elif value == \"A+\":\n def c():\n print(\"Function A+\")\n a3 = tmsg.showinfo(\"Contact This Person\",\n \"Donar Name: Gautam Suvarna\\n Blood Group: A+\\n Number:6767676767\")\n c()\n elif value == \"O+\":\n def d():\n print(\"Function O+\")\n a4 = tmsg.showinfo(\"Contact This Person\", \"Donar Name:Venus Solanki\\n Blood Group: O+\\n Number:9998765499\")\n d()\n elif value == \"O-\":\n def e():\n print(\"Function O-\")\n a5 = tmsg.showinfo(\"Contact This Person\", \"Donar Name:Pratham Tank\\n Blood Group: O-\\n Number:4444449999\")\n e()\n elif value == \"AB-\":\n def f():\n print(\"Function AB-\")\n a6 = tmsg.showinfo(\"Contact This Person\", \"Donar Name:Vans \\n Blood Group: AB-\\n Number:123456789\")\n f()\n elif value == \"B-\":\n def g():\n print(\"Function B-\")\n a7 = tmsg.showinfo(\"Contact This Person\", \"Donar Name: Prem Sangle\\n Blood Group: B-\\n Number:8887778888\")\n g()\n elif value == \"A-\":\n def h():\n print(\"Function A-\")\n a8 = tmsg.showinfo(\"Contact This Person\",\n \"Donar Name: Sahil Wadwekar\\n Blood Group: A-\\n Number:7897776767\")\n h()\n else:\n a = tmsg.showinfo(\"No Data\", \"Sorry we donnot have Any Person in your pincode\")\n\n#loggin\ndef login():\n b = tmsg.showinfo(\"Sorry\",\"This feature is under Maintainance\")\n\n\n#right side frame\nf2 =Frame(root,bg=\"grey\", borderwidth=1,relief=SUNKEN)\nf2.pack(anchor=\"ne\",padx=50,side=TOP,pady=40)\nlabel2 = Label(f2,text=\"Sign in\",bg=\"black\",fg=\"white\",padx=160)\nlabel2.grid(column=1)\n# label1.pack(pady=240,padx=100,side=\"top\")\n#sign in\nusername = Label(f2, text=\"Enter Username:\")\npassword = Label(f2, text=\"Enter password:\")\nusername.grid(row=1)\npassword.grid(row=2)\nusernamevalue = StringVar()\npasswordvalue = StringVar()\n\nusernameentry = Entry(f2, textvariable = usernamevalue,width=60)\npasswordentry = Entry(f2, textvariable = passwordvalue,width=60)\nusernameentry.grid(row=1, column=1)\npasswordentry.grid(row=2, column=1)\nb3 = Button(f2, fg=\"black\",bg=\"white\", text=\"Submit\", command=login,padx=90)\nb3.grid(column=1,row=4)\n\n#signup column\n# f3 =Frame(root,bg=\"grey\", borderwidth=1,relief=SUNKEN)\n# f3.pack(side=TOP,anchor=\"center\")\nlabel3 = Label(f2,text=\"Sign Up\",bg=\"black\",fg=\"white\",width=30)\nlabel3.grid(column=5,row=0)\n# label1.pack(pady=240,padx=100,side=\"top\")\n\nlabel4 =Label(f2,text=\"OR\",width=10,bg=\"black\",fg=\"white\")\nlabel4.grid(column=4,row=3,padx=12)\n\nName = Label(f2, text=\"Enter Full Name:\",width=30)\nEmail = Label(f2, text=\"Enter Your Email:\",width=30)\nNumber = Label(f2, text=\"Enter Your Mobile Number:\",width=30)\nblood = Label(f2, text=\"Enter Your Blood Group:\",width=30)\nAdhaar = Label(f2, text=\"Enter Your Adhaar-card No:\",width=30)\nName.grid(row=1,column=5)\nEmail.grid(row=2,column=5)\nNumber.grid(row=3,column=5)\nblood.grid(row=4,column=5)\nAdhaar.grid(row=5,column=5)\n\nnamevalue = StringVar()\nemailvalue = StringVar()\nnumbervalue = StringVar()\nbloodvalue = StringVar()\nadhaarvalue = StringVar()\n\nnameentry = Entry(f2, textvariable = namevalue,width=70)\nemailentry = Entry(f2, textvariable = emailvalue,width=70)\nnumberentry = Entry(f2, textvariable = numbervalue,width=70)\nbloodentry = Entry(f2, textvariable = bloodvalue,width=70)\nadhaarentry = Entry(f2, textvariable = adhaarvalue,width=70)\nnameentry.grid(row=1, column=6)\nemailentry.grid(row=2, column=6)\nnumberentry.grid(row=3, column=6)\nbloodentry.grid(row=4, column=6)\nadhaarentry.grid(row=5, column=6)\nb3 = Button(f2, fg=\"black\",bg=\"white\", text=\"Submit\", command=load,padx=90)\nb3.grid(column=6,row=6,pady=15)\n\n\n#left side frame\nf1 =Frame(root,bg=\"grey\", borderwidth=1,relief=SUNKEN)\nf1.pack(side=LEFT,anchor=\"n\",pady=50,fill=X,padx=60)\nlabel1 = Label(f1,text=\"Urgent Blood Requirement\",bg=\"black\",fg=\"white\",width=60)\nlabel1.grid(column=1)\n# label1.pack(pady=240,padx=100,side=\"top\")\n\npincode = Label(f1, text=\"Enter Pincode:\",padx=33)\nbloodgroup = Label(f1, text=\"Enter patient blood-group:\")\npincode.grid(row=1)\nbloodgroup.grid(row=2)\npincodevalue = StringVar()\nbloodvalue = StringVar()\n\npincodeentry = Entry(f1, textvariable = pincodevalue,width=70)\nbloodentry = Entry(f1, textvariable = bloodvalue,width=70)\npincodeentry.grid(row=1, column=1)\nbloodentry.grid(row=2, column=1)\nb2 = Button(f1, fg=\"black\",bg=\"white\", text=\"Submit\", command=submit,padx=90)\nb2.grid(column=1,row=3)\n\n#display feature\n# Display = Button(f1, height=2,\n# width=20,\n# text=\"Show\",\n# command=lambda: Take_input())\n#\n# Display.grid(column=4,row=1)\n\nroot.mainloop()\n","repo_name":"gautam798/One_Bloodline","sub_path":"user_interface_gui.py","file_name":"user_interface_gui.py","file_ext":"py","file_size_in_byte":6595,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"40765066770","text":"import json\nimport tempfile\n\nimport fixtures\nfrom lxml import etree\nfrom oslo_config import cfg\nimport requests\nimport testtools\nfrom testtools import content as test_content\nfrom testtools import matchers\nimport urllib.parse as urlparse\n\nfrom os_collect_config import cfn\nfrom os_collect_config import collect\nfrom os_collect_config import exc\n\n\nMETA_DATA = {u'int1': 1,\n u'strfoo': u'foo',\n u'map_ab': {\n u'a': 'apple',\n u'b': 'banana',\n }}\n\n\nSOFTWARE_CONFIG_DATA = {\n u'old-style': u'value',\n u'deployments': [\n {\n u'inputs': [\n {\n u'type': u'String',\n u'name': u'input1',\n u'value': u'value1'\n }\n ],\n u'group': 'Heat::Ungrouped',\n u'name': 'dep-name1',\n u'outputs': None,\n u'options': None,\n u'config': {\n u'config1': 'value1'\n }\n },\n {\n u'inputs': [\n {\n u'type': u'String',\n u'name': u'input1',\n u'value': u'value1'\n }\n ],\n u'group': 'os-apply-config',\n u'name': 'dep-name2',\n u'outputs': None,\n u'options': None,\n u'config': {\n u'config2': 'value2'\n }\n },\n {\n u'inputs': [\n {\n u'type': u'String',\n u'name': u'input1',\n u'value': u'value1'\n }\n ],\n u'name': 'dep-name3',\n u'outputs': None,\n u'options': None,\n u'config': {\n u'config3': 'value3'\n }\n },\n {\n u'inputs': [],\n u'group': 'ignore_me',\n u'name': 'ignore_me_name',\n u'outputs': None,\n u'options': None,\n u'config': 'ignore_me_config'\n }\n ]\n}\n\n\nSOFTWARE_CONFIG_IMPOSTER_DATA = {\n u'old-style': u'value',\n u'deployments': {\n u\"not\": u\"a list\"\n }\n}\n\n\nclass FakeResponse(dict):\n def __init__(self, text):\n self.text = text\n\n def raise_for_status(self):\n pass\n\n\nclass FakeReqSession(object):\n\n SESSION_META_DATA = META_DATA\n\n def __init__(self, testcase, expected_netloc):\n self._test = testcase\n self._expected_netloc = expected_netloc\n self.verify = False\n\n def get(self, url, params, headers, verify=None, timeout=None):\n self._test.addDetail('url', test_content.text_content(url))\n url = urlparse.urlparse(url)\n self._test.assertEqual(self._expected_netloc, url.netloc)\n self._test.assertEqual('/v1/', url.path)\n self._test.assertEqual('application/json',\n headers['Content-Type'])\n self._test.assertIn('SignatureVersion', params)\n self._test.assertEqual('2', params['SignatureVersion'])\n self._test.assertIn('Signature', params)\n self._test.assertIn('Action', params)\n self._test.assertEqual('DescribeStackResource',\n params['Action'])\n self._test.assertIn('LogicalResourceId', params)\n self._test.assertEqual('foo', params['LogicalResourceId'])\n self._test.assertEqual(10, timeout)\n root = etree.Element('DescribeStackResourceResponse')\n result = etree.SubElement(root, 'DescribeStackResourceResult')\n detail = etree.SubElement(result, 'StackResourceDetail')\n metadata = etree.SubElement(detail, 'Metadata')\n metadata.text = json.dumps(self.SESSION_META_DATA)\n if verify is not None:\n self.verify = True\n return FakeResponse(etree.tostring(root))\n\n\nclass FakeRequests(object):\n exceptions = requests.exceptions\n\n def __init__(self, testcase, expected_netloc='192.0.2.1:8000'):\n self._test = testcase\n self._expected_netloc = expected_netloc\n\n def Session(self):\n\n return FakeReqSession(self._test, self._expected_netloc)\n\n\nclass FakeReqSessionSoftwareConfig(FakeReqSession):\n\n SESSION_META_DATA = SOFTWARE_CONFIG_DATA\n\n\nclass FakeRequestsSoftwareConfig(FakeRequests):\n\n FAKE_SESSION = FakeReqSessionSoftwareConfig\n\n def Session(self):\n return self.FAKE_SESSION(self._test, self._expected_netloc)\n\n\nclass FakeReqSessionConfigImposter(FakeReqSession):\n\n SESSION_META_DATA = SOFTWARE_CONFIG_IMPOSTER_DATA\n\n\nclass FakeRequestsConfigImposter(FakeRequestsSoftwareConfig):\n\n FAKE_SESSION = FakeReqSessionConfigImposter\n\n\nclass FakeFailRequests(object):\n exceptions = requests.exceptions\n\n class Session(object):\n def get(self, url, params, headers, verify=None, timeout=None):\n raise requests.exceptions.HTTPError(403, 'Forbidden')\n\n\nclass TestCfnBase(testtools.TestCase):\n def setUp(self):\n super(TestCfnBase, self).setUp()\n self.log = self.useFixture(fixtures.FakeLogger())\n self.useFixture(fixtures.NestedTempfile())\n self.hint_file = tempfile.NamedTemporaryFile()\n self.hint_file.write(u'http://192.0.2.1:8000'.encode('utf-8'))\n self.hint_file.flush()\n self.addCleanup(self.hint_file.close)\n collect.setup_conf()\n cfg.CONF.cfn.heat_metadata_hint = self.hint_file.name\n cfg.CONF.cfn.metadata_url = None\n cfg.CONF.cfn.path = ['foo.Metadata']\n cfg.CONF.cfn.access_key_id = '0123456789ABCDEF'\n cfg.CONF.cfn.secret_access_key = 'FEDCBA9876543210'\n\n\nclass TestCfn(TestCfnBase):\n def test_collect_cfn(self):\n cfn_md = cfn.Collector(requests_impl=FakeRequests(self)).collect()\n self.assertThat(cfn_md, matchers.IsInstance(list))\n self.assertEqual('cfn', cfn_md[0][0])\n cfn_md = cfn_md[0][1]\n\n for k in ('int1', 'strfoo', 'map_ab'):\n self.assertIn(k, cfn_md)\n self.assertEqual(cfn_md[k], META_DATA[k])\n\n self.assertEqual('', self.log.output)\n\n def test_collect_with_ca_cert(self):\n cfn.CONF.cfn.ca_certificate = \"foo\"\n collector = cfn.Collector(requests_impl=FakeRequests(self))\n collector.collect()\n self.assertTrue(collector._session.verify)\n\n def test_collect_cfn_fail(self):\n cfn_collect = cfn.Collector(requests_impl=FakeFailRequests)\n self.assertRaises(exc.CfnMetadataNotAvailable, cfn_collect.collect)\n self.assertIn('Forbidden', self.log.output)\n\n def test_collect_cfn_no_path(self):\n cfg.CONF.cfn.path = None\n cfn_collect = cfn.Collector(requests_impl=FakeRequests(self))\n self.assertRaises(exc.CfnMetadataNotConfigured, cfn_collect.collect)\n self.assertIn('No path configured', self.log.output)\n\n def test_collect_cfn_bad_path(self):\n cfg.CONF.cfn.path = ['foo']\n cfn_collect = cfn.Collector(requests_impl=FakeRequests(self))\n self.assertRaises(exc.CfnMetadataNotConfigured, cfn_collect.collect)\n self.assertIn('Path not in format', self.log.output)\n\n def test_collect_cfn_no_metadata_url(self):\n cfg.CONF.cfn.heat_metadata_hint = None\n cfn_collect = cfn.Collector(requests_impl=FakeRequests(self))\n self.assertRaises(exc.CfnMetadataNotConfigured, cfn_collect.collect)\n self.assertIn('No metadata_url configured', self.log.output)\n\n def test_collect_cfn_missing_sub_path(self):\n cfg.CONF.cfn.path = ['foo.Metadata.not_there']\n cfn_collect = cfn.Collector(requests_impl=FakeRequests(self))\n self.assertRaises(exc.CfnMetadataNotAvailable, cfn_collect.collect)\n self.assertIn('Sub-key not_there does not exist', self.log.output)\n\n def test_collect_cfn_sub_path(self):\n cfg.CONF.cfn.path = ['foo.Metadata.map_ab']\n cfn_collect = cfn.Collector(requests_impl=FakeRequests(self))\n content = cfn_collect.collect()\n self.assertThat(content, matchers.IsInstance(list))\n self.assertEqual('cfn', content[0][0])\n content = content[0][1]\n self.assertIn(u'b', content)\n self.assertEqual(u'banana', content[u'b'])\n\n def test_collect_cfn_metadata_url_overrides_hint(self):\n cfg.CONF.cfn.metadata_url = 'http://127.0.1.1:8000/v1/'\n cfn_collect = cfn.Collector(\n requests_impl=FakeRequests(self,\n expected_netloc='127.0.1.1:8000'))\n cfn_collect.collect()\n\n\nclass TestCfnSoftwareConfig(TestCfnBase):\n def test_collect_cfn_software_config(self):\n cfn_md = cfn.Collector(\n requests_impl=FakeRequestsSoftwareConfig(self)).collect()\n self.assertThat(cfn_md, matchers.IsInstance(list))\n self.assertEqual('cfn', cfn_md[0][0])\n cfn_config = cfn_md[0][1]\n self.assertThat(cfn_config, matchers.IsInstance(dict))\n self.assertEqual(set(['old-style', 'deployments']),\n set(cfn_config.keys()))\n self.assertIn('deployments', cfn_config)\n self.assertThat(cfn_config['deployments'], matchers.IsInstance(list))\n self.assertEqual(4, len(cfn_config['deployments']))\n deployment = cfn_config['deployments'][0]\n self.assertIn('inputs', deployment)\n self.assertThat(deployment['inputs'], matchers.IsInstance(list))\n self.assertEqual(1, len(deployment['inputs']))\n self.assertEqual('dep-name1', cfn_md[1][0])\n self.assertEqual('value1', cfn_md[1][1]['config1'])\n self.assertEqual('dep-name2', cfn_md[2][0])\n self.assertEqual('value2', cfn_md[2][1]['config2'])\n\n def test_collect_cfn_deployments_not_list(self):\n cfn_md = cfn.Collector(\n requests_impl=FakeRequestsConfigImposter(self)).collect()\n self.assertEqual(1, len(cfn_md))\n self.assertEqual('cfn', cfn_md[0][0])\n self.assertIn('not', cfn_md[0][1]['deployments'])\n self.assertEqual('a list', cfn_md[0][1]['deployments']['not'])\n","repo_name":"openstack/os-collect-config","sub_path":"os_collect_config/tests/test_cfn.py","file_name":"test_cfn.py","file_ext":"py","file_size_in_byte":9990,"program_lang":"python","lang":"en","doc_type":"code","stars":54,"dataset":"github-code","pt":"91"}
+{"seq_id":"6198999934","text":"\"\"\"\nhello.py: CIS 211 assignment 1.1, Spring 2014\nauthor: Ian Garrett, igarrett@uoregon.edu\n\nPrints 'hello, world' in 1 of 4 languages. By default, the language is set as english.\nIf the language given is not a key within dictionary lang_dict, program prints an apology \nexplaining that the language asked for is not known by the program.\n\"\"\"\nimport sys\n\nlang_dict = {'english': 'Hello, world', 'french': 'Bounjour tout le monde', 'spanish': 'Hola, mundo', 'german': 'Hallo, welt'}\n\ndef hello(lang = 'english'):\n\t'''\n\tPrints \"Hello world\" in 1 of 4 languages (english, french, spanish, german)\n\n\tArgs:\n\t\tlang = str: optional arg provided by user. Specifies which language to user\n\n\tReturns:\n\t\tnothing\n\n\tEffects:\n\t\tprints 'hello world' in specified language, or explains to user that the language they asked for is not known\n\t'''\n\tif (lang in lang_dict):\n\t\tprint (lang_dict.get(lang))\n\telse:\n\t\tprint (\"Sorry, I don't speak\", lang.title())\n\nif len(sys.argv) > 1:\n\tlang = str(sys.argv[1])\n\thello(lang)\nelse:\n\thello()","repo_name":"ian-garrett/CIS_211","sub_path":"211_projects/Week_1/hello.py","file_name":"hello.py","file_ext":"py","file_size_in_byte":1013,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"4331375977","text":"from sklearn.model_selection import train_test_split\r\nimport pandas as pd\r\nfrom sklearn.neighbors import KNeighborsClassifier\r\nimport sklearn.metrics as metrics\r\n\r\nfeatures = [\"Average_Price\" , \"Maintain\" , \"Doors\" , \"Capacity\" , \"Luggage\" , \"Safety\" , \"Result\" ]\r\nlabel = [\"Label\"]\r\n\r\ndataset_cars = pd.read_csv(\"Carro_dataset_novo_csv.csv\", header = None)\r\nlabels = pd.read_csv(\"Carro_dataset_labels.csv\", header=None)\r\n\r\n\r\nX_train, X_test, y_train, y_test = train_test_split(dataset_cars, labels, test_size = 0.1)\r\n#Classifidores K = 3\r\nclassifier_knn = KNeighborsClassifier(n_neighbors=3)\r\n#treinando os dados\r\nclassifier_knn.fit(X_train, y_train)\r\ny_pred = classifier_knn.predict(X_train)\r\n#previsão\r\na = metrics.accuracy_score(y_train, y_pred)\r\n#recisão\r\nprint(a)\r\n\r\n\r\n","repo_name":"claytonfariasEC/Machine-Learning-Tree-KNN-Decision","sub_path":"Machine learning - KNN.py","file_name":"Machine learning - KNN.py","file_ext":"py","file_size_in_byte":777,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"6823165932","text":"from flask import Flask, json, request\n\napp = Flask(__name__)\n\n#Add two numbers \n@app.route('/add', methods=['POST','OPTIONS'])\n\ndef addition():\n # Get the request in JSON format and return None if it fails\n numbers = request.get_json(silent=True)\n\n # Check the numbers sent via POST\n check = check_numbers(numbers)\n if check == None:\n result = {\"result\": numbers['n1'] + numbers['n2']}\n else:\n result = {\"result\": check}\n \n return format_result(result)\n\n\n#Subtract two numbers\n@app.route('/subtract', methods=['POST','OPTIONS'])\ndef substraction():\n numbers = request.get_json(silent=True)\n\n check = check_numbers(numbers)\n if check == None:\n result = {\"result\": numbers['n1'] - numbers['n2']}\n else:\n result = {\"result\": check}\n \n return format_result(result)\n\n#Divide two numbers \n\n@app.route('/div', methods=['POST','OPTIONS'])\ndef division():\n numbers = request.get_json(silent=True)\n \n check = check_numbers(numbers)\n if check == None:\n result = {\"result\": numbers['n1'] / numbers['n2']}\n else:\n result = {\"result\": check}\n \n return format_result(result)\n\n# Multiplies two numbers\n\n@app.route('/mult', methods=['POST', 'OPTIONS'])\ndef multiplication():\n numbers = request.get_json(silent=True)\n \n check = check_numbers(numbers)\n\n if check == None:\n result = {\"result\": numbers['n1'] * numbers['n2']}\n else:\n result = {\"result\": check}\n \n return format_result(result)\n\n\ndef check_numbers(numbers):\n \"\"\"\n - Numbers should be a dictionary (JSON), Length = 2, keys 'n1' and 'n2'.\n - Returns None if all conditions are met, otherwise returns an error message.\n \"\"\"\n \n if numbers == None:\n return \"Please POST two numbers to use this API with format {\\\"n1\\\":, \\\"n2\\\":}\"\n if len(numbers) != 2:\n return \"Only 2 numbers are allowed\"\n if 'n1' not in numbers or 'n2' not in numbers:\n return \"The JSON POST data should have format {\\\"n1\\\":, \\\"n2\\\":}\"\n\n return None\n\ndef format_result(result):\n \"\"\"\n - Formats the result \n - Turn it into JSON format\n \"\"\"\n # Turns result into a response object, with JSON data\n result = json.jsonify(result) \n \n # Add CORS headers to response\n result.headers.add('Access-Control-Allow-Headers', 'content-type')\n result.headers.add('Access-Control-Allow-Origin', '*')\n\n return result\n\nif __name__ == '__main__':\n app.run(debug=True)","repo_name":"minhtanngo/Calculator-API-Flask","sub_path":"cal.py","file_name":"cal.py","file_ext":"py","file_size_in_byte":2520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"42898191113","text":"from distutils.core import setup, Extension\n\ncallback_manager_module = Extension('callbackmodule',\n libraries = ['callback_manager'],\n library_dirs = ['/home/karsten/workspace/private/code_snippets/python/python_c_extension'],\n sources = ['callbackmodule.c']\n )\n\nsetup(name='callbackmodule',\n version='1.0',\n description='Callback module description',\n ext_modules=[callback_manager_module])\n","repo_name":"Karsten1987/code_snippets","sub_path":"python/python_c_extension/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":547,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"19620184494","text":"import os\nimport sys\n\nfrom . import build\nfrom .utils import bash\n\n\ndef run(components):\n for component in components:\n if not component.release_process:\n print(\"{0}: doesn't support releasing\".format(component.title))\n continue\n if component.release_process == 'docker':\n env = os.environ.copy()\n env.update(component.env)\n bash('tag-push', stdout=sys.stdout, stderr=sys.stderr, env=env)\n elif component.release_process == 'custom':\n build.run('release', [component])\n else:\n raise Exception(\"Unsupported release process\")\n","repo_name":"ployst/component-builder","sub_path":"component-builder/src/component_builder/release.py","file_name":"release.py","file_ext":"py","file_size_in_byte":632,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"91"}
+{"seq_id":"42743650614","text":"class Mahasiswa:\r\n def __init__(self,nama,nim):\r\n self.nama = nama #Atribut public\r\n self.nim = nim #Atribut public\r\n #self.prodi = prodi\r\n #self.thn_masuk = thn_masuk\r\n #self.semester = semester\r\n\r\n def tampilMhs(self): #Function untuk menampilkan nama dan nim\r\n print(\"Nama : \", self.nama, \", nim : \", self.nim)\r\n \r\n #Method Overloading\r\n def HitungSKS(self, jmlsks = None , sks = None): #Function hitung sks\r\n if jmlsks != None and sks != None: #JIka ada nilai jumlah sks dan sks maka cetak total sks\r\n totalsks = jmlsks +sks\r\n print(\"Total sks : \",totalsks) # cetak total sks\r\n else: # jika tidak ada nilai sks maka cetak jumlah sks\r\n totalsks = jmlsks\r\n print(\"Total sks : \", totalsks) #cetak total sks\r\n\r\n#Memanggil kelas\r\nmhs1 = Mahasiswa (\"Eren\" , 123180015) \r\nmhs2 = Mahasiswa (\"Luffy\", 123190007)\r\nmhs1.tampilMhs() #Menampilkan data dengan function tampil Mhs\r\nmhs2.tampilMhs()\r\nmhs1.HitungSKS(80, 34) #Overloading\r\nmhs2.HitungSKS(83) #Overloading\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"Ngakak4G/Tugas-PBO-Praktik","sub_path":"Tugas 6/Overloading_Mahasiswa.py","file_name":"Overloading_Mahasiswa.py","file_ext":"py","file_size_in_byte":1084,"program_lang":"python","lang":"id","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"2475620220","text":"import socket\n\nREMOTE_HOST = \"localhost\"\nREMOTE_PORT = 8080 # Порт, на котором запущен удаленный сервис\n\nclient_socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n\ntry:\n client_socket.sendto(b'Ping', (REMOTE_HOST, REMOTE_PORT))\n print(\"Отправлен запрос Ping на удаленный сервер\")\nexcept Exception as e:\n print(f\"Ошибка при отправке: {str(e)}\")\nfinally:\n client_socket.close()","repo_name":"GromovoiStanislav/UDP","sub_path":"Project 0.2/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":479,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"8232618256","text":"import pygame\n\nfrom scripts.text import Text\nfrom scripts.settings import *\n\nclass Button: \n \n def __init__(self, color, x, y, width = 250, height = 64, texto = ' ', call_back = None):\n self.display = pygame.display.get_surface()\n self.color = color\n self.texto = texto\n self.text_color = SECONDARY_COLOR\n self.react = pygame.Rect(x,y,width,height)\n \n self.text = Text(\n \"assets/fonts/airstrike.ttf\", \n 40,\n texto, \n self.text_color, \n [(x + self.react.width / 2), (y + self.react.height / 2)]\n )\n \n self.call_back = call_back\n \n def events(self, event):\n if event.type == pygame.MOUSEMOTION:\n if self.react.collidepoint(event.pos):\n self.color = SECONDARY_COLOR\n self.text.update_text(self.texto, WHITE_COLOR)\n else:\n self.color = WHITE_COLOR\n self.text.update_text(self.texto, SECONDARY_COLOR)\n \n if event.type == pygame.MOUSEBUTTONDOWN:\n if event.button == 1 and self.react.collidepoint(event.pos):\n self.call_back()\n \n def draw(self):\n pygame.draw.rect(self.display, self.color, self.react)\n self.text.draw_center()","repo_name":"igorcacerez/bolotas-jogo-python","sub_path":"scripts/button.py","file_name":"button.py","file_ext":"py","file_size_in_byte":1305,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"}
+{"seq_id":"15442531102","text":"import sys\r\nfrom collections import deque\r\n\r\nM, N = map(int, sys.stdin.readline().split())\r\nlst = []\r\nqueue = deque([])\r\ndx = [1, -1, 0, 0]\r\ndy = [0, 0, 1, -1]\r\ncount = 0\r\n\r\nfor _ in range(N):\r\n lst.append(list(map(int, sys.stdin.readline().split())))\r\n\r\nfor i in range(N):\r\n for j in range(M):\r\n if lst[i][j] == 1:\r\n queue.append([i, j])\r\n\r\n\r\ndef bfs():\r\n while queue:\r\n a, b = queue.popleft()\r\n for m in range(4):\r\n if a+dx[m] >= 0 and b+dy[m] >= 0 and a+dx[m] < N and b+dy[m] < M and lst[a+dx[m]][b+dy[m]] == 0:\r\n lst[a+dx[m]][b+dy[m]] = lst[a][b]+1\r\n queue.append([a+dx[m], b+dy[m]])\r\n\r\n\r\nbfs()\r\n\r\nfor i in lst:\r\n for j in i:\r\n if j == 0:\r\n print(-1)\r\n exit(0)\r\n if max(i) > count:\r\n count = max(i)\r\n\r\nprint(count-1)\r\n","repo_name":"RinRinPARK/Algorithm","sub_path":"BFS/7576.py","file_name":"7576.py","file_ext":"py","file_size_in_byte":846,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"39864016299","text":"import tensorflow as tf\nfrom nets.inception_v3 import inception_v3, inception_v3_arg_scope\nfrom tensorflow.contrib.framework.python.ops.variables import get_or_create_global_step\nfrom tensorflow.python.platform import tf_logging as logging\nimport inception_preprocessing\nfrom nets import nets_factory\nfrom preprocessing import preprocessing_factory\nimport os\nimport time\n\nfrom tensorflow.python.client import device_lib\n\nslim = tf.contrib.slim\n\ndataset_dir = 'tmp/dataset_parallel_trf/depth'\nlog_dir = 'tmp/log/depth_with_cp'\ncheckpoint_file = 'tmp/checkpoints/inception_v3.ckpt'\nimage_size = 256\nnum_classes = 5\nlabels_file = 'tmp/dataset_parallel_trf/depth/labels.txt'\nlabels = open(labels_file, 'r')\nlabels_to_name = {}\nfor line in labels:\n label, string_name = line.split(':')\n string_name = string_name[:-1]\n labels_to_name[int(label)] = string_name\n\nfile_pattern = 'objects_%s_*.tfrecord'\n\nitems_to_descriptions = {\n 'image': 'A 3-channel RGB coloured product image',\n 'label': 'A label that from 4 labels'\n}\n\nnum_epochs = 3\nbatch_size = 6\ninitial_learning_rate = 0.001\nlearning_rate_decay_factor = 0.75\nnum_epochs_before_decay = 1\n\ndevice_lib.list_local_devices()\n\ndef get_split(split_name, dataset_dir, file_pattern=file_pattern, file_pattern_for_counting='objects'):\n if split_name not in ['train', 'validation']:\n raise ValueError(\n 'The split_name %s is not recognized. Please input either train or validation as the split_name' % (\n split_name))\n\n file_pattern_path = os.path.join(dataset_dir, file_pattern % (split_name))\n\n num_samples = 0\n file_pattern_for_counting = 'objects' + '_' + split_name\n tfrecords_to_count = [os.path.join(dataset_dir, file) for file in os.listdir(dataset_dir) if\n file.startswith(file_pattern_for_counting)]\n for tfrecord_file in tfrecords_to_count:\n for record in tf.python_io.tf_record_iterator(tfrecord_file):\n num_samples += 1\n\n test = num_samples\n\n reader = tf.TFRecordReader\n\n keys_to_features = {\n 'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),\n 'image/format': tf.FixedLenFeature((), tf.string, default_value='jpg'),\n 'image/class/label': tf.FixedLenFeature(\n [], tf.int64, default_value=tf.zeros([], dtype=tf.int64)),\n }\n\n items_to_handlers = {\n 'image': slim.tfexample_decoder.Image(),\n 'label': slim.tfexample_decoder.Tensor('image/class/label'),\n }\n\n decoder = slim.tfexample_decoder.TFExampleDecoder(keys_to_features, items_to_handlers)\n\n labels_to_name_dict = labels_to_name\n\n dataset = slim.dataset.Dataset(\n data_sources=file_pattern_path,\n decoder=decoder,\n reader=reader,\n num_readers=4,\n num_samples=num_samples,\n num_classes=num_classes,\n labels_to_name=labels_to_name_dict,\n items_to_descriptions=items_to_descriptions)\n\n return dataset\n\ndef load_batch(dataset, batch_size, is_training=True):\n '''\n Loads a batch for training.\n\n INPUTS:\n - dataset(Dataset): a Dataset class object that is created from the get_split function\n - batch_size(int): determines how big of a batch to train\n - height(int): the height of the image to resize to during preprocessing\n - width(int): the width of the image to resize to during preprocessing\n - is_training(bool): to determine whether to perform a training or evaluation preprocessing\n\n OUTPUTS:\n - images(Tensor): a Tensor of the shape (batch_size, height, width, channels) that contain one batch of images\n - labels(Tensor): the batch's labels with the shape (batch_size,) (requires one_hot_encoding).\n\n '''\n # First create the data_provider object\n data_provider = slim.dataset_data_provider.DatasetDataProvider(\n dataset,\n common_queue_capacity=24 + 3 * batch_size,\n common_queue_min=24)\n\n # # Obtain the raw image using the get method\n image, label = data_provider.get(['image', 'label'])\n\n # # Perform the correct preprocessing for this image depending if it is training or evaluating\n image_preprocessing_fn = preprocessing_factory.get_preprocessing('inception_v3',is_training=False)\n\n train_image_size = 256\n image = image_preprocessing_fn(image, train_image_size, train_image_size)\n\n # # Batch up the image by enqueing the tensors internally in a FIFO queue and dequeueing many elements with tf.train.batch.\n images, labels = tf.train.batch(\n [image, label],\n batch_size=batch_size,\n num_threads=4,\n capacity=5 * batch_size)\n\n return images, labels\n\ndef run():\n # Create the log directory here. Must be done here otherwise import will activate this unneededly.\n if not os.path.exists(log_dir):\n os.mkdir(log_dir)\n\n # ======================= TRAINING PROCESS =========================\n # Now we start to construct the graph and build our model\n with tf.Graph().as_default() as graph:\n tf.logging.set_verbosity(tf.logging.INFO) # Set the verbosity to INFO level\n\n # First create the dataset and load one batch\n dataset = get_split('train', dataset_dir, file_pattern=file_pattern)\n images, labels = load_batch(dataset, batch_size=batch_size)\n\n # Know the number steps to take before decaying the learning rate and batches per epoch\n num_batches_per_epoch = int(dataset.num_samples / batch_size)\n num_steps_per_epoch = num_batches_per_epoch # Because one step is one batch processed\n decay_steps = int(num_epochs_before_decay * num_steps_per_epoch)\n\n # Create the model inference\n with slim.arg_scope(inception_v3_arg_scope()):\n logits, end_points = inception_v3(images, num_classes=dataset.num_classes, is_training=True)\n\n\n # Define the scopes that you want to exclude for restoration\n exclude = ['InceptionV3/Logits', 'InceptionV3/AuxLogits']\n variables_to_restore = slim.get_variables_to_restore(exclude=exclude)\n\n # Perform one-hot-encoding of the labels (Try one-hot-encoding within the load_batch function!)\n one_hot_labels = slim.one_hot_encoding(labels, dataset.num_classes)\n\n # Performs the equivalent to tf.nn.sparse_softmax_cross_entropy_with_logits but enhanced with checks\n loss = tf.losses.softmax_cross_entropy(onehot_labels=one_hot_labels, logits=logits)\n total_loss = tf.losses.get_total_loss(add_regularization_losses=True) # obtain the regularization losses as well\n\n # Create the global step for monitoring the learning_rate and training.\n global_step = get_or_create_global_step()\n\n # Define your exponentially decaying learning rate\n lr = tf.train.exponential_decay(\n learning_rate=initial_learning_rate,\n global_step=global_step,\n decay_steps=decay_steps,\n decay_rate=learning_rate_decay_factor,\n staircase=True)\n\n # Now we can define the optimizer that takes on the learning rate\n optimizer = tf.train.AdamOptimizer(learning_rate=lr)\n\n # Create the train_op.\n train_op = slim.learning.create_train_op(total_loss, optimizer)\n\n # State the metrics that you want to predict. We get a predictions that is not one_hot_encoded.\n predictions = tf.argmax(end_points['Predictions'], 1)\n probabilities = end_points['Predictions']\n accuracy, accuracy_update = tf.contrib.metrics.streaming_accuracy(predictions, labels)\n metrics_op = tf.group(accuracy_update, probabilities)\n\n # Now finally create all the summaries you need to monitor and group them into one summary op.\n tf.summary.scalar('losses/Total_Loss', total_loss)\n tf.summary.scalar('accuracy', accuracy)\n tf.summary.scalar('learning_rate', lr)\n my_summary_op = tf.summary.merge_all()\n\n # Now we need to create a training step function that runs both the train_op, metrics_op and updates the global_step concurrently.\n def train_step(sess, train_op, global_step):\n '''\n Simply runs a session for the three arguments provided and gives a logging on the time elapsed for each global step\n '''\n # Check the time for each sess run\n start_time = time.time()\n total_loss, global_step_count, _ = sess.run([train_op, global_step, metrics_op])\n time_elapsed = time.time() - start_time\n\n # Run the logging to print some results\n logging.info('global step %s: loss: %.4f (%.2f sec/step)', global_step_count, total_loss, time_elapsed)\n\n return total_loss, global_step_count\n\n # Now we create a saver function that actually restores the variables from a checkpoint file in a sess\n saver = tf.train.Saver(variables_to_restore)\n\n def restore_fn(sess):\n return saver.restore(sess, checkpoint_file)\n\n # Define your supervisor for running a managed session. Do not run the summary_op automatically or else it will consume too much memory\n sv = tf.train.Supervisor(logdir=log_dir, summary_op=None, init_fn=restore_fn)\n\n # Run the managed session\n with sv.managed_session() as sess:\n print (num_steps_per_epoch)\n print (num_epochs)\n for step in range(num_steps_per_epoch * num_epochs):\n # At the start of every epoch, show the vital information:\n if step % num_batches_per_epoch == 0:\n logging.info('Epoch %s/%s', step / num_batches_per_epoch + 1, num_epochs)\n learning_rate_value, accuracy_value = sess.run([lr, accuracy])\n logging.info('Current Learning Rate: %s', learning_rate_value)\n logging.info('Current Streaming Accuracy: %s', accuracy_value)\n\n # optionally, print your logits and predictions for a sanity check that things are going fine.\n logits_value, probabilities_value, predictions_value, labels_value = sess.run(\n [logits, probabilities, predictions, labels])\n print ('logits: \\n', logits_value)\n print ('Probabilities: \\n', probabilities_value)\n print ('predictions: \\n', predictions_value)\n print ('Labels:\\n:', labels_value)\n\n # Log the summaries every 10 step.\n if step % 10 == 0:\n train_writer = tf.summary.FileWriter('tmp/vis/depth_with_cp')\n loss, _ = train_step(sess, train_op, sv.global_step)\n summaries = sess.run(my_summary_op)\n sv.summary_computed(sess, summaries)\n train_writer.add_summary(summaries, step)\n train_writer.close()\n # If not, simply run the training step\n else:\n loss, _ = train_step(sess, train_op, sv.global_step)\n\n # We log the final training loss and accuracy\n logging.info('Final Loss: %s', loss)\n logging.info('Final Accuracy: %s', sess.run(accuracy))\n\n # Once all the training has been done, save the log files and checkpoint model\n logging.info('Finished training! Saving model to disk now.')\n sv.saver.save(sess, sv.save_path, global_step=sv.global_step)\n \nif __name__ == '__main__':\n run()","repo_name":"o-soroka/cv_study","sub_path":"train_se.py","file_name":"train_se.py","file_ext":"py","file_size_in_byte":11454,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"26112510252","text":"import os\nimport sys\nimport socket\nimport re\nimport urllib.request\n\n\nif __name__ == \"__main__\":\n # get host from environment\n host = os.getenv(\"HOST\")\n if not host:\n print(\"No HOST supplied from environment\")\n sys.exit(-1)\n\n # get port from environment\n port = int(os.getenv(\"PORT\",\"0\"))\n if port == 0:\n print(\"No PORT supplied from environment\")\n sys.exit(-1)\n\n # get ticket from environment\n ticket = os.getenv(\"TICKET\")\n\n # connect to service\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((host, port))\n \n # pass ticket to ticket-taker\n if ticket:\n prompt = s.recv(128) # \"Ticket please:\"\n s.send((ticket + \"\\n\").encode(\"utf-8\"))\n\n # receive connection string\n prompt = s.recv(256)\n m = re.search(r\"(http://[a-zA-Z0-9.\\-]+:\\d+/)\", prompt.decode(\"utf-8\"))\n if not m:\n print(\"Did not receive expected prompt from service\")\n sys.exit(-1)\n url = m.group(1)\n print(url)\n \n # get flag from webpage\n f = urllib.request.urlopen(url + \"flag.html\")\n print(f.read())\n","repo_name":"deptofdefense/HAS-Qualifier-Challenges","sub_path":"basic-handoff/solver/solve.py","file_name":"solve.py","file_ext":"py","file_size_in_byte":1106,"program_lang":"python","lang":"en","doc_type":"code","stars":94,"dataset":"github-code","pt":"91"}
+{"seq_id":"13025993810","text":"\r\nd = {0:\"A\",1:\"B\",2:\"C\"}\r\nimport random\r\ndef rand1(q):\r\n return random.randint(int(q),int(2*q))\r\ndef rand():\r\n return random.randrange(30,60)\r\ndef perSecCar(n,g):\r\n m = float(\"{0:.2f}\".format(n/g))\r\n return m\r\n \r\nwith open(\"trafficdata.csv\",'w') as csvfive:\r\n carsGreenSec = [0,0,0]\r\n carPerSec = [0,0,0]\r\n waitTime = [60,30,0]\r\n g = [30,30,30]\r\n p = []\r\n \r\n n = 3\r\n for j in range(3):\r\n carsGreenSec[j] = rand()\r\n carPerSec[j] = perSecCar(carsGreenSec[j],30)\r\n print(str(\"[30,30,30]\") + \" Green in Previous Cycle\")\r\n print(str(carsGreenSec) +\" Number of cars captured\")\r\n print(str(carPerSec) +\" Rate of flow of car\")\r\n print(str(waitTime) +\" Wait Time\")\r\n print(str(sum(carsGreenSec))+\" Total Number of cars in cylce\")\r\n print(str(sum(waitTime)/3)+\" Average WaitTime\")\r\n k = 0\r\n for l in range(10):\r\n p = list(g)\r\n f = (carPerSec[(l+1)%n]+carPerSec[(l+2)%n])/1.025\r\n g[l%n] = int((carPerSec[l%n]*waitTime[l%n])/f)\r\n waitTime[l%n] = 0\r\n for t in range(3):\r\n if t!= l%n:\r\n waitTime[t] += g[l%n]\r\n \r\n for j in range(3):\r\n if j == l%n:\r\n carsGreenSec[j] = rand1(g[j]) \r\n carPerSec[j] = perSecCar(carsGreenSec[j],g[l%n])\r\n if l%n == 0:\r\n print(str(l+1) + \" Cycle\")\r\n print(str(g[l%n]) + \" Green Light for \" +str(d[l%n]) +\" Line\")\r\n \r\n print(str(p) + \" Previous cycle green\")\r\n print( str(carsGreenSec) + \" Cars passing in greenLight\")\r\n print( str(carPerSec) + \" Cars Passing Per Second\")\r\n print(str(waitTime) + \" Wait Time\")\r\n print(str(sum(carsGreenSec))+\" Total Number of cars in cycle\")\r\n print(str(sum(waitTime)/3)+\" Average WaitTime\")\r\n\r\n \r\n\r\n\r\n \r\n\r\n","repo_name":"Karan970/smartCity","sub_path":"SmartCity-master/traffic.py","file_name":"traffic.py","file_ext":"py","file_size_in_byte":1858,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"28421119367","text":"from django.urls import path\r\nfrom rest_framework.urlpatterns import format_suffix_patterns\r\nfrom .views import (\r\n\ttest,\r\n\tview_all_transactions,\r\n\tmake_transaction,\r\n\tdebit_history,\r\n\tcredit_history\r\n)\r\n\r\nurlpatterns = [\r\n path('test/', test, name='test'),\r\n path('viewTransactions/', view_all_transactions, name='view_all_transactions'),\r\n path('makeTransaction/', make_transaction, name='make_transaction'),\r\n path('debitHistory/', debit_history, name='debit_history'),\r\n path('creditHistory/', credit_history, name='credit_history'),\r\n]\r\nurlpatterns = format_suffix_patterns(urlpatterns)\r\n","repo_name":"Mohit-15/UPIGO","sub_path":"transactions/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":609,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"91"}
+{"seq_id":"40909439066","text":"import numpy as np\r\nimport cv2\r\n\r\ncv2.namedWindow('frame', cv2.WINDOW_NORMAL)\r\n\r\nsource = cv2.VideoCapture('video.mp4')\r\nframes = []\r\n\r\nwhile True:\r\n\r\n data, frame = source.read()\r\n if data:\r\n frames.append(frame)\r\n else:\r\n break\r\n\r\nsource.release()\r\n\r\nplay = True\r\npaused = False\r\ni = 0\r\n\r\nwhile play:\r\n frame = frames[i]\r\n\r\n size = cv2.getWindowImageRect(\"frame\")\r\n cv2.imshow('frame', cv2.resize(frame, (size[2], size[3]), interpolation=cv2.INTER_CUBIC))\r\n \r\n key = cv2.waitKey(33) & 0xFF\r\n if key == ord('q'):\r\n break\r\n if key == ord('0'):\r\n i = 0\r\n elif key == ord(' '):\r\n paused = not paused\r\n elif key == ord('a'):\r\n i = max(0, i-240)\r\n elif key == ord('d'):\r\n i += 240\r\n elif key == ord('j'):\r\n i = max(0, i-1)\r\n elif key == ord('k'):\r\n i += 1\r\n\r\n if not paused:\r\n i += 1\r\n i %= len(frames)\r\n\r\ncv2.destroyAllWindows()\r\n","repo_name":"henryz2004/OpenCV_Experiments","sub_path":"openCV_projects/learn/cv2showvid.py","file_name":"cv2showvid.py","file_ext":"py","file_size_in_byte":952,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"2652425847","text":"import httpx\n\ngroup_types = (\"group\", \"supergroup\")\n\nhttp = httpx.AsyncClient(http2=True)\n\n\nclass Permissions:\n can_be_edited = \"can_be_edited\"\n delete_messages = \"can_delete_messages\"\n restrict_members = \"can_restrict_members\"\n promote_members = \"can_promote_members\"\n change_info = \"can_change_info\"\n invite_users = \"can_invite_users\"\n pin_messages = \"can_pin_messages\"\n","repo_name":"PerU-MoNsteR/EduRobot","sub_path":"consts.py","file_name":"consts.py","file_ext":"py","file_size_in_byte":393,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"91"}
+{"seq_id":"8780939869","text":"# https://leetcode.com/problems/lowest-common-ancestor-of-a-binary-tree-iv\n# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n def lowestCommonAncestor(self, root: 'TreeNode', nodes: 'List[TreeNode]') -> 'TreeNode':\n self.parent = dict()\n \n def helper(n, p):\n if not n:\n return\n \n self.parent[n] = p\n helper(n.left, n)\n helper(n.right, n)\n \n helper(root, root)\n \n if len(nodes) == 1:\n return nodes[0]\n \n # take the first node and find its LCA against every other node\n first = nodes[0]\n \n # populate first node's parent dictionary\n dic = dict()\n while first:\n p = self.parent[first]\n dic[first] = p\n \n if p == first:\n break\n first = p\n \n # finds lca between node n and first node\n def findLca(n):\n while n:\n if n in dic:\n return n\n \n p = self.parent[n]\n \n if p == n:\n return None\n \n n = p\n \n # go through all OTHER nodes and populate LCAs list with only unique LCA candidates\n lcas = set()\n for i in range(1, len(nodes)):\n res = findLca(nodes[i])\n lcas.add(res)\n \n # do another traversal to find the global LCA from among the candidates\n # the global LCA will be the FIRST element to be encountered (amongst potential LCAs) as we traverse down the tree\n self.globalLca = None\n def findGlobalLca(node):\n if not node:\n return\n \n if node in lcas and not self.globalLca:\n self.globalLca = node\n return\n \n findGlobalLca(node.left)\n findGlobalLca(node.right)\n \n findGlobalLca(root)\n return self.globalLca","repo_name":"abhisha1991/LeetCodePractice","sub_path":"Python/LowestCommonAncestorBinaryTreeIV.py","file_name":"LowestCommonAncestorBinaryTreeIV.py","file_ext":"py","file_size_in_byte":2174,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"8738279074","text":"import click\n\n\n@click.command(\"check\", help=\"Check Configuration file\")\n@click.option(\n \"-c\", \"--configuration_file\", required=True, help=\"Input configuration file.\"\n)\n@click.option(\n \"--close/--noclose\", default=True, help=\"Raises system error if placeholders found\"\n)\ndef check(configuration_file, close):\n\n from choixe.configurations import XConfig\n import rich\n\n try:\n cfg = XConfig(filename=configuration_file)\n rich.print(cfg.to_dict())\n except Exception as e:\n rich.print(f\"[red]Invalid configuration file: {e}[/red]\")\n import sys\n\n sys.exit(1)\n\n rich.print(\"[green]Configuration is good![/green]\")\n\n # d = cfg.to_dict(discard_private_qualifiers=False)\n cfg.check_available_placeholders(close_app=close)\n","repo_name":"eyecan-ai/choixe","sub_path":"choixe/cli/check.py","file_name":"check.py","file_ext":"py","file_size_in_byte":773,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"91"}
+{"seq_id":"28800818244","text":"import os\nimport datetime\nfrom gnr.core.gnrbag import Bag\nfrom gnr.web.gnrwebpage_proxy.gnrbaseproxy import GnrBaseProxy\n\nclass GnrWebDebugger(GnrBaseProxy):\n def init(self, **kwargs):\n self.db = self.page.db\n self.debug = getattr(self.page, 'debug', False)\n self._debug_calls = Bag()\n\n def output(self, debugtype, **kwargs):\n page = self.page\n debugopt = getattr(page, 'debugopt', '') or ''\n if debugopt and debugtype in debugopt:\n getattr(self, 'output_%s' % debugtype)(page, **kwargs)\n\n def output_sql(self, page, sql=None, sqlargs=None, dbtable=None, error=None):\n dbtable = dbtable or '-no table-'\n kwargs=dict(sqlargs)\n kwargs.update(sqlargs)\n value = sql\n if error:\n kwargs['sqlerror'] = str(error)\n self._debug_calls.addItem('%03i Table:%s' % (len(self._debug_calls), dbtable.replace('.', '_')), value,\n **kwargs)\n\n def event_onCollectDatachanges(self):\n page = self.page\n if page.debugopt and self._debug_calls:\n path = 'gnr.debugger.main.c_%s' % self.page.callcounter\n page.setInClientData(path, self._debug_calls)\n\n def log(self, msg):\n if self.debug:\n f = file(self.logfile, 'a')\n f.write('%s -- %s\\n' % (datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), msg))\n f.close()\n\n def _get_logfile(self):\n if not hasattr(self, '_logfile'):\n logdir = os.path.normpath(os.path.join(self.page.site.site_path, 'data', 'logs'))\n if not os.path.isdir(logdir):\n os.makedirs(logdir)\n self._logfile = os.path.join(logdir, 'error_%s.log' % datetime.date.today().strftime('%Y%m%d'))\n return self._logfile\n\n logfile = property(_get_logfile)","repo_name":"poppogbr/genropy","sub_path":"gnrpy/gnr/web/gnrwebpage_proxy/debugger.py","file_name":"debugger.py","file_ext":"py","file_size_in_byte":1836,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"}
+{"seq_id":"20377906463","text":"\"\"\"Hyperparameter sweeps with Halton sequences of quasi-random numbers.\n\nBased off the algorithms described in https://arxiv.org/abs/1706.03200. Inspired\nby the code in\nhttps://github.com/google/uncertainty-baselines/blob/master/uncertainty_baselines/halton.py\nwritten by the same authors.\n\"\"\"\n\nimport collections\nimport functools\nimport itertools\nimport math\nfrom typing import Any, Callable, Dict, List, Sequence, Text, Tuple, Union\n\nfrom absl import logging\nfrom numpy import random\n\n_SweepSequence = List[Dict[Text, Any]]\n_GeneratorFn = Callable[[float], Tuple[Text, float]]\n\n\ndef generate_primes(n: int) -> List[int]:\n \"\"\"Generate primes less than `n` (except 2) using the Sieve of Sundaram.\"\"\"\n half_m1 = int((n - 2) / 2)\n sieve = [0] * (half_m1 + 1)\n for outer in range(1, half_m1 + 1):\n inner = outer\n while outer + inner + 2 * outer * inner <= half_m1:\n sieve[outer + inner + (2 * outer * inner)] = 1\n inner += 1\n return [2 * i + 1 for i in range(1, half_m1 + 1) if sieve[i] == 0]\n\n\ndef _is_prime(n: int) -> bool:\n \"\"\"Check if `n` is a prime number.\"\"\"\n return all(n % i != 0 for i in range(2, int(n**0.5) + 1)) and n != 2\n\n\ndef _generate_dim(num_samples: int,\n base: int,\n per_dim_shift: bool,\n shuffled_seed_sequence: List[int]) -> List[float]:\n \"\"\"Generate `num_samples` from a Van der Corput sequence with base `base`.\n\n Args:\n num_samples: int, the number of samples to generate.\n base: int, the base for the Van der Corput sequence. Must be prime.\n per_dim_shift: boolean, if true then each dim in the sequence is shifted by\n a random float (and then passed through fmod(n, 1.0) to keep in the range\n [0, 1)).\n shuffled_seed_sequence: An optional list of length `base`, used as the input\n sequence to generate samples. Useful for deterministic testing.\n\n Returns:\n A shuffled Van der Corput sequence of length `num_samples`, and optionally a\n shift added to each dimension.\n\n Raises:\n ValueError: if `base` is negative or not prime.\n \"\"\"\n if base < 0 or not _is_prime(base):\n raise ValueError('Each Van der Corput sequence requires a prime `base`, '\n f'received {base}.')\n\n rng = random.RandomState(base)\n if shuffled_seed_sequence is None:\n shuffled_seed_sequence = list(range(1, base))\n # np.random.RandomState uses MT19937 (see\n # https://numpy.org/devdocs/reference/random/legacy.html#numpy.random.RandomState).\n rng.shuffle(shuffled_seed_sequence)\n shuffled_seed_sequence = [0] + shuffled_seed_sequence\n\n # Optionally generate a random float in the range [0, 1) to shift this\n # dimension by.\n dim_shift = rng.random_sample() if per_dim_shift else None\n\n dim_sequence = []\n for i in range(1, num_samples + 1):\n num = 0.\n denominator = base\n while i:\n num += shuffled_seed_sequence[i % base] / denominator\n denominator *= base\n i //= base\n if per_dim_shift:\n num = math.fmod(num + dim_shift, 1.0)\n dim_sequence.append(num)\n return dim_sequence\n\n\nMatrix = List[List[int]]\n\n\ndef generate_sequence(num_samples: int,\n num_dims: int,\n skip: int = 100,\n per_dim_shift: bool = True,\n shuffle_sequence: bool = True,\n primes: Sequence[int] = None,\n shuffled_seed_sequence: Matrix = None) -> Matrix:\n \"\"\"Generate `num_samples` from a Halton sequence of dimension `num_dims`.\n\n Each dimension is generated independently from a shuffled Van der Corput\n sequence with a different base prime, and an optional shift added. The\n generated points are, by default, shuffled before returning.\n\n Args:\n num_samples: int, the number of samples to generate.\n num_dims: int, the number of dimensions per generated sample.\n skip: non-negative int, if positive then a sequence is generated and the\n first `skip` samples are discarded in order to avoid unwanted\n correlations.\n per_dim_shift: boolean, if true then each dim in the sequence is shifted by\n a random float (and then passed through fmod(n, 1.0) to keep in the range\n [0, 1)).\n shuffle_sequence: boolean, if true then shuffle the sequence before\n returning.\n primes: An optional sequence (of length `num_dims`) of prime numbers to use\n as the base for the Van der Corput sequence for each dimension. Useful for\n deterministic testing.\n shuffled_seed_sequence: An optional list of length `num_dims`, with each\n element being a sequence of length `primes[d]`, used as the input sequence\n to the Van der Corput sequence for each dimension. Useful for\n deterministic testing.\n\n Returns:\n A shuffled Halton sequence of length `num_samples`, where each sample has\n `num_dims` dimensions, and optionally a shift added to each dimension.\n\n Raises:\n ValueError: if `skip` is negative.\n ValueError: if `primes` is provided and not of length `num_dims`.\n ValueError: if `shuffled_seed_sequence` is provided and not of length\n `num_dims`.\n ValueError: if `shuffled_seed_sequence[d]` is provided and not of length\n `primes[d]` for any d in range(num_dims).\n \"\"\"\n if skip < 0:\n raise ValueError(f'Skip must be non-negative, received: {skip}.')\n\n if primes is not None and len(primes) != num_dims:\n raise ValueError(\n 'If passing in a sequence of primes it must be the same length as '\n f'num_dims={num_dims}, received {primes} (len {len(primes)}).')\n\n if shuffled_seed_sequence is not None:\n if len(shuffled_seed_sequence) != num_dims:\n raise ValueError(\n 'If passing in `shuffled_seed_sequence` it must be the same length '\n f'as num_dims={num_dims}, received {shuffled_seed_sequence} '\n f'(len {len(shuffled_seed_sequence)}).')\n for d in range(num_dims):\n if len(shuffled_seed_sequence[d]) != primes[d]:\n raise ValueError(\n 'If passing in `shuffled_seed_sequence` it must have element `{d}` '\n 'be a sequence of length `primes[{d}]`={expected}, received '\n '{actual} (len {length})'.format(\n d=d,\n expected=primes[d],\n actual=shuffled_seed_sequence[d],\n length=shuffled_seed_sequence[d]))\n\n if primes is None:\n primes = []\n prime_attempts = 1\n while len(primes) < num_dims + 1:\n primes = generate_primes(1000 * prime_attempts)\n prime_attempts += 1\n primes = primes[-num_dims - 1:-1]\n\n # Skip the first `skip` points in the sequence because they can have unwanted\n # correlations.\n num_samples += skip\n\n halton_sequence = []\n for d in range(num_dims):\n if shuffled_seed_sequence is None:\n dim_shuffled_seed_sequence = None\n else:\n dim_shuffled_seed_sequence = shuffled_seed_sequence[d]\n dim_sequence = _generate_dim(\n num_samples=num_samples,\n base=primes[d],\n shuffled_seed_sequence=dim_shuffled_seed_sequence,\n per_dim_shift=per_dim_shift)\n dim_sequence = dim_sequence[skip:]\n halton_sequence.append(dim_sequence)\n\n # Transpose the 2-D list to be shape [num_samples, num_dims].\n halton_sequence = list(zip(*halton_sequence))\n\n # Shuffle the sequence.\n if shuffle_sequence:\n random.shuffle(halton_sequence)\n return halton_sequence\n\n\ndef _generate_double_point(name: Text,\n min_val: float,\n max_val: float,\n scaling: Text,\n halton_point: float) -> Tuple[str, float]:\n \"\"\"Generate a float hyperparameter value from a Halton sequence point.\"\"\"\n if scaling not in ['linear', 'log']:\n raise ValueError(\n 'Only log or linear scaling is supported for floating point '\n f'parameters. Received {scaling}.')\n if scaling == 'log':\n # To transform from [0, 1] to [min_val, max_val] on a log scale we do:\n # min_val * exp(x * log(max_val / min_val)).\n rescaled_value = (\n min_val * math.exp(halton_point * math.log(max_val / min_val)))\n else:\n rescaled_value = halton_point * (max_val - min_val) + min_val\n return name, rescaled_value\n\n\ndef _generate_discrete_point(name: str,\n feasible_points: Sequence[Any],\n halton_point: float) -> Any:\n \"\"\"Generate a discrete hyperparameter value from a Halton sequence point.\"\"\"\n index = int(math.floor(halton_point * len(feasible_points)))\n return name, feasible_points[index]\n\n\n_DiscretePoints = collections.namedtuple('_DiscretePoints', 'feasible_points')\n\n\ndef discrete(feasible_points: Sequence[Any]) -> _DiscretePoints:\n return _DiscretePoints(feasible_points)\n\n\ndef interval(start: int, end: int) -> Tuple[int, int]:\n return start, end\n\n\ndef loguniform(name: Text, range_endpoints: Tuple[int, int]) -> _GeneratorFn:\n min_val, max_val = range_endpoints\n return functools.partial(_generate_double_point,\n name,\n min_val,\n max_val,\n 'log')\n\n\ndef uniform(\n name: Text, search_points: Union[_DiscretePoints,\n Tuple[int, int]]) -> _GeneratorFn:\n if isinstance(search_points, _DiscretePoints):\n return functools.partial(_generate_discrete_point,\n name,\n search_points.feasible_points)\n\n min_val, max_val = search_points\n return functools.partial(_generate_double_point,\n name,\n min_val,\n max_val,\n 'linear')\n\n\ndef product(sweeps: Sequence[_SweepSequence]) -> _SweepSequence:\n \"\"\"Cartesian product of a list of hyperparameter generators.\"\"\"\n # A List[Dict] of hyperparameter names to sweep values.\n hyperparameter_sweep = []\n for hyperparameter_index in range(len(sweeps)):\n hyperparameter_sweep.append([])\n # Keep iterating until the iterator in sweep() ends.\n sweep_i = sweeps[hyperparameter_index]\n for point_index in range(len(sweep_i)):\n hyperparameter_name, value = list(sweep_i[point_index].items())[0]\n hyperparameter_sweep[-1].append((hyperparameter_name, value))\n return list(map(dict, itertools.product(*hyperparameter_sweep)))\n\n\ndef sweep(name, feasible_points: Sequence[Any]) -> _SweepSequence:\n return [{name: x} for x in feasible_points.feasible_points]\n\n\ndef zipit(generator_fns_or_sweeps: Sequence[Union[_GeneratorFn,\n _SweepSequence]],\n length: int) -> _SweepSequence:\n \"\"\"Zip together a list of hyperparameter generators.\n\n Args:\n generator_fns_or_sweeps: A sequence of either:\n - Generator functions that accept a Halton sequence point and return a\n quasi-ranom sample, such as those returned by halton.uniform() or\n halton.loguniform()\n - Lists of dicts with one key/value such as those returned by\n halton.sweep()\n We need to support both of these (instead of having halton.sweep() return\n a list of generator functions) so that halton.sweep() can be used directly\n as a list.\n length: the number of hyperparameter points to generate. If any of the\n elements in generator_fns_or_sweeps are sweep lists, and their length is\n less than `length`, the sweep generation will be terminated and will be\n the same length as the shortest sweep sequence.\n\n Returns:\n A list of dictionaries, one for each trial, with a key for each unique\n hyperparameter name from generator_fns_or_sweeps.\n \"\"\"\n halton_sequence = generate_sequence(\n num_samples=length, num_dims=len(generator_fns_or_sweeps))\n # A List[Dict] of hyperparameter names to sweep values.\n hyperparameter_sweep = []\n for trial_index in range(length):\n hyperparameter_sweep.append({})\n for hyperparameter_index in range(len(generator_fns_or_sweeps)):\n halton_point = halton_sequence[trial_index][hyperparameter_index]\n if callable(generator_fns_or_sweeps[hyperparameter_index]):\n generator_fn = generator_fns_or_sweeps[hyperparameter_index]\n hyperparameter_name, value = generator_fn(halton_point)\n else:\n sweep_list = generator_fns_or_sweeps[hyperparameter_index]\n if trial_index > len(sweep_list):\n break\n hyperparameter_point = sweep_list[trial_index]\n hyperparameter_name, value = list(hyperparameter_point.items())[0]\n hyperparameter_sweep[trial_index][hyperparameter_name] = value\n return hyperparameter_sweep\n\n\n_DictSearchSpace = Dict[str, Dict[str, Union[str, float, Sequence]]]\n_ListSearchSpace = List[Dict[str, Union[str, float, Sequence]]]\n\n\ndef generate_search(search_space: Union[_DictSearchSpace, _ListSearchSpace],\n num_trials: int) -> List[collections.namedtuple]:\n \"\"\"Generate a random search with the given bounds and scaling.\n\n Args:linear\n search_space: A dict where the keys are the hyperparameter names, and the\n values are a dict of:\n - {\"min\": x, \"max\": y, \"scaling\": z} where x and y are floats and z is\n one of \"linear\" or \"log\"\n - {\"feasible_points\": [...]} for discrete hyperparameters.\n Alternatively, it can be a list of dict where keys are the hyperparameter\n names, and the values are hyperparameters.\n num_trials: the number of hyperparameter points to generate.\n\n Returns:\n A list of length `num_trials` of namedtuples, each of which has attributes\n corresponding to the given hyperparameters, and values randomly sampled.\n \"\"\"\n if isinstance(search_space, dict):\n all_hyperparameter_names = list(search_space.keys())\n elif isinstance(search_space, list):\n assert len(search_space) > 0\n all_hyperparameter_names = list(search_space[0].keys())\n else:\n raise AttributeError('tuning_search_space should either be a dict or list.')\n\n named_tuple_class = collections.namedtuple('Hyperparameters',\n all_hyperparameter_names)\n\n if isinstance(search_space, dict):\n hyperparameter_generators = []\n for name, space in search_space.items():\n if 'feasible_points' in space: # Discrete search space.\n generator_fn = uniform(name, discrete(space['feasible_points']))\n else: # Continuous space.\n if space['scaling'] == 'log':\n generator_fn = loguniform(name, interval(space['min'], space['max']))\n else:\n generator_fn = uniform(name, interval(space['min'], space['max']))\n hyperparameter_generators.append(generator_fn)\n return [\n named_tuple_class(**p)\n for p in zipit(hyperparameter_generators, num_trials)\n ]\n else:\n hyperparameters = []\n updated_num_trials = min(num_trials, len(search_space))\n if num_trials != len(search_space):\n logging.info(f'--num_tuning_trials was set to {num_trials}, but '\n f'{len(search_space)} trial(s) found in the JSON file. '\n f'Updating --num_tuning_trials to {updated_num_trials}.')\n for trial in search_space:\n hyperparameters.append(named_tuple_class(**trial))\n return hyperparameters[:updated_num_trials]\n","repo_name":"mlcommons/algorithmic-efficiency","sub_path":"algorithmic_efficiency/halton.py","file_name":"halton.py","file_ext":"py","file_size_in_byte":15224,"program_lang":"python","lang":"en","doc_type":"code","stars":212,"dataset":"github-code","pt":"91"}
+{"seq_id":"3981538626","text":"#coding=utf-8\nimport re\nimport time\nimport unittest\nfrom selenium import webdriver\nfrom selenium.common.exceptions import ElementNotVisibleException, NoSuchElementException\nfrom util.decorator.PhoneModelDecorator import set_phone_model,options\nfrom appModules.LoginAction import LoginAction\nfrom config.VarConfig import *\nfrom pageObjects.BookCityPage import BookCityPage\nfrom pageObjects.ClassificationPage import ClassificationPage\nfrom pageObjects.CollectionPage import CollectionPage\nfrom pageObjects.DiscountPage import DiscountPage\nfrom pageObjects.FreebookPage import FreebookPage\nfrom pageObjects.LeaderboardPage import LeaderboardPage\nfrom pageObjects.MinePage import MinePage\nfrom pageObjects.MyBooksPage import MyBooksPage\nfrom pageObjects.ShoppingCart import ShoppingCartPage\nfrom pageObjects.WeeklyDiscountPage import WeeklyDiscountPage\nfrom util.Log import *\nfrom util.ParseConfigurationFile import ParseConfigFile\nfrom config.accounts import *\nfrom util.ObjectMap import *\nfrom util.ParseExcel import ParseExcel\nfrom util.getApiData import GetCollectionBooks\n\n\nclass TestBookCity(unittest.TestCase):\n excelObj = ParseExcel(dataFilePath)\n\n @classmethod\n @set_phone_model(phone_model)\n def setUpClass(cls) -> None:\n TestBookCity.browser = webdriver.Chrome(chrome_options=options)\n logger.info(\"开始登陆...\")\n TestBookCity.browser.get(\"https://plogin.m.jd.com/user/login.action\")\n TestBookCity.login_cookie = LoginAction.login(not_vip_username, not_vip_password, TestBookCity.browser)\n\n @classmethod\n def tearDownClass(cls) -> None:\n TestBookCity.browser.quit()\n\n def setUp(self) -> None:\n pass\n\n def tearDown(self) -> None:\n pass\n def test_More_Tools_Button(self):\n logger.info(\"开始执行唤起更多工具按钮脚本...\")\n pc = ParseConfigFile()\n\n try:\n\n TestBookCity.browser.get(pc.getUrl('bookcity'))\n\n logger.info('启动浏览器，访问\"书城\"页面...')\n bookcityPage = BookCityPage(TestBookCity.browser)\n\n logger.info('点击右上角更多按钮...')\n bookcityPage.toolButtonObj().click()\n\n try:\n\n self.assertIs(bookcityPage.firstPageButtonObj().is_displayed(), True)\n logger.info('调起菜单成功！用例通过')\n\n except AssertionError as e:\n logger.debug('更多按钮操作失败！用例不通过')\n raise e\n\n\n except ElementNotVisibleException as e:\n\n logger.error(\"数据问题，元素没有找到..\")\n\n except NoSuchElementException as e:\n\n logger.error(\"数据问题..重试\")\n\n except Exception as e:\n\n logger.error(e)\n\n raise e\n\n def test_Switch_Channel_Button(self):\n logger.info(\"开始执行频道切换按钮脚本...\")\n pc = ParseConfigFile()\n\n try:\n\n TestBookCity.browser.get(pc.getUrl('bookcity'))\n\n logger.info('启动浏览器，访问\"书城\"页面...')\n bookcityPage = BookCityPage(TestBookCity.browser)\n\n logger.info('点击频道折叠按钮...')\n bookcityPage.channelFoldDownButton().click()\n bookcityPage.channelVIPButton().click()\n TestBookCity.browser.refresh()\n titleText=bookcityPage.VIPText().text\n\n try:\n\n self.assertIs( '开通京东读书VIP，海量书籍免费借阅' in titleText, True)\n logger.info('切换频道成功！用例通过')\n\n except AssertionError as e:\n logger.debug('切换频道失败！用例不通过')\n raise e\n\n\n except ElementNotVisibleException as e:\n\n logger.error(\"数据问题，元素没有找到..\")\n\n except NoSuchElementException as e:\n\n logger.error(\"数据问题..重试\")\n\n except Exception as e:\n\n logger.error(e)\n\n raise e\n\n def test_Check_Domain(self):\n logger.info(\"开始执行频道切换按钮脚本...\")\n pc = ParseConfigFile()\n\n try:\n\n TestBookCity.browser.get(pc.getUrl('bookcity'))\n\n logger.info('启动浏览器，访问\"书城\"页面...')\n bookcityPage = BookCityPage(TestBookCity.browser)\n\n domainname=bookcityPage.domainName().text\n domainurl=bookcityPage.domainUrl().text\n\n try:\n\n self.assertIs( ('e.m.jd.com' in domainurl) and ('京东读书' in domainname) , True)\n logger.info('检查域名成功！用例通过')\n\n except AssertionError as e:\n logger.debug('检查域名失败！用例不通过')\n raise e\n\n\n except ElementNotVisibleException as e:\n\n logger.error(\"数据问题，元素没有找到..\")\n\n except NoSuchElementException as e:\n\n logger.error(\"数据问题..重试\")\n\n except Exception as e:\n\n logger.error(e)\n\n raise e\n\n def test_More_Tools_Index_Button(self):\n logger.info(\"开始执行更多按钮首页按钮脚本...\")\n pc = ParseConfigFile()\n\n try:\n\n TestBookCity.browser.get(pc.getUrl('bookcity'))\n\n bookcityPage = BookCityPage(TestBookCity.browser)\n bookcityPage.toolButtonObj().click()\n index_image=bookcityPage.indexButtonImageInMoreTools()\n index_button=bookcityPage.firstPageButtonObj()\n if index_image.is_displayed() and index_button.is_displayed():\n index_button.click()\n\n # my_books_image=bookcityPage.myBooksButtonImageInMoreTools()\n # shop_cart_image=bookcityPage.shoppingCartButtonImageInMoreTools()\n # mine_image=bookcityPage.mineButtonImageInMoreTools()\n\n try:\n\n self.assertIs(bookcityPage.domainName().is_displayed(), True)\n\n logger.info('书城首页按钮点击成功！用例通过')\n\n except AssertionError as e:\n logger.debug('书城首页按钮点击失败！用例不通过')\n raise e\n\n except ElementNotVisibleException as e:\n\n logger.error(\"数据问题，元素没有找到..\")\n raise e\n\n\n except NoSuchElementException as e:\n\n logger.error(\"数据问题..重试\")\n raise e\n\n\n except Exception as e:\n\n logger.error(e)\n\n raise e\n\n def test_More_Tools_My_Books_Button(self):\n logger.info(\"开始执行更多按钮-【我的书籍】按钮脚本...\")\n pc = ParseConfigFile()\n\n try:\n\n TestBookCity.browser.get(pc.getUrl('bookcity'))\n\n bookcityPage = BookCityPage(TestBookCity.browser)\n bookcityPage.toolButtonObj().click()\n my_books_image=bookcityPage.myBooksButtonImageInMoreTools()\n my_books_button = bookcityPage.myBookPageButtonObj()\n if my_books_image.is_displayed() and my_books_button.is_displayed():\n my_books_button.click()\n mybooksPage=MyBooksPage(TestBookCity.browser)\n # mybooks_title=mybooksPage.mybooksTitle()\n time.sleep(5)\n print(TestBookCity.browser.page_source)\n\n try:\n\n self.assertIs(mybooksPage.mybooksTitle().is_displayed(), True)\n\n logger.info('书城更多按钮-【我的书籍】点击成功！用例通过')\n\n except AssertionError as e:\n logger.debug('书城更多按钮-【我的书籍】点击失败！用例不通过')\n raise e\n\n except ElementNotVisibleException as e:\n\n logger.error(\"数据问题，元素没有找到..\")\n raise e\n\n\n except NoSuchElementException as e:\n\n logger.error(\"数据问题..重试\")\n raise e\n\n\n except Exception as e:\n\n logger.error(e)\n\n raise e\n\n def test_More_Tools_Shopping_Cart_Button(self):\n logger.info(\"开始执行更多按钮-【购物车】按钮脚本...\")\n pc = ParseConfigFile()\n\n try:\n\n TestBookCity.browser.get(pc.getUrl('bookcity'))\n\n bookcityPage = BookCityPage(TestBookCity.browser)\n bookcityPage.toolButtonObj().click()\n\n shopping_cart_image = bookcityPage.shoppingCartButtonImageInMoreTools()\n shopping_cart_button = bookcityPage.shoppingCartPageButtonObj()\n if shopping_cart_image.is_displayed() and shopping_cart_button.is_displayed():\n shopping_cart_button.click()\n # shop_cart_image=bookcityPage.shoppingCartButtonImageInMoreTools()\n # mine_image=bookcityPage.mineButtonImageInMoreTools()\n shoppingCartPage = ShoppingCartPage(TestBookCity.browser)\n time.sleep(5)\n\n\n try:\n\n self.assertIs(shoppingCartPage.shoppingCartTitle().is_displayed(), True)\n\n logger.info('书城更多按钮-【购物车】点击成功！用例通过')\n\n except AssertionError as e:\n logger.debug('书城更多按钮-【购物车】点击失败！用例不通过')\n raise e\n\n except ElementNotVisibleException as e:\n\n logger.error(\"数据问题，元素没有找到..\")\n raise e\n\n\n except NoSuchElementException as e:\n\n logger.error(\"数据问题..重试\")\n raise e\n\n\n except Exception as e:\n\n logger.error(e)\n\n raise e\n\n def test_More_Tools_Mine_Button(self):\n logger.info(\"开始执行更多按钮-【我的】按钮脚本...\")\n pc = ParseConfigFile()\n\n try:\n\n TestBookCity.browser.get(pc.getUrl('bookcity'))\n\n bookcityPage = BookCityPage(TestBookCity.browser)\n bookcityPage.toolButtonObj().click()\n\n mine_image = bookcityPage.mineButtonImageInMoreTools()\n mine_button = bookcityPage.minePageButtonObj()\n if mine_image.is_displayed() and mine_button.is_displayed():\n mine_button.click()\n minePage = MinePage(TestBookCity.browser)\n time.sleep(5)\n\n try:\n\n self.assertIs(minePage.title().is_displayed(), True)\n\n logger.info('书城更多按钮-【我的】点击成功！用例通过')\n\n except AssertionError as e:\n logger.debug('书城更多按钮-【我的】点击失败！用例不通过')\n raise e\n\n except ElementNotVisibleException as e:\n\n logger.error(\"数据问题，元素没有找到..\")\n raise e\n\n\n except NoSuchElementException as e:\n\n logger.error(\"数据问题..重试\")\n raise e\n\n\n except Exception as e:\n\n logger.error(e)\n\n raise e\n\n def test_Banner_Click(self):\n logger.info(\"开始执行书城banner点击脚本...\")\n pc = ParseConfigFile()\n\n try:\n\n TestBookCity.browser.get(pc.getUrl('bookcity'))\n\n logger.info('启动浏览器，访问\"书城\"页面...')\n bookcityPage = BookCityPage(TestBookCity.browser)\n\n bookcityPage.channelFoldDownButton().click()\n time.sleep(2)\n bookcityPage.channelNewEraButton().click()\n time.sleep(2)\n bookcityPage.newEraBanner().click()\n time.sleep(5)\n collectionPage=CollectionPage(TestBookCity.browser)\n titleElement=collectionPage.title()\n\n try:\n\n self.assertIs(titleElement.is_displayed(), True)\n logger.info('书城banner点击成功！用例通过')\n\n except AssertionError as e:\n logger.debug('书城banner点击失败！用例不通过')\n raise e\n\n\n except ElementNotVisibleException as e:\n\n logger.error(\"数据问题，元素没有找到..\")\n\n except NoSuchElementException as e:\n\n logger.error(\"数据问题..重试\")\n\n except Exception as e:\n\n logger.error(e)\n\n raise e\n\n def test_Leaderboard_Button_Click(self):\n logger.info(\"开始执行五个圈-排行按钮点击脚本...\")\n pc = ParseConfigFile()\n\n try:\n\n TestBookCity.browser.get(pc.getUrl('bookcity'))\n\n logger.info('启动浏览器，访问\"书城\"页面...')\n bookcityPage = BookCityPage(TestBookCity.browser)\n bookcityPage.channelFoldDownButton().click()\n time.sleep(2)\n bookcityPage.channelFeaturedButton().click()\n time.sleep(2)\n bookcityPage.leaderBoardButton().click()\n\n time.sleep(2)\n leaderboardPage=LeaderboardPage(TestBookCity.browser)\n titleElement=leaderboardPage.title()\n title_text=titleElement.text\n\n try:\n\n self.assertIs('排行榜'==title_text, True)\n logger.info('书城五个圈-排行按钮点击成功！用例通过')\n\n except AssertionError as e:\n logger.debug('书城五个圈-排行按钮点击失败！用例不通过')\n raise e\n\n\n except ElementNotVisibleException as e:\n\n logger.error(\"数据问题，元素没有找到..\")\n\n except NoSuchElementException as e:\n\n logger.error(\"数据问题..重试\")\n\n except Exception as e:\n\n logger.error(e)\n\n raise e\n\n def test_Classification_Button_Click(self):\n logger.info(\"开始执行五个圈-分类按钮点击脚本...\")\n pc = ParseConfigFile()\n\n try:\n\n TestBookCity.browser.get(pc.getUrl('bookcity'))\n\n logger.info('启动浏览器，访问\"书城\"页面...')\n bookcityPage = BookCityPage(TestBookCity.browser)\n bookcityPage.channelFoldDownButton().click()\n time.sleep(2)\n bookcityPage.channelFeaturedButton().click()\n time.sleep(2)\n bookcityPage.classificationButton().click()\n\n time.sleep(2)\n classificationPage=ClassificationPage(TestBookCity.browser)\n titleElement=classificationPage.title()\n title_text=titleElement.text\n\n try:\n\n self.assertIs('分类'==title_text, True)\n logger.info('书城五个圈-分类按钮点击成功！用例通过')\n\n except AssertionError as e:\n logger.debug('书城五个圈-分类按钮点击失败！用例不通过')\n raise e\n\n\n except ElementNotVisibleException as e:\n\n logger.error(\"数据问题，元素没有找到..\")\n\n except NoSuchElementException as e:\n\n logger.error(\"数据问题..重试\")\n\n except Exception as e:\n\n logger.error(e)\n\n raise e\n\n def test_Discount_Button_Click(self):\n logger.info(\"开始执行五个圈-特价按钮点击脚本...\")\n pc = ParseConfigFile()\n\n try:\n\n TestBookCity.browser.get(pc.getUrl('bookcity'))\n\n logger.info('启动浏览器，访问\"书城\"页面...')\n bookcityPage = BookCityPage(TestBookCity.browser)\n bookcityPage.channelFoldDownButton().click()\n time.sleep(2)\n\n bookcityPage.channelFeaturedButton().click()\n time.sleep(2)\n bookcityPage.discountButton().click()\n\n time.sleep(2)\n discountPage=DiscountPage(TestBookCity.browser)\n titleElement=discountPage.title()\n title_text=titleElement.text\n\n try:\n\n # self.assertIs('特价专区'==title_text , True)\n result1='特价专区'==title_text\n result2 = re.search(r'\\d+件[\\s\\S]+元', title_text)\n result3 = \"双周特价\"==title_text\n if result1:\n self.assertIs('特价专区' == title_text, True)\n elif None!=result2:\n self.assertIs(None!=result2,True)\n\n logger.info('书城五个圈-特价按钮点击成功！用例通过')\n\n except AssertionError as e:\n logger.debug('书城五个圈-特价按钮点击失败！用例不通过')\n raise e\n\n\n except ElementNotVisibleException as e:\n\n logger.error(\"数据问题，元素没有找到..\")\n\n except NoSuchElementException as e:\n\n logger.error(\"数据问题..重试\")\n\n except Exception as e:\n\n logger.error(e)\n\n raise e\n\n def test_Freebook_Button_Click(self):\n logger.info(\"开始执行五个圈-免费按钮点击脚本...\")\n pc = ParseConfigFile()\n\n try:\n\n TestBookCity.browser.get(pc.getUrl('bookcity'))\n\n logger.info('启动浏览器，访问\"书城\"页面...')\n bookcityPage = BookCityPage(TestBookCity.browser)\n bookcityPage.channelFoldDownButton().click()\n time.sleep(2)\n bookcityPage.channelFeaturedButton().click()\n time.sleep(2)\n bookcityPage.freebookButton().click()\n\n time.sleep(2)\n freePage=FreebookPage(TestBookCity.browser)\n time.sleep(2)\n titleElement=freePage.title()\n title_text=titleElement.text\n\n try:\n\n self.assertIs('免费专区'==title_text, True)\n logger.info('书城五个圈-免费按钮点击成功！用例通过')\n\n except AssertionError as e:\n logger.debug('书城五个圈-免费按钮点击失败！用例不通过')\n raise e\n\n\n except ElementNotVisibleException as e:\n\n logger.error(\"数据问题，元素没有找到..\")\n\n except NoSuchElementException as e:\n\n logger.error(\"数据问题..重试\")\n\n except Exception as e:\n\n logger.error(e)\n\n raise e\n\n def test_VIP_Button_Click(self):\n logger.info(\"开始执行五个圈-VIP按钮点击脚本...\")\n pc = ParseConfigFile()\n\n try:\n\n TestBookCity.browser.get(pc.getUrl('bookcity'))\n\n logger.info('启动浏览器，访问\"书城\"页面...')\n bookcityPage = BookCityPage(TestBookCity.browser)\n bookcityPage.channelFoldDownButton().click()\n time.sleep(2)\n bookcityPage.vipButton().click()\n time.sleep(2)\n titleElement=bookcityPage.VIPText()\n\n try:\n\n self.assertIs(titleElement.is_displayed(), True)\n logger.info('书城五个圈-VIP按钮点击成功！用例通过')\n\n except AssertionError as e:\n logger.debug('书城五个圈-VIP按钮点击失败！用例不通过')\n raise e\n\n\n except ElementNotVisibleException as e:\n\n logger.error(\"数据问题，元素没有找到..\")\n\n except NoSuchElementException as e:\n\n logger.error(\"数据问题..重试\")\n\n except Exception as e:\n\n logger.error(e)\n\n raise e\n\n @set_phone_model(phone_model)\n def test_Discount_Collection_Title(self):\n logger.info(\"开始执行本周特价title点击脚本...\")\n pc = ParseConfigFile()\n\n try:\n\n browser = webdriver.Chrome(chrome_options=options)\n\n browser.get(pc.getUrl('bookcity'))\n\n logger.info('启动浏览器，访问\"书城\"页面...')\n bookcityPage = BookCityPage(browser)\n\n collectionName=bookcityPage.firstCollectionTitle().text\n counterHour=bookcityPage.firstCollectionCounterHour().text\n counterMinute=bookcityPage.firstCollectionCounterMinute().text\n\n checkHour= str(24-int(time.localtime().tm_hour)-1).zfill(2)\n checkMinute=str(60-int(time.localtime().tm_min)-1).zfill(2)\n\n bookcityPage.firstCollectionTitle().click()\n time.sleep(5)\n\n weeklyDiscountTitleText=WeeklyDiscountPage(browser).title().text\n\n try:\n\n self.assertIs(checkHour==counterHour, True)\n self.assertIs(checkMinute==counterMinute, True)\n # self.assertIs(\"本周特价\"==collectionName,True)\n # self.assertIs(\"本周特价\"==weeklyDiscountTitleText,True)\n\n\n logger.info('书城本周特价title点击成功！用例通过')\n\n except AssertionError as e:\n logger.debug('书城本周特价title点击失败！用例不通过')\n raise e\n\n\n except ElementNotVisibleException as e:\n\n logger.error(\"数据问题，元素没有找到..\")\n raise e\n\n\n except NoSuchElementException as e:\n\n logger.error(\"数据问题..重试\")\n raise e\n\n\n except Exception as e:\n\n logger.error(e)\n\n raise e\n\n @set_phone_model(phone_model)\n def test_Discount_Collection_List(self):\n logger.info(\"开始执行本周特价列表检查脚本...\")\n pc = ParseConfigFile()\n\n try:\n\n browser = webdriver.Chrome(chrome_options=options)\n\n browser.get(pc.getUrl('bookcity'))\n\n logger.info('启动浏览器，访问\"��城\"页面...')\n bookcityPage = BookCityPage(browser)\n\n\n page_book_set=set([bookcityPage.firstCollectionFirstBookName().text.replace(\" \",\"\"),\n bookcityPage.firstCollectionSecondBookName().text.replace(\" \",\"\"),\n bookcityPage.firstCollectionThirdBookName().text.replace(\" \",\"\")])\n collectionName=bookcityPage.firstCollectionTitle().text\n api_book_set=set(GetCollectionBooks.getBookNameDataList(collectionName))\n # print(page_book_set)\n # print(api_book_set)\n booklist_check=page_book_set.issubset(api_book_set)\n\n # api_firstbook_jd_price= str(int(float(GetCollectionBooks.getDataList(collectionName)[0]['jd_price']) * 100)) + \"阅豆\"\n # api_firstbook_price= str(int(float(GetCollectionBooks.getDataList(collectionName)[0]['price']) * 100)) + \"阅豆\"\n api_firstbook_jd_price = str(round(GetCollectionBooks.getDataList(collectionName)[0]['jd_price'],2))\n api_firstbook_price = str(round(GetCollectionBooks.getDataList(collectionName)[0]['price'],2))\n page_firstbook_jd_price=str(round(float(bookcityPage.firstCollectionFirstBookNewPrice().text.strip('￥')),2))\n page_firstbook_price=str(round(float(bookcityPage.firstCollectionFirstBookOldPrice().text.strip('￥')),2))\n\n\n try:\n\n self.assertIs(booklist_check, True)\n self.assertIs((api_firstbook_jd_price==page_firstbook_jd_price)and(api_firstbook_price ==page_firstbook_price),True)\n\n logger.info('书城本周特价列表检查成功！用例通过')\n\n except AssertionError as e:\n logger.debug('书城本周特价列表检查失败！用例不通过')\n raise e\n\n\n except ElementNotVisibleException as e:\n\n logger.error(\"数据问题，元素没有找到..\")\n\n except NoSuchElementException as e:\n\n logger.error(\"数据问题..重试\")\n\n except Exception as e:\n\n logger.error(e)\n\n raise e\n\n def test_First_Collection_Book_Click_Redirect(self):\n logger.info(\"开始执行本周特价书籍点击脚本...\")\n pc = ParseConfigFile()\n\n try:\n\n TestBookCity.browser.get(pc.getUrl('bookcity'))\n\n logger.info('启动浏览器，访问\"书城\"页面...')\n bookcityPage = BookCityPage(TestBookCity.browser)\n bookcityPage.channelFoldDownButton().click()\n time.sleep(2)\n bookcityPage.channelFeaturedButton().click()\n time.sleep(2)\n page_first_book_name=bookcityPage.firstCollectionFirstBookName().text\n bookcityPage.firstCollectionFirstBookName().click()\n time.sleep(3)\n book_detail_page_title=TestBookCity.browser.title\n\n try:\n\n self.assertIs(page_first_book_name==book_detail_page_title, True)\n\n logger.info('书城本周特价书籍点击成功！用例通过')\n\n except AssertionError as e:\n logger.debug('书城本周特价书籍点击失败！用例不通过')\n raise e\n\n\n except ElementNotVisibleException as e:\n\n logger.error(\"数据问题，元素没有找到..\")\n\n except NoSuchElementException as e:\n\n logger.error(\"数据问题..重试\")\n\n except Exception as e:\n\n logger.error(e)\n\n raise e\n\n def test_First_Collection_Detail_List(self):\n logger.info(\"开始执行本周特价合集页列表检查脚本...\")\n pc = ParseConfigFile()\n\n try:\n\n TestBookCity.browser.get(pc.getUrl('bookcity'))\n\n logger.info('启动浏览器，访问\"书城\"页面...')\n bookcityPage = BookCityPage(TestBookCity.browser)\n bookcityPage.channelFoldDownButton().click()\n time.sleep(2)\n bookcityPage.channelFeaturedButton().click()\n time.sleep(2)\n collectionName=bookcityPage.firstCollectionTitle().text\n\n bookcityPage.firstCollectionTitle().click()\n\n collectionPage=CollectionPage(TestBookCity.browser)\n book_element_list=collectionPage.bookNameList()\n book_name_set=set([e.text.replace(\" \",\"\") for e in book_element_list if e.text!=''])\n api_book_name_set = set(GetCollectionBooks.getBookNameDataList(collectionName))\n book_name_set_check = book_name_set.issubset(api_book_name_set)\n\n api_thirdbook_name=GetCollectionBooks.getDataList(collectionName)[2]['name']\n api_thirdbook_old_price= str(GetCollectionBooks.getDataList(collectionName)[2]['price'])\n api_thirdbook_cur_price= str(GetCollectionBooks.getDataList(collectionName)[2]['jd_price'])\n api_thirdbook_catagory=GetCollectionBooks.getDataList(collectionName)[2]['category_third'][0]\n\n page_thirdbook_name =collectionPage.bookNameList()[2].text\n # page_thirdbook_old_price =collectionPage.bookOldPriceList()[2].text.strip('￥')\n # page_thirdbook_cur_price =collectionPage.bookCurPriceList()[2].text.strip('￥')\n\n\n try:\n\n self.assertIs(book_name_set_check,True)\n self.assertIs(api_thirdbook_name==page_thirdbook_name,True)\n # self.assertIs(api_thirdbook_catagory==page_thirdbook_catagory,True)\n # self.assertIs(api_thirdbook_cur_price==page_thirdbook_cur_price,True)\n # self.assertIs(api_thirdbook_old_price==page_thirdbook_old_price,True)\n\n logger.info('书城本周特价合集页列表检查脚本成功！用例通过')\n\n except AssertionError as e:\n logger.debug('书城本周特价合集页列表检查脚本失败！用例不通过')\n raise e\n\n\n except ElementNotVisibleException as e:\n\n logger.error(\"数据问题，元素没有找到..\")\n\n except NoSuchElementException as e:\n\n logger.error(\"数据问题..重试\")\n\n except Exception as e:\n\n logger.error(e)\n\n raise e\n\n # def test_Three_books_Collections_List(self):\n # logger.info(\"开始执行其余所有合集页列表检查脚本...\")\n # pc = ParseConfigFile()\n #\n # try:\n #\n # TestBookCity.browser.get(pc.getUrl('bookcity'))\n #\n # logger.info('启动浏览器，访问\"书城\"页面...')\n # bookcityPage = BookCityPage(TestBookCity.browser)\n # bookcityPage.channelFoldDownButton().click()\n # time.sleep(2)\n # bookcityPage.channelFeaturedButton().click()\n # time.sleep(2)\n # title_elements = bookcityPage.allBooksCollectionTitles()\n # # list_elements = bookcityPage.allBooksCollectionLists()\n # first_book_names=bookcityPage.allCollectionFirstBookNames()\n # second_book_names=bookcityPage.allCollectionSecondBookNames()\n # third_book_names=bookcityPage.allCollectionThirdBookNames()\n #\n # for i in range(1,len(title_elements)):\n # # bookcityPage = BookCityPage(browser)\n # # title_elements = bookcityPage.threeBooksCollectionTitles()\n # title=title_elements[i]\n # # time.sleep(2)\n # TestBookCity.browser.execute_script(\"arguments[0].scrollIntoView();\" ,title)\n # collectionName=title.text\n #\n # page_book_set = set([first_book_names[i].text.replace(\" \",\"\"),\n # second_book_names[i].text.replace(\" \",\"\"),\n # third_book_names[i].text.replace(\" \",\"\")])\n # api_book_set = set(GetCollectionBooks.getBookNameDataList(collectionName))\n # booklist_check = page_book_set.issubset(api_book_set)\n #\n # try:\n #\n # self.assertIs(booklist_check,True)\n # logger.info('书城%s合集页列表检查脚本成功！用例通过'%collectionName)\n #\n # except AssertionError as e:\n # logger.debug('书城%s合集页列表检查脚本失败！用例不通过'%collectionName)\n # raise e\n #\n # except ElementNotVisibleException as e:\n #\n # logger.error(\"数据问题，元素没有找到..\")\n #\n # except NoSuchElementException as e:\n #\n # logger.error(\"数据问题..重试\")\n #\n # except Exception as e:\n #\n # logger.error(e)\n #\n # raise e\n #\n # #书城大多数的合集都属于此类\n # def test_Three_books_Collections_Detail_List(self):\n # logger.info(\"开始执行其余所有合集页列表检查脚本...\")\n # pc = ParseConfigFile()\n #\n # try:\n #\n # TestBookCity.browser.get(pc.getUrl('bookcity'))\n #\n # logger.info('启动浏览器，访问\"书城\"页面...')\n # bookcityPage = BookCityPage(TestBookCity.browser)\n # bookcityPage.channelFoldDownButton().click()\n # time.sleep(2)\n # bookcityPage.channelFeaturedButton().click()\n # time.sleep(2)\n # title_elements = bookcityPage.threeBooksCollectionTitles()\n #\n # for i in range(1,len(title_elements)):\n # bookcityPage = BookCityPage(TestBookCity.browser)\n # title_elements = bookcityPage.threeBooksCollectionTitles()\n # title=title_elements[i]\n # time.sleep(2)\n # TestBookCity.browser.execute_script(\"arguments[0].scrollIntoView();\" ,title)\n # collectionName=title.text\n # title.click()\n # collectionPage=CollectionPage(TestBookCity.browser)\n # book_element_list=collectionPage.bookNameList()\n # book_name_set=set([e.text.replace(\" \",\"\") for e in book_element_list if e.text!=''])\n #\n # api_book_name_set = set(GetCollectionBooks.getBookNameDataList(collectionName))\n # book_name_set_check = book_name_set.issubset(api_book_name_set)\n # api_thirdbook_name=GetCollectionBooks.getDataList(collectionName)[2]['name']\n # page_thirdbook_name =collectionPage.bookNameList()[2].text\n #\n # if api_book_name_set==None:\n # continue\n #\n #\n # try:\n #\n # self.assertIs(book_name_set_check,True)\n # self.assertIs(api_thirdbook_name==page_thirdbook_name,True)\n # logger.info('书城%s合集页列表检查脚本成功！用例通过'%collectionName)\n #\n # except AssertionError as e:\n # logger.debug('书城%s合集页列表检查脚本失败！用例不通过'%collectionName)\n # raise e\n # finally:\n # collectionPage.backButton().click()\n #\n #\n # except ElementNotVisibleException as e:\n #\n # logger.error(\"数据问题，元素没有找到..\")\n #\n # except NoSuchElementException as e:\n #\n # logger.error(\"数据问题..重试\")\n #\n # except Exception as e:\n #\n # logger.error(e)\n #\n # raise e\n\n\nif __name__==\"__main__\":\n # unittest.main()\n\n #通过多个测试集合组成一个测试套\n testsuit = unittest.TestSuite()\n testsuit.addTest(TestBookCity(\"test_More_Tools_Button\"))\n #运行测试套，verbosity=2说明输出每个测试用例运行的详细信息\n unittest.TextTestRunner(verbosity=2).run(testsuit)\n\n\n\n","repo_name":"yaqingirl/python-selenium-UI-automation-frame","sub_path":"testScripts/testBookCity.py","file_name":"testBookCity.py","file_ext":"py","file_size_in_byte":34779,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"}
+{"seq_id":"762558276","text":"'''\n동전 앞/뒤 맞추는 게임\n\n'''\nimport random\n\nprint(\"Hello world!\")\np = True\nwhile p :\n print(\"== 동전 앞/뒤 맞추기 ==\")\n print(\"1.앞면/2.뒷면/3.종료\")\n u = int(input(\"선택 > \"))\n i = int(random.randrange(1,3))\n if u == 3 :\n print(\"종료합니다.\")\n break\n elif u == i :\n print(\"맞췄습니다!\")\n else :\n print(\"틀렸습니다!\") \n\n\n'''선생님 답안'''\nimport random\nimport os\nwhile True :\n print(\"== 동전 앞/뒤 맞추기 ==\")\n print(\"1.앞면/2.뒷면/3.종료\")\n select = int(input(\"선택 > \"))\n com = random.randrange(1,3)\n if select == 3 :\n print(\"동전 게임을 종료합니다\")\n break\n if 0 < select and select < 3 :\n if com == select :\n print(\"맞췄습니다!\")\n else :\n print(\"틀렸습니다.\")\n else :\n print(\"메뉴를 확인 후 다시 입력해주세요.\")\n os.system(\"pause\")\n os.system(\"cls\")\n \n","repo_name":"ghdwpaks/python","sub_path":"it/day14/it/ex_solv02.py","file_name":"ex_solv02.py","file_ext":"py","file_size_in_byte":988,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"36188163490","text":"\"\"\"\nDefine a set of universal constants that may be useful\n\"\"\"\n\n# Boltzmann's Constant\nboltzmann = 1.3806e-23\n \n# Reference Temperature (290 K), used for background noise level\nref_temp = 290.\nkT = 4.00374e-21 # boltzmann * T0\n \n# Speed of Light\nspeed_of_light = 299792458.\n \n# Radius of the earth; with a (4/3) constant applied to account for\n# electromagnetic propagation around the Earth\nradius_earth_true = 6371000.\nradius_earth_eff = radius_earth_true*4./3.\n","repo_name":"nodonoughue/emitter-detection-python","sub_path":"utils/constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"91"}
+{"seq_id":"30902483","text":"names=['PAN','AN','PANAN']\nprint(names)\nfor name in names: #依次把list或者tuple中的每个元素迭代出来\n print(name)\n\nsum1=0\nfor x in [1,2,3,4,5,6,7,8,9,10]:\n sum1=sum1+x\nprint(sum1)\n\n#range()函数生成整数序列\nprint(list(range(5)))\n\nsum=0\nfor x in range(101):\n sum=sum+x #print缩进在循环内，不缩进在循环外\nprint(sum)\n\nsum2=0\nn=100\nwhile n>0: #while循环，只要条件满足就不断循环，条件满足时退出\n sum2=sum2+n\n n=n-1\nprint(sum2)\nprint(n)\n\n\n","repo_name":"iMANER/Pythonhello","sub_path":"loop_testpy1.py","file_name":"loop_testpy1.py","file_ext":"py","file_size_in_byte":505,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"}
+{"seq_id":"6699856253","text":"from brownie import accounts, network, config, Contract, VRFCoordinatorMock, LinkToken\nfrom web3 import Web3\n\nLOCAL_BLOCKCHAIN_ENVIRONMENTS = [\"development\", \"ganache\", \"mainnet-fork\"]\n# {contract} / {tokenID}\nOPENSEA_URL = \"https://testnets.opensea.io/assets/{}/{}\"\n\n\ndef get_account(index=None, id=None):\n\n if index:\n return accounts[index]\n if id:\n return accounts.load(id)\n if network.show_active() in LOCAL_BLOCKCHAIN_ENVIRONMENTS:\n return accounts[0]\n return accounts.add(config[\"wallets\"][\"from_key\"])\n\n\ncontract_to_mock = {\n \"vrf_coordinator\": VRFCoordinatorMock,\n \"link_token\": LinkToken,\n}\n\n\ndef get_contract(contract_name):\n contract_type = contract_to_mock[contract_name]\n # if in local deploy mocks, else return contract\n if network.show_active() in LOCAL_BLOCKCHAIN_ENVIRONMENTS:\n if len(contract_type) <= 0:\n deploy_mocks()\n contract = contract_type[-1]\n else:\n # address\n contract_address = config[\"networks\"][network.show_active()][contract_name]\n # abi\n contract = Contract.from_abi(\n contract_type._name, contract_address, contract_type.abi\n )\n return contract\n\n\ndef deploy_mocks():\n print(\"Deploying Mocks...\")\n account = get_account()\n print(\"Deploying Linktoken...\")\n link_token = LinkToken.deploy({\"from\": account})\n print(\"Deployed Linktoken!\")\n print(\"Deploying VRFCoordinator...\")\n vrf_coordinator = VRFCoordinatorMock.deploy(link_token.address, {\"from\": account})\n print(\"Deplyed All Mocks!\")\n\n\ndef fund_with_link(contract_address, account = None, link_token = None, amount = Web3.toWei(1, \"ether\")):\n print(\"Funding link token...\")\n account = account if account else get_account()\n link_token = link_token if link_token else get_contract(\"link_token\")\n tx = link_token.transfer(contract_address, amount, {\"from\": account})\n tx.wait(1)\n print(\"Funded link token!!!\")\n return tx\n\nBreedMapping = {0:\"PUG\", 1:\"SHIBA_INU\", 2:\"ST_BERNARD\"}\n# mapping different state number to different breed\ndef get_breed(breed_number):\n return BreedMapping[breed_number]\n\n\n\n\n","repo_name":"ZheZhang10/NFT_Demo","sub_path":"scripts/helpful_scripts.py","file_name":"helpful_scripts.py","file_ext":"py","file_size_in_byte":2155,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"8780934489","text":"# https://leetcode.com/problems/lowest-common-ancestor-of-a-binary-tree-ii\n# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n def lowestCommonAncestor(self, root: 'TreeNode', p: 'TreeNode', q: 'TreeNode') -> 'TreeNode': \n # stores key = node, value = parent node\n self.parent = dict()\n \n def helper(node, prt):\n if not node:\n return\n \n self.parent[node] = prt\n helper(node.left, node)\n helper(node.right, node)\n \n # populate parent dictionary\n # remember parent of root == root\n helper(root, root)\n \n # if any of the nodes are not in node to parent dictionary, then there cannot be a lca\n if p not in self.parent or q not in self.parent:\n return None\n \n # parent dictionary for node p\n dicp = dict()\n while p:\n prt = self.parent[p]\n # build parent dictionary for current p\n dicp[p] = prt\n \n # break when we've reached the top of the tree, where the parent of the root == root itself\n if prt == p:\n break\n \n # move up one level and repeat\n p = prt\n \n lca = None\n while q:\n # if q itself is a parent registered in dicp, then q is the lca\n if q in dicp:\n lca = q\n break\n \n prt = self.parent[q]\n \n # first common parent between q and p's lineage is going to be the lca otherwise\n if prt in dicp:\n lca = prt\n break\n \n # in this case, we haven't found an lca since q has also reached the top of the tree\n if prt == q:\n break\n \n # move up one level\n q = prt\n \n \n return lca","repo_name":"abhisha1991/LeetCodePractice","sub_path":"Python/LowestCommonAncestorBinaryTreeII.py","file_name":"LowestCommonAncestorBinaryTreeII.py","file_ext":"py","file_size_in_byte":2073,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"28004000056","text":"from typing import List, Optional, Tuple\nfrom data import DVAE_Dataset\nfrom modeling_discrete_vae import DiscreteVAE\nfrom utils import EMA, cosine_scheduler\nimport torch\nimport torch.nn as nn\nimport os\nimport yaml\nimport numpy as np\nfrom torch.utils.data import DataLoader\nfrom torch.utils.data.distributed import DistributedSampler\nfrom torch.optim.lr_scheduler import LRScheduler, CosineAnnealingLR\nfrom torch.optim.optimizer import Optimizer\nfrom torch.optim.adamw import AdamW\nfrom loguru import logger\nfrom collections import deque\nfrom tqdm import tqdm\nimport argparse\nfrom torch.nn.parallel import DistributedDataParallel as DDP\nimport random\n\n\ndef get_all_image_paths(folder_path: str, image_suffixes: List[str] = ['jpg', 'jpeg', 'png'], break_through: bool = False) -> List[str]:\n res = []\n for root, dirs, files in os.walk(folder_path):\n for file in files:\n if break_through or file.split('.')[-1] in image_suffixes:\n res.append(os.path.join(root, file))\n break\n return res\n\n\nclass DVAE_Train():\n def __init__(self, *, model: nn.Module, rank: int, world_size: int, train_dataloader: DataLoader, val_dataloader: Optional[DataLoader], optimizer: Optimizer, lr_scheduler: LRScheduler, \n param_averager: EMA, kl_schedule: np.ndarray, temperature_schedule: np.ndarray, checkpoint_pos: Optional[str], max_saved_checkpoints: Optional[int]) -> None:\n # model\n self.model = model\n # dataloader\n self.train_dataloader = train_dataloader\n self.val_dataloader = val_dataloader\n # optimizer & scheduler\n self.optimizer = optimizer\n self.lr_scheduler = lr_scheduler\n self.param_averager = param_averager\n # schedule\n self.kl_schedule = kl_schedule\n self.temperature_schedule = temperature_schedule\n self.kl_schedule_len = len(self.kl_schedule)\n self.temperature_schedule_len = len(self.temperature_schedule)\n # scaler\n self.scaler = torch.cuda.amp.GradScaler()\n # other info & statistic\n self.rank = rank\n self.world_size = world_size\n self.master_rank = (self.rank == 0)\n self.update_cnt = torch.tensor(0, dtype = torch.int64).cuda().detach()\n self.world_updates = [torch.zeros(1, dtype=torch.int64).cuda().detach() for _ in range(self.world_size)]\n self.world_train_loss = [torch.zeros(2, dtype = torch.float32).cuda().detach() for _ in range(self.world_size)]\n self.world_val_loss = [torch.zeros(2, dtype = torch.float32).cuda().detach() for _ in range(self.world_size)]\n self.epoch_loss_info = [] # (loss, batch_item_cnt)\n # checkpoint\n self.checkpoint_pos = os.path.abspath(checkpoint_pos) if checkpoint_pos is not None else None\n self.max_saved_checkpoints = max_saved_checkpoints\n self.saved_checkpoint_path = deque()\n self.best_train_loss = None\n self.best_train_epoch = None\n self.best_val_loss = None\n self.best_val_epoch = None\n self.saved_epoch_idx = set()\n\n\n def is_master_rank(self):\n return self.master_rank\n\n\n def get_current_schedule(self, break_through: bool = False, **kl_temperature):\n if break_through:\n assert len(kl_temperature) == 2\n return kl_temperature['kl'], kl_temperature['temperature']\n torch.distributed.all_gather(self.world_updates, self.update_cnt)\n update_cnt = 0\n for cnt in self.world_updates:\n update_cnt += cnt.item()\n kl_index = min(self.kl_schedule_len - 1, update_cnt)\n temperature_index = min(self.temperature_schedule_len - 1, update_cnt)\n logger.debug(f'update cnt {update_cnt} <- {self.rank} process')\n return self.kl_schedule[kl_index], self.temperature_schedule[temperature_index]\n\n\n def save_model(self, epoch: int, **others):\n assert self.checkpoint_pos is not None\n # make saved dict\n saved_dict = {\n 'model': self.model.module.state_dict(),\n 'lr_scheduler': self.lr_scheduler.state_dict(),\n 'optimizer': self.optimizer.state_dict(),\n 'param_averager': (self.param_averager.shadow, self.param_averager.mu),\n 'kl_schedule': self.kl_schedule,\n 'temperature_schedule': self.temperature_schedule,\n 'update_cnt': self.update_cnt\n }\n saved_dict.update(others)\n \n # save the checkpoint\n saved_path = os.path.join(self.checkpoint_pos, f'{epoch}_{self.update_cnt}.pth')\n torch.save(saved_dict, saved_path)\n logger.debug(f'Detailed information have been saved to {saved_path}')\n \n # check the number of checkpoints\n self.saved_checkpoint_path.append(saved_path)\n if self.max_saved_checkpoints is not None and len(self.saved_checkpoint_path) > self.max_saved_checkpoints:\n poped_checkpoint_path = self.saved_checkpoint_path.popleft()\n if os.path.exists(poped_checkpoint_path):\n os.remove(poped_checkpoint_path)\n logger.debug(f'The number of checkpoints exceed, {poped_checkpoint_path} have been removed!')\n\n\n @staticmethod\n def load_model(checkpoint_path: str, model: nn.Module, optimizer: Optimizer, lr_scheduler: LRScheduler, map_location = torch.device('cuda')):\n assert checkpoint_path is not None\n checkpoint_path = os.path.abspath(checkpoint_path)\n saved_dict = torch.load(checkpoint_path, map_location = map_location)\n # load dict\n model.load_state_dict(saved_dict['model'])\n optimizer.load_state_dict(saved_dict['optimizer'])\n lr_scheduler.load_state_dict(saved_dict['lr_scheduler'])\n # EMA\n shadow, mu = saved_dict['param_averager']\n param_averager = EMA(mu, shadow)\n return param_averager, saved_dict['kl_schedule'], saved_dict['temperature_schedule'], saved_dict['update_cnt']\n\n\n def val(self, epoch: int):\n total_loss, total_cnt = .0, 0\n for idx, images in tqdm(enumerate(self.val_dataloader)):\n if self.master_rank: logger.trace(f'Valuation epoch: {epoch}, batch index: {idx}')\n # epoch setting\n self.val_dataloader.sampler.set_epoch(1)\n # superparameter\n kl, temperature = self.get_current_schedule(break_through = True, kl = 0., temperature = 1.)\n with torch.no_grad():\n loss = self.model(img = images, temperature = temperature, kl_div_loss_weight = kl, return_loss = True)\n total_loss += loss.cpu().item()\n total_cnt += images.shape[0]\n\n # rank loss info\n logger.debug(f'Valuation epoch: {epoch}, rank id: {self.rank}, items: {total_cnt}, loss: {total_loss}')\n\n # gather all loss & cnt\n proc_loss_cnt = torch.tensor((total_loss, total_cnt), dtype = torch.float32).cuda().detach()\n torch.distributed.all_gather(self.world_val_loss, proc_loss_cnt)\n if self.master_rank:\n # get val loss & cnt\n val_loss, val_cnt = .0, 0\n for loss_cnt in self.world_val_loss:\n val_loss += loss_cnt[0]\n val_cnt += loss_cnt[1]\n # print to log\n val_cnt = int(val_cnt)\n learning_rate = self.optimizer.state_dict()['param_groups'][0]['lr']\n val_statistic_output = f'epoch {epoch}, item: {val_cnt}, lr: {learning_rate}, loss: {val_loss}'\n logger.info(val_statistic_output)\n logger.log('VAL', val_statistic_output)\n \n # checkpoint\n if self.best_val_loss is None:\n self.best_val_loss = val_loss\n self.best_val_epoch = epoch\n if self.checkpoint_pos is not None and epoch not in self.saved_epoch_idx:\n self.save_model(epoch)\n self.saved_epoch_idx.add(epoch)\n else:\n if self.best_val_loss >= val_loss:\n logger.debug(f'Valuation previous best loss: {self.best_val_loss} ({self.best_val_epoch}) -> current best loss: {val_loss} ({epoch})')\n self.best_train_loss = val_loss\n self.best_val_epoch = epoch\n if self.checkpoint_pos is not None and epoch not in self.saved_epoch_idx:\n self.save_model(epoch)\n self.saved_epoch_idx.add(epoch)\n \n\n def _train(self, epoch: int):\n proc_epoch_cnt = 0\n proc_epoch_loss = .0\n for idx, images in tqdm(enumerate(self.train_dataloader)):\n if self.master_rank: logger.trace(f'Train epoch: {epoch}, batch index: {idx}')\n # epoch setting\n self.train_dataloader.sampler.set_epoch(epoch)\n # superparameter \n kl, temperature = self.get_current_schedule(break_through = True, kl = 0., temperature = 1.)\n proc_epoch_cnt += images.shape[0]\n self.optimizer.zero_grad()\n with torch.cuda.amp.autocast(): # type: ignore\n loss = self.model(img = images, temperature = temperature, kl_div_loss_weight = kl, return_loss = True)\n # statics\n proc_epoch_loss += loss.cpu().item()\n self.update_cnt += 1\n # update model\n self.scaler.scale(loss).backward() # type: ignore\n self.scaler.step(self.optimizer)\n self.scaler.update()\n self.lr_scheduler.step()\n for name, param in self.model.named_parameters():\n if param.requires_grad:\n self.param_averager.update(name, param.data)\n \n # rank epoch loss info\n logger.debug(f'Train epoch: {epoch}, rank id: {self.rank}, items: {proc_epoch_cnt}, loss: {proc_epoch_loss}')\n \n # checkpoint\n loss_cnt_tensor = torch.tensor((proc_epoch_loss, proc_epoch_cnt), dtype = torch.float32).cuda().detach()\n torch.distributed.all_gather(self.world_train_loss, loss_cnt_tensor)\n if self.master_rank:\n # get total loss\n total_epoch_loss, total_epoch_cnt = .0, 0\n for epoch_loss_cnt in self.world_train_loss:\n total_epoch_loss += epoch_loss_cnt[0].item()\n total_epoch_cnt += int(epoch_loss_cnt[1].item())\n # print to log\n learning_rate = self.optimizer.state_dict()['param_groups'][0]['lr']\n train_statistic_output = f'epoch: {epoch}, items: {total_epoch_cnt}, lr: {learning_rate}, loss: {total_epoch_loss}'\n logger.info(train_statistic_output)\n logger.log('TRAIN', train_statistic_output)\n self.epoch_loss_info.append((total_epoch_loss, total_epoch_cnt))\n # checkpoint\n if self.best_train_loss is None:\n self.best_train_loss = total_epoch_loss\n self.best_train_epoch = epoch\n if self.checkpoint_pos is not None and self.master_rank:\n self.save_model(epoch)\n self.saved_epoch_idx.add(epoch)\n else:\n if self.best_train_loss >= total_epoch_loss:\n logger.debug(f'Train previous best loss: {self.best_train_loss} ({self.best_train_epoch}) -> current best loss: {total_epoch_loss} ({epoch})')\n self.best_train_loss = total_epoch_loss\n self.best_train_epoch = epoch\n if self.checkpoint_pos is not None and self.master_rank:\n self.save_model(epoch)\n self.saved_epoch_idx.add(epoch)\n\n\n def train(self, s_epoch: int, e_epoch: int, update_cnt : Optional[int] = None, need_val: bool = True, val_epoch_interval: int = 5):\n if update_cnt is not None:\n self.update_cnt = torch.tensor(update_cnt, dtype = torch.int64).cuda().detach()\n if self.master_rank: logger.info(f'Start training {s_epoch} - {e_epoch}')\n for epoch in range(s_epoch, e_epoch + 1):\n if self.master_rank: logger.info(f'Start training of {epoch} epoch.')\n self._train(epoch)\n if need_val and epoch % val_epoch_interval == 0:\n if self.master_rank: logger.info(f'Start valuation of {epoch} epoch.')\n self.val(epoch)\n\n\ndef init_seeds(seed=0, cuda_deterministic=True):\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n # Speed-reproducibility tradeoff https://pytorch.org/docs/stable/notes/randomness.html\n if cuda_deterministic:\n # slower, more reproducible\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = False\n else:\n # faster, less reproducible\n torch.backends.cudnn.deterministic = False\n torch.backends.cudnn.benchmark = True\n\n\nif __name__ == \"__main__\":\n # load config\n CONFIG_PATH = r''\n with open(CONFIG_PATH, 'r') as f:\n config = yaml.safe_load(f)\n\n # logger setting\n logger_dict = config['log']\n logger.remove()\n logger.level(name = 'TRAIN', no = 20)\n logger.level(name = 'VAL', no = 20)\n logger_file_folder = logger_dict['path']['folder']\n roration = logger_dict['rotation']\n logger.add(sink = os.path.join(logger_file_folder, 'info.log'), rotation=roration, compression=\"zip\", enqueue=True, filter=lambda record: record[\"level\"].name == \"INFO\")\n logger.add(sink = os.path.join(logger_file_folder, 'debug.log'), rotation=roration, compression=\"zip\", enqueue=True, filter=lambda record: record[\"level\"].name == \"DEBUG\")\n logger.add(sink = os.path.join(logger_file_folder, 'trace.log'), rotation=roration, compression=\"zip\", enqueue=True, level=\"TRACE\")\n logger.add(sink = os.path.join(logger_file_folder, 'train.log'), rotation=roration, compression=\"zip\", enqueue=True, filter=lambda record: record[\"level\"].name == \"TRAIN\")\n logger.add(sink = os.path.join(logger_file_folder, 'val.log'), rotation=roration, compression=\"zip\", enqueue=True, filter=lambda record: record[\"level\"].name == \"VAL\")\n\n # bash args parse\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--local-rank\", type=int)\n ARGS = parser.parse_args()\n local_rank = ARGS.local_rank\n\n # DDP backend init\n train_dict = config['train']\n distributed_dict = train_dict['distributed']\n torch.cuda.set_device(local_rank)\n torch.distributed.init_process_group(backend = distributed_dict['backend']) # type: ignore\n device = torch.device(train_dict['device'], local_rank)\n \n # set a seed of random algorithm\n rank = torch.distributed.get_rank()\n init_seeds(seed = 1 + rank)\n \n # basic model & init\n model_dict = config['model']\n model = DiscreteVAE(**model_dict).to(device)\n for m in model.modules():\n if isinstance(m, nn.Conv2d):\n nn.init.xavier_normal_(m.weight)\n\n # optimizer\n optimizer_dict = train_dict['optimizer']\n optimizer = AdamW(model.parameters(), betas = (optimizer_dict['beta_1'], optimizer_dict['beta_2']), \n eps = optimizer_dict['eps'], weight_decay = optimizer_dict['weight_decay'], lr = optimizer_dict['lr'])\n \n # schedule list\n schedule_dict = train_dict['schedule']\n # kl\n kl_schedule_dict = schedule_dict['kl']\n kl_schedule = cosine_scheduler(base_value = kl_schedule_dict['start'], final_value = kl_schedule_dict['end'], \n max_length = kl_schedule_dict['length'])\n # temperature\n temperature_schedule_dict = schedule_dict['temperature']\n temperature_schedule = cosine_scheduler(base_value = temperature_schedule_dict['start'], final_value = temperature_schedule_dict['end'], \n max_length = temperature_schedule_dict['length'])\n # learning rate\n lr_dict = schedule_dict['lr']\n lr_schedule = CosineAnnealingLR(optimizer, T_max = lr_dict['length'], eta_min = lr_dict['end'])\n\n # device & checkpoint\n checkpoint_dict = train_dict['checkpoint']\n checkpoint_pos = checkpoint_dict['folder']\n max_saved_checkpoints = checkpoint_dict['max_size']\n \n # train control\n train_ctrl_dict = train_dict['control']\n s_epoch = train_ctrl_dict['s_epoch']\n e_epoch = train_ctrl_dict['e_epoch']\n\n # load model\n update_cnt = 0\n if train_ctrl_dict['continue_train']:\n param_averager, kl_schedule, temperature_schedule, update_cnt = DVAE_Train.load_model(train_ctrl_dict['checkpoint'], model, optimizer, lr_schedule)\n \n # model DDP\n model = DDP(model, device_ids = [local_rank], output_device = local_rank)\n \n # param\n if not train_ctrl_dict['continue_train']:\n decay_coefficient = schedule_dict['param']['decay_coefficient']\n param_averager = EMA(mu = decay_coefficient)\n # param register\n for name, param in model.named_parameters():\n if param.requires_grad:\n param_averager.register(name, param.data)\n\n # data\n # train\n train_data_dict = train_dict['data']\n train_dataset = DVAE_Dataset(get_all_image_paths(train_data_dict['folder']), model_dict['image_size'], False)\n train_sampler = DistributedSampler(train_dataset)\n train_dataloader = DataLoader(train_dataset, batch_size = train_data_dict['bs'], sampler = train_sampler, num_workers = train_data_dict['num_workers'])\n # val\n val_dict = config['val']\n val_data_dict = val_dict['data']\n val_dataset = DVAE_Dataset(get_all_image_paths(val_data_dict['folder']), model_dict['image_size'], False)\n val_sampler = DistributedSampler(val_dataset)\n val_dataloader = DataLoader(val_dataset, batch_size = val_data_dict['bs'], sampler = val_sampler, num_workers = val_data_dict['num_workers'])\n \n # Trainer\n trainer = DVAE_Train(model = model, rank = torch.distributed.get_rank(), world_size = torch.distributed.get_world_size(), train_dataloader = train_dataloader, val_dataloader = val_dataloader, optimizer = optimizer,\n lr_scheduler = lr_schedule, param_averager = param_averager, kl_schedule = kl_schedule, temperature_schedule = temperature_schedule,\n checkpoint_pos = checkpoint_pos, max_saved_checkpoints = max_saved_checkpoints)\n trainer.train(s_epoch, e_epoch, update_cnt, val_dict['need'], val_dict['interval'])\n","repo_name":"MikuAndRabbit/Model-Implement-Record","sub_path":"dvae/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":18332,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"20196075831","text":"import json\nimport requests\nimport telebot\nimport time_helper\nimport sqlite3\n\ntelebot.apihelper.proxy = {'https': 'socks5h://14611055481:U777Vluhz8@orbtl.s5.opennetwork.cc:999'}\n\nbot = telebot.TeleBot('674637341:AAGg5XJ5LMwwLL0ft2vnRdwX4ToinWxW-6Q')\nuser_id = bot.get_me().id\n\nkeyboard = telebot.types.ReplyKeyboardMarkup(True, True)\nkeyboard.row('1', '2', '3')\n\n\ndef get_current_delivery_method():\n conn = sqlite3.connect(r\"E:\\dev\\Projects\\telegramBot\\data.sqlite\")\n cursor = conn.cursor()\n cursor.execute(\"SELECT delivery_method FROM delivery WHERE id=:id\", {\"id\": user_id})\n current_method = cursor.fetchall()\n current_method = time_helper.garbage(str(current_method))\n return current_method\n\n\ndef build_keyboard():\n delivery_days_list = time_helper.delivery_days_list(time_helper.get_current_date()[0],\n time_helper.get_current_date()[1],\n time_helper.get_current_date()[2])\n keyboard1 = telebot.types.InlineKeyboardMarkup()\n for x in range(5):\n button = telebot.types.InlineKeyboardButton(\n text=delivery_days_list[x][0] + \"th \" + delivery_days_list[x][2] + \" - \" + delivery_days_list[x][1],\n callback_data=str(x) + \"day\")\n keyboard1.add(button)\n return keyboard1\n\n\ndef show_menu(call):\n bot.send_message(call,\n 'Do you wanna do something else?\\n'\n '1 - Choose date and place for your order\\n'\n '2 - Pickup the order\\n'\n '3 - Track the order\\n', reply_markup=keyboard)\n\n\n@bot.callback_query_handler(func=lambda call: True)\ndef query_handler(call):\n\n for x in range(5):\n if call.data == str(x) + 'day':\n delivery_days_list = time_helper.delivery_days_list(time_helper.get_current_date()[0],\n time_helper.get_current_date()[1],\n time_helper.get_current_date()[2])\n conn = sqlite3.connect(r\"E:\\dev\\Projects\\telegramBot\\data.sqlite\")\n cursor = conn.cursor()\n date = delivery_days_list[x][0] + \".\" + delivery_days_list[x][2] + \".\" + delivery_days_list[x][3]\n try:\n cursor.execute(\"INSERT INTO delivery (id, delivery_method, delivery_date) VALUES (:id,'delivery', :date)\"\n , {\"date\": date, \"id\": user_id})\n except sqlite3.IntegrityError:\n cursor.execute(\"UPDATE delivery SET delivery_date=:date WHERE id=:id\", {\"date\": date, \"id\": user_id})\n finally:\n conn.commit()\n conn.close()\n\n keyboard2 = telebot.types.InlineKeyboardMarkup()\n button1 = telebot.types.InlineKeyboardButton(text=\"from 8 am to 1pm\", callback_data=\"f8t1\")\n keyboard2.add(button1)\n button2 = telebot.types.InlineKeyboardButton(text=\"from 1 pm am to 6pm\", callback_data=\"f1t6\")\n keyboard2.add(button2)\n bot.edit_message_text(chat_id=call.message.chat.id, message_id=call.message.message_id,\n text=\"Choose time for delivery:\", reply_markup=keyboard2)\n\n if call.data == \"f8t1\" or call.data == \"f1t6\":\n conn = sqlite3.connect(r\"E:\\dev\\Projects\\telegramBot\\data.sqlite\")\n cursor = conn.cursor()\n if call.data == \"f8t1\":\n cursor.execute(\"UPDATE delivery SET delivery_time=:date WHERE id=:id\",\n {\"date\": \"from 8am to 1pm\", \"id\": user_id})\n elif call.data == \"f1t6\":\n cursor.execute(\"UPDATE delivery SET delivery_time=:date WHERE id=:id\",\n {\"date\": \"from 1pm to 6pm\", \"id\": user_id})\n conn.commit()\n cursor.execute(\"SELECT delivery_date FROM delivery WHERE id=:id\", {\"id\": user_id})\n date_result = cursor.fetchall()\n cursor.execute(\"SELECT delivery_time FROM delivery WHERE id=:id\", {\"id\": user_id})\n time_result = cursor.fetchall()\n date_result = time_helper.garbage(str(date_result))\n time_result = time_helper.garbage(str(time_result))\n conn.close()\n\n bot.edit_message_text(chat_id=call.message.chat.id, message_id=call.message.message_id,\n text=\"Your package will come in \"+date_result+\" at \"+time_result)\n show_menu(call.message.chat.id)\n if call.data == 'change_to_pickup':\n conn = sqlite3.connect(r\"E:\\dev\\Projects\\telegramBot\\data.sqlite\")\n cursor = conn.cursor()\n cursor.execute(\"UPDATE delivery SET delivery_method='pickup' WHERE id=:id\",\n {\"id\": user_id})\n conn.commit()\n conn.close()\n bot.edit_message_text(chat_id=call.message.chat.id, message_id=call.message.message_id,\n text=\"You're delivery method had changed to 'pickup'\")\n show_menu(call.message.chat.id)\n\n if call.data == 'change_to_delivery':\n conn = sqlite3.connect(r\"E:\\dev\\Projects\\telegramBot\\data.sqlite\")\n cursor = conn.cursor()\n cursor.execute(\"UPDATE delivery SET delivery_method='delivery' WHERE id=:id\",\n {\"id\": user_id})\n conn.commit()\n conn.close()\n bot.edit_message_text(chat_id=call.message.chat.id, message_id=call.message.message_id,\n text='Choose day, when you can get the order(except days-off)',\n reply_markup=build_keyboard())\n\n if call.data == 'no':\n bot.edit_message_text(chat_id=call.message.chat.id, message_id=call.message.message_id,\n text=\"Back to te main menu\")\n show_menu(call.message.chat.id)\n\n\n@bot.message_handler(commands=['start'])\ndef start_message(message):\n bot.send_message(message.chat.id,\n 'Welcome to the our telegram bot. Here you can track your order. What do you what to do?\\n'\n '1 - Choose date and place for your order\\n'\n '2 - Pickup the order\\n'\n '3 - Track the order\\n', reply_markup=keyboard)\n\n\n@bot.message_handler(content_types=['text'])\ndef send_text(message):\n if message.text.lower() == '1':\n # 0-day,1-weekday,2-month,3-year\n delivery_days_list = time_helper.delivery_days_list(time_helper.get_current_date()[0],\n time_helper.get_current_date()[1],\n time_helper.get_current_date()[2])\n keyboard1 = telebot.types.InlineKeyboardMarkup()\n for x in range(5):\n button = telebot.types.InlineKeyboardButton(\n text=delivery_days_list[x][0] + \"th \" + delivery_days_list[x][2] + \" - \" + delivery_days_list[x][1],\n callback_data=str(x) + \"day\")\n keyboard1.add(button)\n\n if get_current_delivery_method() == 'pickup':\n keyboard3 = telebot.types.InlineKeyboardMarkup()\n button1 = telebot.types.InlineKeyboardButton(text=\"Yes, change my delivery method to 'delivery'\",\n callback_data=\"change_to_delivery\")\n keyboard3.add(button1)\n button2 = telebot.types.InlineKeyboardButton(text=\"No\", callback_data=\"no\")\n keyboard3.add(button2)\n bot.send_message(message.chat.id, \"You're current delivery method is 'pickup',\"\n \" do you want to change current delivery method?\", reply_markup=keyboard3)\n else:\n bot.send_message(message.chat.id, 'Choose day, when you can get the order(except days-off)',\n reply_markup=build_keyboard())\n\n elif message.text.lower() == '2':\n response = requests.get(\n \"https://places.cit.api.here.com/places/v1/autosuggest?at=56.114526%2C47.198588&q=почта&Accept-Language=en-US%3Ben&app_id=x2p3TDHxbG04n0y1cC9j&app_code=gRs6qSR72e0_-k_nLMgP7A\")\n todos = json.loads(response.text)\n xd_list = []\n for x in range(len(todos.get('results'))):\n if todos.get('results')[x].get('category') == 'post-office':\n xd_list.append(todos.get('results')[x].get('distance'))\n else:\n xd_list.append(0)\n closest_mail = min(x for x in xd_list if x != 0)\n delivery_days_list = time_helper.delivery_days_list(time_helper.get_current_date()[0],\n time_helper.get_current_date()[1],\n time_helper.get_current_date()[2])\n bot.send_message(message.chat.id, \"You can take your package in \" +\n todos.get('results')[xd_list.index(closest_mail)].get('title') +\n \" after \"+delivery_days_list[0][0]+\"th \"+delivery_days_list[0][2] +\n \"(\"+delivery_days_list[0][1]+\")\")\n bot.send_location(message.chat.id, todos.get('results')[xd_list.index(closest_mail)].get('position')[0],\n todos.get('results')[xd_list.index(closest_mail)].get('position')[1])\n\n if get_current_delivery_method() == 'delivery':\n keyboard3 = telebot.types.InlineKeyboardMarkup()\n button1 = telebot.types.InlineKeyboardButton(text=\"Yes, change my delivery method to 'pickup'\",\n callback_data=\"change_to_pickup\")\n keyboard3.add(button1)\n button2 = telebot.types.InlineKeyboardButton(text=\"No\", callback_data=\"no\")\n keyboard3.add(button2)\n bot.send_message(message.chat.id, \"You're current delivery method is 'delivery',\"\n \" do you want to change current delivery method?\", reply_markup=keyboard3)\n else:\n date = delivery_days_list[0][0] + \".\" + delivery_days_list[0][2] + \".\" + delivery_days_list[0][3]\n conn = sqlite3.connect(r\"E:\\dev\\Projects\\telegramBot\\data.sqlite\")\n cursor = conn.cursor()\n try:\n cursor.execute(\"INSERT INTO delivery (id, delivery_method, delivery_date) VALUES (:id,'pickup', :date)\"\n , {\"date\": date, \"id\": user_id})\n except sqlite3.IntegrityError:\n cursor.execute(\"UPDATE delivery SET delivery_date=:date WHERE id=:id\", {\"date\": date, \"id\": user_id})\n finally:\n conn.commit()\n conn.close()\n\n else:\n bot.send_message(message.chat.id, \"Sorry, I don't understand you\", reply_markup=keyboard)\n\n\nif __name__ == '__main__':\n bot.polling(none_stop=True)\n","repo_name":"Mayor2k/firstTelegramBot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":10826,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"5963011452","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Jul 8 21:43:02 2020\r\n\r\n@author: JM070903\r\n\"\"\"\r\n\r\n\"\"\"\r\nA palindromic number reads the same both ways. The largest palindrome made from the product of two 2-digit \r\nnumbers is 9009 = 91 × 99.\r\n\r\nFind the largest palindrome made from the product of two 3-digit numbers\r\n\"\"\"\r\n\r\n\r\ndef palindrome(x,y):\r\n total = x* y\r\n if str(total) == str(total)[::-1]:\r\n print(total)\r\n return \r\n \r\nfor i in range (100,999):\r\n for j in range (100,999):\r\n palindrome(i,j)\r\n\r\n\r\n\r\n \r\n ","repo_name":"jmoro0408/Euler","sub_path":"Euler4.py","file_name":"Euler4.py","file_ext":"py","file_size_in_byte":563,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"6817323256","text":"__author__ = 'Timothy Lam'\n\nimport os\nimport ctypes\nimport hashlib\nimport datetime\n\ndef selector(message):\n if message == 'windows sort download folder':\n sort_downloads()\n return {'display': 'download files have been sorted', 'say': 'download files have been sorted'}\n elif message == 'windows check duplicates':\n check_duplicates()\n return {'display': 'windows has checked duplicates', 'say': 'find below the ducplicated'}\n elif message == 'windows lock screen': #Lock Screen\n ctypes.windll.user32.LockWorkStation()\n elif message == 'windows sign out': #Logoff\n ctypes.windll.user32.ExitWindowsEx(0)\n elif message == 'windows shutdown': #Shutdown\n os.system(\"shutdown /s /t 1\")\n elif message == \"windows restart\": #Restart\n os.system(\"shutdown /r /t 1\")\n else:\n return 'windows is currently offline'\n\n\ndef sort_downloads():\n path = r'C:\\Users\\Timothy Lam\\Downloads'\n # folders = [i for i in os.listdir('.') if os.path.isdir(i)]\n folders = []\n files = []\n os.chdir(path)\n for i in os.listdir('.'):\n if os.path.isdir(i):\n folders.append(i)\n else:\n files.append(i)\n dirs = ['PDF', 'EXE', 'WordFiles', 'FILES', 'PowerPoint', 'Excel', 'Pictures', 'Videos']\n try:\n for i in dirs:\n if i not in folders:\n os.mkdir(path+fr'\\{i}')\n except OSError as e:\n print(e)\n\n for file in files:\n name, ext = os.path.splitext(file)\n ext = ext[1:]\n if name[-1] == ')':\n os.remove(file)\n else:\n if ext == 'exe':\n os.rename(rf'{path}\\{file}', rf'{path}\\EXE\\{file}')\n elif ext == 'pdf':\n os.rename(rf'{path}\\{file}', rf'{path}\\PDF\\{file}')\n elif (ext == 'doc') or (ext == 'docx'):\n os.rename(rf'{path}\\{file}', rf'{path}\\WordFiles\\{file}')\n elif (ext == 'ppt') or (ext == 'pptx'):\n os.rename(rf'{path}\\{file}', rf'{path}\\PowerPoint\\{file}')\n elif (ext == 'csv') or (ext == 'xlsx'):\n os.rename(rf'{path}\\{file}', rf'{path}\\Excel\\{file}')\n elif (ext == 'png') or (ext == 'jpg'):\n os.rename(rf'{path}\\{file}', rf'{path}\\Pictures\\{file}')\n elif (ext == 'avi') or (ext == 'mp4'):\n os.rename(rf'{path}\\{file}', rf'{path}\\Videos\\{file}')\n else:\n os.rename(rf'{path}\\{file}', rf'{path}\\FILES\\{file}')\n\ndef get_hash(file):\n ha = hashlib.md5(file)\n hash_no = ha.hexdigest()\n return hash_no\n\n\ndef ft(time_):\n return datetime.datetime.fromtimestamp(time_)\n\n\ndef format_print(dup_dict):\n header = 'file' + ' ' * (36 - len('file')) + '| Duplicates'\n print('-' * (len(header) + 35))\n print(header)\n print('-' * (len(header) + 35))\n for i in dup_dict:\n print(f'{i}' + ' ' * (36 - len(i)) + f'| {\", \".join(dup_dict[i])}')\n print('-' * (len(header) + 35))\n\n\ndef check_duplicates():\n path = r'C:\\Users\\Timothy Lam\\Downloads'\n files = {}\n dup_dict = {}\n os.chdir(path)\n for i in os.listdir('.')[::-1]:\n if os.path.isfile(i):\n file = open(i, \"rb\")\n hash_ = get_hash(file.read())\n if hash_ in files:\n if files[hash_] in dup_dict:\n dup_dict[files[hash_]].append(i)\n else:\n dup_dict[files[hash_]] = [i]\n #print(f\"'{i}' is duplicated in this directory. Same content with '{files[hash_]}'\")\n # created time | modified time\n #print(f\"{ft(os.path.getctime(i))}|{ft(os.path.getmtime(i))} vs {ft(os.path.getctime(files[hash_]))}|{ft(os.path.getmtime(files[hash_]))}\")\n else:\n files[hash_] = i\n format_print(dup_dict)\n","repo_name":"talam1992/Chatbot","sub_path":"parrotlet_bot/parrotlet_windows.py","file_name":"parrotlet_windows.py","file_ext":"py","file_size_in_byte":3839,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"}
+{"seq_id":"1192234750","text":"import speechbrain as sb\r\nfrom speechbrain.dataio.dataio import read_audio\r\n#from IPython.display import Audio\r\nimport os\r\nfrom scipy.io.wavfile import write\r\nimport numpy as np\r\n\r\n#separation\r\nfrom speechbrain.pretrained import SepformerSeparation as separator\r\nfrom speechbrain.pretrained import SpeakerRecognition\r\n\r\nimport parselmouth \r\nfrom parselmouth.praat import call\r\n\r\nimport sys\r\nimport soundfile\r\n\r\nfrom pathlib import WindowsPath\r\n\r\nold_open = soundfile.SoundFile._open\r\ndef patched_open(self, file, mode_int, closefd):\r\n if isinstance(file, WindowsPath):\r\n file = str(file.resolve())\r\n return old_open(self, file, mode_int, closefd)\r\nsoundfile.SoundFile._open = patched_open\r\n\r\ndef measurePitch(voiceID, f0min, f0max, unit):\r\n sound = parselmouth.Sound(voiceID) # read the sound\r\n \r\n duration = call(sound, \"Get total duration\") # duration\r\n pitch = call(sound, \"To Pitch\", 0.0, f0min, f0max) #create a praat pitch object\r\n\r\n pitch_values = pitch.selected_array['frequency']\r\n pitch_values[pitch_values==0] = np.nan\r\n Fhi, Flo = 0, 1e9\r\n for p in pitch_values:\r\n if p != np.nan:\r\n Fhi = max(Fhi, p)\r\n Flo = min(Flo, p)\r\n\r\n meanF0 = call(pitch, \"Get mean\", 0, 0, unit) # get mean pitch\r\n stdevF0 = call(pitch, \"Get standard deviation\", 0 ,0, unit) # get standard deviation\r\n harmonicity = call(sound, \"To Harmonicity (cc)\", 0.01, f0min, 0.1, 1.0)\r\n hnr = call(harmonicity, \"Get mean\", 0, 0)\r\n pointProcess = call(sound, \"To PointProcess (periodic, cc)\", f0min, f0max)\r\n localJitter = call(pointProcess, \"Get jitter (local)\", 0, 0, 0.0001, 0.02, 1.3)\r\n localabsoluteJitter = call(pointProcess, \"Get jitter (local, absolute)\", 0, 0, 0.0001, 0.02, 1.3)\r\n rapJitter = call(pointProcess, \"Get jitter (rap)\", 0, 0, 0.0001, 0.02, 1.3)\r\n ppq5Jitter = call(pointProcess, \"Get jitter (ppq5)\", 0, 0, 0.0001, 0.02, 1.3)\r\n ddpJitter = call(pointProcess, \"Get jitter (ddp)\", 0, 0, 0.0001, 0.02, 1.3)\r\n localShimmer = call([sound, pointProcess], \"Get shimmer (local)\", 0, 0, 0.0001, 0.02, 1.3, 1.6) / 1000\r\n localdbShimmer = call([sound, pointProcess], \"Get shimmer (local_dB)\", 0, 0, 0.0001, 0.02, 1.3, 1.6) / 1000\r\n apq3Shimmer = call([sound, pointProcess], \"Get shimmer (apq3)\", 0, 0, 0.0001, 0.02, 1.3, 1.6) / 1000\r\n apq5Shimmer = call([sound, pointProcess], \"Get shimmer (apq5)\", 0, 0, 0.0001, 0.02, 1.3, 1.6) / 1000\r\n apq11Shimmer = call([sound, pointProcess], \"Get shimmer (apq11)\", 0, 0, 0.0001, 0.02, 1.3, 1.6) / 1000\r\n ddaShimmer = call([sound, pointProcess], \"Get shimmer (dda)\", 0, 0, 0.0001, 0.02, 1.3, 1.6) / 1000\r\n \r\n \"\"\"\r\n return (duration, meanF0, stdevF0, hnr, localJitter, localabsoluteJitter, rapJitter, ppq5Jitter, ddpJitter, \r\n localShimmer, localdbShimmer, apq3Shimmer, apq5Shimmer, apq11Shimmer, ddaShimmer)\r\n\r\n status: 似乎可以忽略\r\n 其他: praat沒這個參數\r\n \"\"\"\r\n\r\n nhr, status, RPDE, DFA, spread1, spread2, D2, PPE = (-1,) * 8\r\n \r\n return (meanF0, Fhi, Flo, localJitter, localabsoluteJitter, rapJitter, ppq5Jitter, ddpJitter, \r\n localShimmer, localdbShimmer, apq3Shimmer, apq5Shimmer, apq11Shimmer, ddaShimmer, hnr)\r\n\r\nmodel = separator.from_hparams(source=\"speechbrain/sepformer-wsj02mix\", savedir='pretrained_models/sepformer-wsj02mix')\r\nverification = SpeakerRecognition.from_hparams(source=\"speechbrain/spkrec-ecapa-voxceleb\", savedir=\"pretrained_models/spkrec-ecapa-voxceleb\")\r\n\r\n#input\r\npath = sys.argv[-1].replace(\"\\\\\",\"/\")\r\nif not os.path.exists(path):\r\n exit(1)\r\n#path = os.getcwd()+\"/test_data_raw/test_set_subject_26.wma\"\r\n\r\nspearated = model.separate_file(path)\r\n\r\nfile_name = \".\".join(path.split(\"/\")[-1].split(\".\")[:-1])\r\nscores = [0,0]\r\nfor i in range(2):\r\n\r\n spearated_np = np.array(spearated[:,:,i].detach().cpu().squeeze())\r\n write(file_name+f'-{i}.wav', 8000, np.int16(spearated_np/np.max(np.abs(spearated_np)) * 32767))\r\n\r\n# score, prediction = verification.verify_files(file_name+f'-{i}.wav', f'test.wav')\r\n# scores[i] = score\r\n\r\n#if max(scores) < 0.7:\r\n# exit(2)\r\n\r\ni = scores.index(max(scores))\r\n\r\nresult = measurePitch(parselmouth.Sound(file_name+f'-{i}.wav'), 75, 300, \"Hertz\")\r\nprint(*result)\r\n\r\nresult = measurePitch(parselmouth.Sound(file_name+f'-{1-i}.wav'), 75, 300, \"Hertz\")\r\nprint(*result)\r\n\r\nfor i in range(2):\r\n os.remove(file_name+f'-{1-i}.wav')\r\nos.remove(file_name+'.wav')","repo_name":"rikurzt/SA-Group7-TW2","sub_path":"src/py/data_processing.py","file_name":"data_processing.py","file_ext":"py","file_size_in_byte":4437,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"}
+{"seq_id":"11852249976","text":"def square(n: int):\n start = 0\n finish = n - 1\n a = [[0 for j in range(n)] for i in range(n)]\n if n % 2 != 0:\n n += 1\n for i in range(n // 2):\n for j in range(start, finish + 1):\n a[start][j] = start + 1\n a[j][finish] = start + 1\n a[finish][j] = start + 1\n a[j][start] = start + 1\n start += 1\n finish -= 1\n for i in range(len(a)):\n for j in range(len(a)):\n print(f\"{' ' * (len(str(a[n // 2 - 1][n // 2 - 1])) - len(str(a[i][j])))}\"\n f\"{a[i][j]}\", end=\" \")\n print(\"\\n\", end=\"\")\n\n\ndef main():\n n = int(input())\n square(n)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"senyayukhnev/m80-112B-23_Arseniy_Yukhnev_LMS","sub_path":"2.4_labs/test19.py","file_name":"test19.py","file_ext":"py","file_size_in_byte":698,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"30573743527","text":"# DATA STRUCTURES\n# STRING METHODS\n\ntext = \"hEllO WoRlD\"\n# print(text.upper())\n# print(text.lower())\n# print(text.title())\n# # text = text.upper()\n# print(text[0:5].upper())\n# print(text.capitalize())\n# print(\"Hello! \\nHow are you doing today?\")\n# print(\n# \"\"\"Hello! \n# How are you doing today?\"\"\")\ngreetings = \"\"\"Hello! \nhow are you doing this morning?\"\"\"\n# print(greetings)\ntext = \"hello world\"\n# print(text.islower())\n# print(text.isupper())\n# print(text.count(\"o\"))\n# print(text.count(\"l\"))\n# print(text.count(\"hello\"))\n# print(text.count(\"o\", 0, 8))\n# print(text.replace(\"hello\", \"hi\"))\n# print(text.replace(\"e\", \"a\"))\n\ntext = \"welcome to this class\"\n# print(text.split())\ntext = \"welcome to this class, have a nice day\"\n# print(text.split(\",\"))\n\ntext = \" hello \"\n# print(text)\n# print(text.strip())\n\na, b = 4, 3\n# print(a+b)\n\n# # Number Manipulation\n# print(round(12.2, 2))\n\n\n# ACCESSING TUPLES\ncars = (\"Volvo\", \"Toyota\", \"Lexus\", \"Ferrari\", \"Mercedes\")\n# print(cars[0])\n# print(cars[2])\n# print(cars[-1])\n# print(cars[-4])\n\"\"\"\nwhen accessing tuples or lists from the beginning,\nthe index is usually 0,1,2.....\nwhen accessing tuples or lists from the end,\nthe index is usually -1,-2,-3.....\n\"\"\"\n# You can use a length function to check how many items are in your list or tuple\n# print(len(cars))\n# print(cars)\n\n# changing tuple to list\nnew_cars = list(cars)\n# print(new_cars)\nnew_cars[1] = \"Camry\\n then\"\n# print(new_cars)\n\n# changing tuple back to list\ncars = tuple(new_cars)\n# print(cars)\n\n# SET\nset1 = {1,2,3,4,5}\nset2 = {6,7,8,9,1}\nunion_of_sets = set1.union(set2)\n# print(union_of_sets)\ninter_of_sets = set1.intersection(set2)\n# print(inter_of_sets)\n\n#ACESSING LISTS\ncars = [\"Volvo\", \"Toyota\", \"Lexus\", \"Ferrari\", \"Mercedes\"]\n# print(len(cars))\ncars[3] = \"Bugatti\"\n# print(cars)\nnumbers = [10,20,30,40,50,60,70,80]\n# print(numbers[0:5]) # the second position after the colon is usually n-1\n# print(numbers[:5]) # from beginning to a specific point\n# print(numbers[2:]) # from a specific point to the end\n# print(numbers[:])\n# replacing multiple items in the list\n# print(cars[1:3])\n# print(cars)\ncars[1:3] = [\"Honda\", \"BMW\"]\n# print(cars)\n\n#append adds an item to the end of the list\ncars.append(\"Toyota\")\n# print(cars)\n#insert adds an item to a specific position in your list\ncars.insert(0, \"Lexus\")\n# print(cars)\n#extend\nfood = [\"rice\", \"beans\", \"yam\"]\nfruit = [\"Pineapple\", \"Mango\", \"Apple\"]\nfood.extend(fruit)\n# print(food)\n\n#Three methods to remove an item from a list\n#remove\nfood.remove(\"rice\")\n# print(food)\n#pop\nfood.pop()\n# print(food)\nfood.pop(0)\n# print(food)\n#del\ndel food[-1]\n# print(food)\n\n#DICTIONARY - Key Value Pairs\n\ncars = {\n \"brand\": \"Toyota\",\n \"model\": \"Corolla\",\n \"year\": 2019,\n \"color\": \"black\"\n }\n\ncomputers = {\n \"brand\": \"HP\",\n \"model\": \"Elitebook 840 G3\",\n \"storage\": \"256 GB\",\n \"RAM\": \"8 GB\"\n }\n# print(cars)\n# print(computers)\n# print(type(cars))\n# print(cars[\"model\"])\n# print(cars[\"brand\"])\n\n#update\n# cars.update({\"year\": 2020})\n# cars.update({\"engineno\": 2222020})\ncars.update({\"year\": 2020, \"engineno\": 2222020})\n# print(cars)\n# print(type(cars))\n\ncars = {\n \"brand\": \"Toyota\",\n \"model\": \"Corolla\",\n \"year\": 2019,\n \"color\": [\"black\", \"white\", \"blue\", \"silver\"]\n }\n# print(cars[\"color\"])\n# cars[\"model\"] = \"Yaris\"\n# print(cars)\n# x = cars.keys()\n# print(x)\n# y = cars.values()\n# print(y)\nnew_cars = cars.copy()\n# print(new_cars)\nnew_cars.pop(\"model\")\n# print(new_cars)\n\ncar = dict(brand = \"Toyota\", model = \"Camry\", year = \"2020\")\n# print(car)\n\n# NESTED DICTIONARY\nlaptops = {\n \"laptop1\": {\n \"name\": \"HP\",\n \"model\": \"Pavilion G6\"\n },\n \"laptop2\": {\n \"name\": \"Dell\",\n \"model\": \"Vostro\"\n },\n \"laptop3\": {\n \"name\": \"Lenovo\",\n \"model\": \"Thinkpad\"\n }\n }\nprint(laptops[\"laptop1\"])\nprint(laptops[\"laptop1\"][\"name\"])\n\n#NESTED DICTIONARY USING dict()\nlaptops = dict({\n \"laptop1\": {\n \"name\": \"HP\",\n \"model\": \"Pavilion G6\"\n },\n \"laptop2\": {\n \"name\": \"Dell\",\n \"model\": \"Vostro\"\n },\n \"laptop3\": {\n \"name\": \"Lenovo\",\n \"model\": \"Thinkpad\"\n }\n })\nprint(laptops)","repo_name":"Tra1307/NAIDT-Cohort-12","sub_path":"day2.py","file_name":"day2.py","file_ext":"py","file_size_in_byte":4174,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"91"}
+{"seq_id":"74290064944","text":"# -*- coding: utf-8 -*-\n# Uncomment the import only for coding support\nfrom openeo_udf.api.datacube import DataCube\nfrom typing import Dict\n\n\ndef apply_datacube(cube: DataCube, context: Dict) -> DataCube:\n\n import pandas\n import xarray\n import numpy\n \n class PhenologypParams:\n \n def __init__(self,year):\n self.year= year # yer of the season, int\n self.sStart=pandas.DateOffset(months=4, days=2) # Start date of interval for start of season\n self.sEnd= pandas.DateOffset(months=6, days=10) # End date of tart interval for start of season\n self.mStart=pandas.DateOffset(months=6, days=10) # Start date of interval for mid of season\n self.mEnd= pandas.DateOffset(months=9, days=1) # End date of tart interval for mid of season\n self.eStart=pandas.DateOffset(months=9, days=1) # Start date of interval for end of season\n self.eEnd= pandas.DateOffset(months=12,days=31) # End date of tart interval for end of season\n self.tSos= 10. # Threshold for start of season\n self.tEos= 10. # Threshold for end of season\n \n \n \"\"\"\n sStartDate: First date of the interval for getting season start\n sEndDate: Last date of the interval for getting season start\n \n mStartDate: First date of the interval for getting maximum greenness\n mEndDate: Last date of the interval for getting maximum greenness\n \n eStartDate: First date of the interval for getting season end\n eEndDate: Last date of the interval for getting season end\n \n tSos: The offset (%) to add to the start date minimum to set the start of the season\n tEos: The offset (%) to subtract from the end date minimum to set the end of the season\n \"\"\"\n class CropPhenology:\n \n def extractSeasonDates(self, timeseries, args):\n \n if timeseries is None:\n return None\n else:\n\n # Get the local maximum greenness\n mMax = self.getLocalMax(timeseries, pandas.Timestamp(args.year,args.mStart.months,args.mStart.days), pandas.Timestamp(args.year,args.mEnd.months,args.mEnd.days))\n dmMax = mMax['Times']\n ymMax = mMax['Greenness']\n \n # Get the start of season dates\n sos = self.getStartOfSeason(timeseries, pandas.Timestamp(args.year,args.sStart.months,args.sStart.days), pandas.Timestamp(args.year,args.sEnd.months,args.sEnd.days), float(args.tSos), float(ymMax))\n \n # Get the end of season dates\n eos = self.getEndOfSeason(timeseries, pandas.Timestamp(args.year,args.eStart.months,args.eStart.days), pandas.Timestamp(args.year,args.eEnd.months,args.eEnd.days), float(args.tEos), float(ymMax))\n \n #return result\n return [sos[3],eos[3]]\n \n def getLocalMax(self, df, start, end):\n df_range = df.loc[df['Times'].between(start, end)]\n return df_range.loc[df_range['Greenness'].idxmax()]\n \n \"\"\"\n Calculate the start of the season based on selected interval [start, end] and a greenness curve (df). \n Within this interval we will first look for the local minimum greenness, marked by (dsMin, ysMin). In the\n second step we will use the offset (%) to calculate the amount greenness offset that needs to be applied to \n the minumum value in order to get the start of the season. This offset is calculated as a percentage of the \n difference between the maximum greenness and the local minimum.\n \"\"\"\n def getStartOfSeason(self, df, start, end, offset, yMax):\n # Get the local minimum greenness in the start season interval\n df_sRange = df.loc[df['Times'].between(start, end)]\n sMin = df_sRange.loc[df_sRange['Greenness'].idxmin()]\n dsMin = sMin['Times']\n ysMin = sMin['Greenness']\n \n # Calculate the greenness value corresponding to the start of the season\n ySos = ysMin + ((yMax - ysMin) * (offset / 100.0))\n \n # Get the closest value to this greenness\n df_sRange = df_sRange.loc[df_sRange['Times'] >= dsMin]\n sos = df_sRange.iloc[(df_sRange['Greenness'] - ySos).abs().argsort()[:1]]\n return (dsMin, ysMin, ySos, pandas.to_datetime(str(sos['Times'].values[0])))\n \n \"\"\"\n Calculate the end of the season based on selected interval [start, end] and a greenness curve (df). \n Within this interval we will first look for the local minimum greenness, marked by (deMin, yeMin). In the\n second step we will use the offset (%) to calculate the amount greenness offset that needs to be applied to \n the minumum value in order to get the start of the season. This offset is calculated as a percentage of the \n difference between the maximum greenness and the local minimum.\n \"\"\"\n \n def getEndOfSeason(self, df, start, end, offset, yMax):\n # Get the local minimum greenness in the start season interval\n df_eRange = df.loc[df['Times'].between(start, end)]\n eMin = df_eRange.loc[df_eRange['Greenness'].idxmin()]\n deMin = eMin['Times']\n yeMin = eMin['Greenness']\n \n # Calculate the greenness value corresponding to the start of the season\n yEos = yeMin + ((yMax - yeMin) * (offset / 100.0))\n \n # Get the closest value to this greenness\n df_eRange = df_eRange.loc[df_eRange['Times'] <= deMin]\n eos = df_eRange.iloc[(df_eRange['Greenness'] - yEos).abs().argsort()[:1]]\n return (deMin, yeMin, yEos, pandas.to_datetime(str(eos['Times'].values[0]))) \n \n\n \n array=cube.get_array()\n cropphenology=CropPhenology()\n phenologyparams=PhenologypParams(int(array.t.dt.year[0])) \n season=xarray.DataArray(numpy.zeros((2,array.x.shape[0],array.y.shape[0]),dtype=numpy.datetime64),dims=('bands','x', 'y'),coords={'bands': ['sos','eos']})\n \n for ix in array.x.values:\n for iy in array.y.values:\n iserie=pandas.DataFrame(data={\n 'Greenness':array[:,0,ix,iy].values,\n 'Times':array.t.values\n })\n iseason=cropphenology.extractSeasonDates(iserie, phenologyparams)\n #season.values[:,ix,iy]=[iseason[0].dayofyear,iseason[1].dayofyear]\n season.values[:,ix,iy]=iseason\n\n return DataCube(season)","repo_name":"Open-EO/openeo-usecases","sub_path":"vito_phenology_usecase/phenology_usecase/udf_phenology_old.py","file_name":"udf_phenology_old.py","file_ext":"py","file_size_in_byte":6594,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"91"}
+{"seq_id":"11204842297","text":"#!/usr/bin/env python3\n\"\"\" Module tests basic/introductory MondoDB features with pymongo \"\"\"\nfrom typing import Collection\n\n\ndef schools_by_topic(mongo_collection: Collection,\n topic: str) -> list:\n \"\"\" Returns the list of school having a specific topic \"\"\"\n return list(mongo_collection.find({'topics': {'$in': [topic]}}))\n\n\nif __name__ == '__main__':\n \"\"\" Tests the code in this module \"\"\"\n from pymongo import MongoClient\n list_all = __import__('8-all').list_all\n insert_school = __import__('9-insert_school').insert_school\n with MongoClient() as client:\n school_collection = client.my_db.school\n j_schools = [\n {'name': 'Holberton school',\n 'topics': ['Algo', 'C', 'Python', 'React']},\n {'name': 'UCSF', 'topics': ['Algo', 'MongoDB']},\n {'name': 'UCLA', 'topics': ['C', 'Python']},\n {'name': 'UCSD', 'topics': ['Cassandra']},\n {'name': 'Stanford', 'topics': ['C', 'React', 'Javascript']}\n ]\n for j_school in j_schools:\n insert_school(school_collection, **j_school)\n\n schools = schools_by_topic(school_collection, 'Python')\n for school in schools:\n print('[{}] {} {}'.format(school.get('_id'), school.get('name'),\n school.get('topics', '')))\n","repo_name":"adobki/alx-backend-storage","sub_path":"0x01-NoSQL/11-schools_by_topic.py","file_name":"11-schools_by_topic.py","file_ext":"py","file_size_in_byte":1354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"17949708025","text":"\"\"\" Data validation and coercion \"\"\"\nimport re\nfrom datetime import datetime\nfrom dateutil.parser import parse as parse_date\nfrom eth_utils.address import is_address, to_normalized_address\n\n\nclass InvalidInput(ValueError):\n \"\"\" Exception thrown if an input var is the wrong type \"\"\"\n pass\n\n\ndef be_integer(v):\n \"\"\" Try and make v an integer or throw \"\"\"\n if isinstance(v, int):\n return v\n else:\n try:\n return int(v)\n except ValueError:\n raise InvalidInput(\"Input needs to be an integer\")\n\ndef be_string(v):\n \"\"\" Try and make v a string \"\"\"\n if isinstance(v, bytes):\n v = v.decode('utf-8')\n\n if not isinstance(v, str):\n raise InvalidInput(\"Input is not a string\")\n\n return v\n\ndef be_hash(v):\n \"\"\" Make sure v is a hexidecimal hash \"\"\"\n \n v = be_string(v)\n\n if not re.match(r'^(0x)?[A-Fa-f0-9]+$', v):\n raise InvalidInput(\"String is not a hash\")\n\n if len(v) not in (64,66):\n raise InvalidInput(\"Hash is an invalid length\")\n\n # Add the prefix if it doesn't have it\n if v[:2] != '0x':\n return '0x' + v\n else:\n return v\n\ndef be_address(v):\n \"\"\" Make sure v is an Ethereum address \"\"\"\n \n v = be_string(v)\n\n if not is_address(v):\n raise InvalidInput(\"Address is invalid\")\n\n # Add the prefix if it doesn't have it\n return to_normalized_address(v)\n\ndef be_datetime(v):\n \"\"\" Make sure v is a date \"\"\"\n if isinstance(v, datetime):\n return v\n elif isinstance(v, str):\n try:\n return parse_date(v)\n except ValueError as e:\n raise InvalidInput(str(e))\n else:\n raise InvalidInput(\"Unable to parse %s as a date\" % type(v))","repo_name":"mikeshultz/blocksapi","sub_path":"blocksapi/validate.py","file_name":"validate.py","file_ext":"py","file_size_in_byte":1728,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"41930393517","text":"l = '2C7CBi*66iC6C2BBB3i6B36i<;][XJ\\D>AQJ>Q7[\\C;|Q[M]>917,.E.|G]B>S.2X3YXYXXY./YY.2Y3XY32.X.Yl//lmml.63mm2*l6.+7lml622336*26/'\nz = ''\nk = ''\npo = 0\nfor char in l:\n\tz = bin(ord(char))\n\tpo = len(z)\n\tk = k + z[po - 1]\n\n#print (k)\n\n\nm = ''\nj = ''\nfor x in k:\n\tif len(j) < 8:\n\t\tj = j + x\n\telse:\n\t\tm = m + ' ' + j\n\t\tj = x\nprint (m + ' ' + j)\n","repo_name":"rentixeli/CTF","sub_path":"Net-Force 2020/Steganography/7/py.py","file_name":"py.py","file_ext":"py","file_size_in_byte":336,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"7049607450","text":"start_x, start_y = [int(x) for x in input().split()]\nend_x, end_y = [int(x) for x in input().split()]\ndistance = abs(start_x - end_x) + abs(start_y - end_y)\nfuel = int(input())\nif distance > fuel:\n\tprint(\"N\")\nelif (fuel - distance) % 2 == 0:\n\tprint(\"Y\")\nelse:\n\tprint(\"N\")","repo_name":"xCiaraG/Kattis","sub_path":"exactlyelectrical.py","file_name":"exactlyelectrical.py","file_ext":"py","file_size_in_byte":271,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"91"}
+{"seq_id":"1358847889","text":"import itertools\nimport logging\nfrom typing import List\n\nfrom rest_framework import generics\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\n\nfrom musicwire.core.exceptions import (AllPlaylistsAlreadyProcessed,\n AllTracksAlreadyProcessed)\nfrom musicwire.music.filters import CreatedPlaylistFilter, PlaylistTrackFilter, \\\n PlaylistFilter\nfrom musicwire.music.models import (CreatedPlaylist, Playlist, PlaylistTrack,\n SearchErrorTrack)\nfrom musicwire.music.serializers import (AddPlaylistTrackSerializer,\n CreatedPlaylistSerializer,\n CreatePlaylistSerializer,\n PlaylistPostSerializer,\n PlaylistSerializer,\n PulledPlaylistSerializer,\n SearchSerializer, TrackPostSerializer,\n TrackSerializer, SearchErrorSerializer)\nfrom musicwire.provider.models import Provider\n\nlogger = logging.getLogger(__name__)\n\n\nclass PlaylistView(generics.ListAPIView):\n serializer_class = PlaylistSerializer\n filter_backends = [PlaylistFilter]\n\n def get_queryset(self):\n playlists = Playlist.objects.filter(user=self.request.account)\n return playlists\n\n def post(self, request, *args, **kwargs):\n \"\"\"\n This method pull all playlists from provider.\n \"\"\"\n serialized = PlaylistPostSerializer(data=self.request.data)\n serialized.is_valid(raise_exception=True)\n valid_data = serialized.validated_data\n\n source = valid_data['source']\n adapter = Provider.get_provider(\n provider=source,\n token=valid_data['source_token'],\n user=request.account\n )\n\n playlists: list = adapter.playlists()\n if source == Provider.SPOTIFY:\n try:\n Playlist.objects.get(\n remote_id=adapter.saved_tracks_id,\n user=request.account\n )\n except Playlist.DoesNotExist:\n saved_tracks_playlist = Playlist(\n name=\"saved_tracks\",\n status=\"private\",\n remote_id=adapter.saved_tracks_id,\n content=None,\n provider=Provider.SPOTIFY,\n user=request.account\n )\n saved_tracks_playlist.save()\n playlists.append(saved_tracks_playlist)\n\n if not any(playlists):\n raise AllPlaylistsAlreadyProcessed(\n \"All playlist on this account already processed.\"\n )\n\n serialized = PulledPlaylistSerializer({\"playlists\": playlists})\n return Response(serialized.data, status=200)\n\n\nclass PlaylistTrackView(generics.ListAPIView):\n serializer_class = TrackSerializer\n filter_backends = [PlaylistTrackFilter]\n\n def get_queryset(self):\n tracks = PlaylistTrack.objects.select_related('playlist').filter(\n user=self.request.account\n )\n return tracks\n\n def post(self, request, *args, **kwargs):\n \"\"\"\n This method pull all playlist tracks from provider for given playlist.\n \"\"\"\n serialized = TrackPostSerializer(data=self.request.data)\n serialized.is_valid(raise_exception=True)\n valid_data = serialized.validated_data\n\n source, playlist_id = valid_data['source'], valid_data['playlist_id']\n\n adapter = Provider.get_provider(\n provider=source,\n token=valid_data['source_token'],\n user=request.account\n )\n\n if source == Provider.SPOTIFY and playlist_id == \"spotify_saved_tracks\":\n tracks: List[list] = adapter.saved_tracks(playlist_id=playlist_id)\n else:\n tracks: List[list] = adapter.playlist_tracks(playlist_id=playlist_id)\n\n if not any(tracks):\n raise AllTracksAlreadyProcessed(\n \"All tracks on this playlist already processed.\"\n )\n\n # We get list of lists. Due to this condition need to flatten that list.\n merged = list(itertools.chain(*tracks))\n serialized = TrackSerializer(merged, many=True)\n return Response(serialized.data, status=200)\n\n\nclass CreatePlaylistView(generics.ListAPIView):\n serializer_class = CreatedPlaylistSerializer\n filter_backends = [CreatedPlaylistFilter]\n\n def get_queryset(self):\n created_playlists = CreatedPlaylist.objects.filter(\n user=self.request.account\n )\n return created_playlists\n\n def post(self, request, *args, **kwargs):\n serialized = CreatePlaylistSerializer(data=self.request.data)\n serialized.is_valid(raise_exception=True)\n valid_data = serialized.validated_data\n\n adapter = Provider.get_provider(\n provider=valid_data[\"end\"],\n token=valid_data['end_token'],\n user=request.account\n )\n\n playlist = adapter.create_playlist(valid_data)\n\n return Response(playlist, status=200)\n\n\nclass AddTrackToPlaylistView(APIView):\n serializer_class = AddPlaylistTrackSerializer\n\n def post(self, request, *args, **kwargs):\n serialized = self.serializer_class(data=self.request.data)\n serialized.is_valid(raise_exception=True)\n valid_data = serialized.validated_data\n\n adapter = Provider.get_provider(\n provider=valid_data['end'],\n token=valid_data['end_token'],\n user=request.account\n )\n adapter.add_track_to_playlist(valid_data['playlist_id'], valid_data['track_id'])\n\n return Response(status=201)\n\n\nclass SearchView(generics.ListCreateAPIView):\n serializer_class = SearchErrorSerializer\n queryset = SearchErrorTrack.objects.all()\n\n def get_queryset(self):\n return self.queryset.filter(user=self.request.account)\n\n def post(self, request, *args, **kwargs):\n serialized = SearchSerializer(data=self.request.data)\n serialized.is_valid(raise_exception=True)\n valid_data = serialized.validated_data\n\n adapter = Provider.get_provider(\n provider=valid_data['source'],\n token=valid_data['source_token'],\n user=request.account\n )\n response = adapter.search(valid_data['track_name'])\n\n return Response(response, status=200)\n","repo_name":"cihankarluk/Ulfsaar","sub_path":"musicwire/music/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6580,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"}
+{"seq_id":"36340094853","text":"#! /usr/bin/env python3.7\n\nimport argparse\nfrom getpass import getpass\nimport logging\n\nfrom netapp_ontap import config, HostConnection, NetAppRestError\nfrom netapp_ontap.resources import Qtree, QuotaRule, QosPolicy\n\ndef create_qtree(qtree_name: str, volume_name: str, vserver_name: str) -> None:\n \"\"\"Creates a new volume in a SVM\"\"\"\n print(\"Creating QTree...\")\n data = {\n \"name\": qtree_name,\n \"svm\": { \"name\": vserver_name },\n \"volume\": { \"name\": volume_name }\n }\n\n qtree = Qtree(**data)\n\n try:\n qtree.post()\n print(\"qtree %s created successfully\" % qtree.name)\n except NetAppRestError as err:\n print(\"qtree create: %s\" % err)\n return\n\ndef make_qos_policy(vserver_name: str, qos_policy_name: str) -> None:\n print(\"Creating QOS Policy...\")\n data = {\n \"adaptive\": \n {\n \"absolute_min_iops\": 100,\n \"expected_iops\": 100,\n \"peak_iops\": 100\n },\n \"name\": qos_policy_name ,\n \"svm\": { \"name\": vserver_name }\n }\n\n qos = QosPolicy(**data)\n try:\n qos.post()\n print(\"qtree %s created successfully\" % qos.name)\n except NetAppRestError as err:\n print(\"qos policy err: %s\" % err)\n return\n\ndef create_policy_rule(user_name, space_hard_limit, files_hard_limit, qtree_name, volume_name, vserver_name) -> None:\n print(\"Creating Quota Rule...\")\n data = {\n \"files\": { \"hard_limit\": files_hard_limit },\n \"qtree\": { \"name\": qtree_name },\n \"space\": { \"hard_limit\": space_hard_limit },\n \"svm\": { \"name\": vserver_name },\n \"type\": \"user\",\n \"user_mapping\": \"off\",\n \"users\": [{ \"name\": user_name }],\n \"volume\":{ \"name\": volume_name } \n }\n\n qr = QuotaRule(**data)\n\n try:\n qr.post()\n print(\"quota rule for %s created successfully\" % qtree_name)\n except NetAppRestError as err:\n print(\"Error: Quota Rule: %s\" % err)\n return\n\n\ndef parse_args() -> argparse.Namespace:\n \"\"\"Parse the command line arguments from the user\"\"\"\n\n parser = argparse.ArgumentParser(\n description=\"This script will create a new volume.\"\n )\n parser.add_argument(\n \"-c\", \"--cluster\", required=True, help=\"API server IP:port details\"\n )\n parser.add_argument(\n \"-v\", \"--volume_name\", required=True, help=\"Volume to create qtree in\"\n )\n parser.add_argument(\n \"-q\", \"--qtree_name\", required=True, help=\"qtree name\"\n )\n parser.add_argument(\n \"-vs\", \"--vserver_name\", required=True, help=\"SVM to create the qtree in\"\n )\n parser.add_argument(\n \"-sh\", \"--space_hard_limit\", required=True, help=\"qtree space hard limit\"\n )\n parser.add_argument(\n \"-fh\", \"--files_hard_limit\", required=True, help=\"qtree file hard limit\"\n )\n parser.add_argument(\n \"-un\", \"--user_name\", required=True, help=\"username for quota\"\n )\n parser.add_argument(\n \"-qos\", \"--qos_policy_name\", required=True, help=\"name of qos policy to create\"\n )\n parser.add_argument(\"-u\", \"--api_user\",\n default=\"admin\", help=\"API Username\")\n parser.add_argument(\"-p\", \"--api_pass\", help=\"API Password\")\n parsed_args = parser.parse_args()\n\n # collect the password without echo if not already provided\n if not parsed_args.api_pass:\n parsed_args.api_pass = getpass()\n\n return parsed_args\n\n\nif __name__ == \"__main__\":\n logging.basicConfig(\n level=logging.INFO,\n format=\"[%(asctime)s] [%(levelname)5s] [%(module)s:%(lineno)s] %(message)s\",\n )\n args = parse_args()\n config.CONNECTION = HostConnection(\n args.cluster, username=args.api_user, password=args.api_pass, verify=False,\n )\n\n # Create a qtree\n create_qtree(args.qtree_name, args.volume_name, args.vserver_name)\n\n # Set qtree quota\n create_policy_rule(args.user_name, args.space_hard_limit, args.files_hard_limit, args.qtree_name, args.volume_name, args.vserver_name)\n \n # set qos policy\n make_qos_policy(args.vserver_name, args.qos_policy_name)\n","repo_name":"nicholasbernstein/netapp-python-restapi-course-examples","sub_path":"qtree.py","file_name":"qtree.py","file_ext":"py","file_size_in_byte":4206,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"}
+{"seq_id":"7057602122","text":"import torch\nfrom model import Classify\nfrom tqdm import tqdm\nimport numpy as np\nfrom sklearn.metrics import precision_recall_fscore_support\nfrom sklearn.metrics import confusion_matrix\nimport seaborn as sn\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n\nclass Evaluator:\n def __init__(self, params, utils, data_loader):\n self.params = params\n self.utils = utils\n self.data_loader = data_loader\n\n def get_sentences_from_indices(self, docs):\n actual_sentences = []\n for doc, sent_lens in docs:\n sentences = []\n for i, sent in enumerate(doc):\n sentences.append(' '.join([self.data_loader.i2w[int(wid)] for wid in sent[:sent_lens[i]]]))\n actual_sentences.append(sentences)\n return actual_sentences\n\n def _evaluate_aux(self, model, data_loader):\n hits = 0\n total = 0\n all_actual = None\n all_predicted = None\n for sents, lens, labels, adjs in tqdm(data_loader):\n y_batch = self.utils.to_tensor(labels)\n actual_sentences = None\n if self.params.plot == 1:\n actual_sentences = None # self.get_sentences_from_indices(sents)\n\n if self.params.encoder >= 2:\n # This is currently unbatched\n logits = self.utils.get_gcn_logits(model, sents, adjs, actual_sentences)\n else:\n x_batch = self.utils.to_tensor(sents)\n logits = model(x_batch, lens)\n predicted = torch.argmax(logits, dim=1)\n hits += torch.sum(predicted == y_batch).item()\n total += len(sents)\n all_predicted = predicted.cpu().data.numpy() if all_predicted is None else np.concatenate((all_predicted,\n predicted.cpu().data.numpy()))\n all_actual = labels if all_actual is None else np.concatenate((all_actual, labels))\n accuracy = hits / total\n return accuracy, all_actual, all_predicted\n\n def evaluate(self):\n model = Classify(self.params, vocab_size=len(self.data_loader.w2i),\n ntags=self.data_loader.ntags, pte=None)\n if torch.cuda.is_available():\n model = model.cuda()\n # Load the model weights\n model.load_state_dict(torch.load(\"models/\" + self.params.model_file, map_location=lambda storage, loc: storage))\n\n model.eval()\n\n # This dataset is only available for the binary classifier\n if self.params.ntags == 2:\n accuracy, all_actual, all_predicted = self._evaluate_aux(model, self.data_loader.test_data_loader)\n prec_mac, recall_mac, f1_mac, _ = precision_recall_fscore_support(all_actual, all_predicted, average='macro')\n prec_mic, recall_mic, f1_mic, _ = precision_recall_fscore_support(all_actual, all_predicted, average='micro')\n print(\"Accuracy on the OOD test set 1: {}\".format(accuracy))\n print(\"Precision on the OOD test set 1 macro / micro: {}, {}\".format(prec_mac, prec_mic))\n print(\"Recall on the OOD test set 1 macro / micro: {}, {}\".format(recall_mac, recall_mic))\n print(\"F1 on the OOD test set 1 macro / micro: {}, {}\".format(f1_mac, f1_mic))\n\n print(\"----------------------------------------------------------------------\")\n\n accuracy, all_actual, all_predicted = self._evaluate_aux(model, self.data_loader.test_data_loader_2)\n prec_mac, recall_mac, f1_mac, _ = precision_recall_fscore_support(all_actual, all_predicted, average='macro')\n prec_mic, recall_mic, f1_mic, _ = precision_recall_fscore_support(all_actual, all_predicted, average='micro')\n print(\"Accuracy on the OOD test set 2: {}\".format(accuracy))\n print(\"Precision on the OOD test set 2 macro / micro: {}, {}\".format(prec_mac, prec_mic))\n print(\"Recall on the OOD test set 2 macro / micro: {}, {}\".format(recall_mac, recall_mic))\n print(\"F1 on the OOD test set 2 macro / micro: {}, {}\".format(f1_mac, f1_mic))\n\n if self.params.ntags == 4:\n results = confusion_matrix(all_actual, all_predicted)\n df_cm = pd.DataFrame(results, index=[i for i in [\"Satire\", \"Hoax\", \"Propaganda\", \"Trusted\"]],\n columns=[i for i in [\"Satire\", \"Hoax\", \"Propaganda\", \"Trusted\"]])\n sns_plot = sn.heatmap(df_cm, annot=True, fmt='g')\n plt.yticks(rotation=45)\n sns_plot.get_figure().savefig('plots/cm.png')\n\n print(\"----------------------------------------------------------------------\")\n\n accuracy, all_actual, all_predicted = self._evaluate_aux(model, self.data_loader.dev_data_loader)\n prec_mac, recall_mac, f1_mac, _ = precision_recall_fscore_support(all_actual, all_predicted, average='macro')\n prec_mic, recall_mic, f1_mic, _ = precision_recall_fscore_support(all_actual, all_predicted, average='micro')\n print(\"Accuracy on the dev set: {}\".format(accuracy))\n print(\"Precision on the dev set macro / micro: {}, {}\".format(prec_mac, prec_mic))\n print(\"Recall on the dev macro / micro: {}, {}\".format(recall_mac, recall_mic))\n print(\"F1 on the dev macro / micro: {}, {}\".format(f1_mac, f1_mic))\n\n\n","repo_name":"MysteryVaibhav/fake_news_semantics","sub_path":"evaluator.py","file_name":"evaluator.py","file_ext":"py","file_size_in_byte":5319,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"91"}
+{"seq_id":"25434382856","text":"class Solution:\n def merge(self, intervals: List[List[int]]) -> List[List[int]]:\n # greedy problem\n # sort the intervals by starting point, create a pivot ending point\n # update pivot point when iterating the intervals\n # when the current starting point is larger than the pivot point, create a new interval\n \n res = []\n intervals = sorted(intervals, key=lambda x: x[0])\n res.append(intervals[0])\n \n for i in range(1, len(intervals)):\n if intervals[i][0] <= res[-1][1]:\n res[-1] = [res[-1][0], max(res[-1][1], intervals[i][1])]\n else:\n res.append(intervals[i])\n return res","repo_name":"yaoyuan0608/leetcode-daily","sub_path":"codes/0-200/56_Merge_Intervals.py","file_name":"56_Merge_Intervals.py","file_ext":"py","file_size_in_byte":704,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"23561115278","text":"from prometheus_client import start_http_server, Summary, Gauge, Info\r\nimport random\r\nimport time\r\n\r\nimport requests\r\nimport os\r\nfrom os.path import join, dirname\r\nfrom dotenv import load_dotenv\r\nimport json\r\nfrom datetime import datetime\r\nfrom sys import platform\r\nimport logging\r\nfrom cachetools import cached, TTLCache\r\n\r\ndef cur_price(data, time):\r\n result = 101.00\r\n for a in data:\r\n if a['valid_from'] == time:\r\n logging.debug(\r\n \"Located: {valid_from} {data}\".format(valid_from=a['valid_from'], data=a))\r\n result = a\r\n break\r\n\r\n return result\r\n\r\n\r\n@cached(cache=TTLCache(maxsize=2048, ttl=1800))\r\ndef fetch_unit_rates(): # GET current tariff data from Octopus API\r\n url = \"{url}/v1/products/{prod_code}/electricity-tariffs/{tariff}/standard-unit-rates\".format(\r\n url=os.getenv('BASE_URL'),\r\n prod_code=os.getenv(\r\n 'PRODUCT_CODE'),\r\n tariff=os.getenv('TARIFF'))\r\n logging.debug(\"GET URL \" + url)\r\n\r\n response = requests.get(url)\r\n if response.status_code == 200: # if success, continue. If not, die.\r\n return json.loads(response.text)\r\n else:\r\n logging.error(\"Unable to get rates. Response code {code}. Exiting\".format(code=str(response.status_code)))\r\n exit(0)\r\n\r\n\r\n@cached(cache=TTLCache(maxsize=2048, ttl=32000))\r\ndef fetch_consumption(): # GET consumption data from Octopus API\r\n url = \"{url}/v1/electricity-meter-points/{mpan}/meters/{serial_number}/consumption/\" \\\r\n \"?group_by=day&page_size=2&order_by=-period\".format(\r\n url=os.getenv('BASE_URL'), mpan=os.getenv('MPAN'), serial_number=os.getenv('ELEC_SERIAL_NUMBER'))\r\n logging.debug(\"GET URL \" + url)\r\n key = os.getenv('KEY')\r\n response = requests.get(url=url, auth=(key, ''))\r\n if response.status_code == 200: # if success, continue. If not, die.\r\n return json.loads(response.text)\r\n else:\r\n logging.error(\"Unable to get rates. Response code {code}. Exiting\".format(code=str(response.status_code)))\r\n exit(0)\r\n\r\n\r\n# Create a metric to track time spent and requests made.\r\nCURRENT_PRICE_INC_VAT = Gauge('current_electricity_price_inc_vat', 'Current Electricity price including VAT')\r\nCURRENT_PRICE_EXC_VAT = Gauge('current_electricity_price_exc_vat', 'Current Electricity price excluding VAT')\r\nCONSUMPTION_YESTERDAY = Gauge('electricity_consumption_yesterday', 'Yesterdays Energy Use.')\r\n\r\n\r\n# Decorate function with metric.\r\n\r\n\r\ndef set_current_price(time, unit_rates):\r\n price = cur_price(data=unit_rates,\r\n time=time.isoformat() + \"Z\") # Octopus adds \"Z\" to end of ISO Time, python doesn't\r\n CURRENT_PRICE_INC_VAT.set(price['value_inc_vat'])\r\n CURRENT_PRICE_EXC_VAT.set(price['value_exc_vat'])\r\n\r\n\r\ndef set_consumption(consumption):\r\n CONSUMPTION_YESTERDAY.set(consumption[1]['consumption']) # Yesterdays value is second result on first page.\r\n\r\n\r\nif __name__ == '__main__':\r\n path = join(dirname(__file__), '.env')\r\n logging.basicConfig(filename='boinc{date}.log'.format(date=datetime.now().date()), format='%(asctime)s %(message)s',\r\n level=logging.DEBUG)\r\n load_dotenv(path)\r\n\r\n # Start up the server to expose the metrics.\r\n start_http_server(8000)\r\n\r\n while True:\r\n rates = fetch_unit_rates()['results']\r\n consumption = fetch_consumption()['results']\r\n now_utc = datetime.utcnow()\r\n\r\n if (30, 0) in now_utc.minute:\r\n set_current_price(rd_utc, rates)\r\n set_consumption(consumption)\r\n time.sleep(30)\r\n","repo_name":"grantr98/octopus-boinc","sub_path":"boinc_prometheus.py","file_name":"boinc_prometheus.py","file_ext":"py","file_size_in_byte":3602,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"19174930392","text":"sonlar = list(range(11)) \r\n\r\ndef daraja2(x):\r\n \"\"\"Berilgan sonning kvadratini qaytaruvchi funksiya\"\"\"\r\n return x*x\r\n\r\nprint(list(map(daraja2,sonlar)))\r\n# sonlarningkvadratini chiqarib beruvchi codlar 11 gacha bulgan sonlarni\r\n\r\na = [4, 5, 6]\r\nb = [7, 8, 9]\r\na_plus_b = list(map(lambda x,y:x+y,a,b))\r\nprint(a_plus_b)\r\n# bu yerda a dagi sonlarni b dagi sonlarga qushadi\r\n\r\nimport random\r\ndef sontop(x = 10):\r\n tasodifiy_son = random.randint(1,x)\r\n print(f\"Men 1 dan {x} gacha son o'yladim. Topishga harakat qiling? :\")\r\n taxminlar = 1\r\n while True:\r\n taxminlar += 1\r\n taxmin = int(input(\"-->\"))\r\n if taxmin < tasodifiy_son:\r\n print(\"Xato man o'ylagan son bundan kattaroq. Yana harakat qiling:\")\r\n elif taxmin > tasodifiy_son:\r\n print(\"Xato man o'ylagan son bundan kichikroq. Yana harakat qiling:\")\r\n else:\r\n break\r\n return taxminlar\r\nprint(\"Siz {taxminlar} taxmin bilan topdingiz\")\r\nsontop()\r\n# so'z o'yini kampyuter so'z uylaydi siz uni topishiingiz kerak","repo_name":"teamakosh/Python-darslari","sub_path":"python_darslar/Python-darslari/dars25.py","file_name":"dars25.py","file_ext":"py","file_size_in_byte":995,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"7266693213","text":"import re\nimport itertools as it\n\ndef check_overleap(first_list, second_list):\n for first in range(first_list[0], first_list[1] + 1):\n for second in range(second_list[0], second_list[1] + 1):\n if (first == second):\n return 1\n return 0\n\ndef solution():\n with open(\"input\") as file:\n input_lines = [line.strip() for line in file]\n\n counter = 0\n for line in input_lines:\n numbers = re.search(\"([0-9]+)-([0-9]+),([0-9]+)-([0-9]+)\", line)\n first_list = [int(numbers.group(1)), int(numbers.group(2))]\n second_list = [int(numbers.group(3)), int(numbers.group(4))]\n counter = counter + check_overleap(first_list, second_list)\n return counter\n\nif __name__ == '__main__':\n print(solution())\n","repo_name":"mvjkel1/advent-of-code-2022","sub_path":"solution_04-part2.py","file_name":"solution_04-part2.py","file_ext":"py","file_size_in_byte":770,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"43462128016","text":"import torch.nn as nn\nfrom config import cfg\n\n\ndef init_param(m):\n if isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d)):\n m.weight.data.fill_(1)\n m.bias.data.zero_()\n elif isinstance(m, nn.Linear):\n m.bias.data.zero_()\n return m\n\n\nclass CNN(nn.Module):\n def __init__(self, data_shape, hidden_size, classes_size):\n super(CNN, self).__init__()\n # torch.manual_seed(cfg['model_init_seed'])\n\n # head\n blocks = [nn.Conv2d(data_shape[0], hidden_size[0], 3, 1, 1)]\n\n for i in range(len(hidden_size) - 1):\n blocks.extend([\n nn.Conv2d(hidden_size[i], hidden_size[i + 1], 3, 1, 1),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=2, stride=2)\n ])\n\n # classifier\n blocks.extend([\n nn.Flatten(),\n nn.Linear(\n int(hidden_size[-1] * cfg['data_shape'][1] * cfg['data_shape'][2] / (4 ** (len(hidden_size)-1))),\n classes_size\n )\n ])\n\n self.blocks = nn.Sequential(*blocks)\n\n def forward(self, x):\n output = self.blocks(x)\n return output\n","repo_name":"slwang-ustc/FL_PS_MPI_client_selection","sub_path":"models/cnn.py","file_name":"cnn.py","file_ext":"py","file_size_in_byte":1165,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"27093219774","text":"import tensorflow as tf\nimport numpy as np\nimport tensorflow.keras.backend as kb\nimport os\nimport tempfile\n\nclass EWC_Network():\n \"\"\"Use Elastic Weight Consolidation (EWC) in a Keras sequential model.\n\n Usage:\n The EWC Network is built from a Keras sequential model. The EWC \n regularization is applied only to the Dense and Conv2D layers of the \n input model.\n\n Tasks can be added to the network by defining their training and \n test data, as well as a task name. The metrics (at the moment only \n loss & accuracy) for each trained task can be recorded while training \n new tasks, giving the user a sense of how the performance of the\n network degrades on previous tasks as new ones are learnt.\n\n A typical use case could be:\n 1. Create a Keras sequential model\n 2. Instantiate EWC_Network based on said model\n 3. Add tasks to the EWC_Network\n 4. Train the EWC_Network on all tasks\n 5. Analyze the performance \n \"\"\"\n\n class _MultipleValidationSets(tf.keras.callbacks.Callback):\n # Callback used to record metrics for multiple test sets,\n # i.e. test sets for all the tasks that have been trained already.\n # Mostly copied from:\n # https://stackoverflow.com/questions/47731935/using-multiple-validation-sets-with-keras\n\n def __init__(self, validation_sets, verbose=0, batch_size=None):\n super(EWC_Network._MultipleValidationSets, self).__init__()\n self.validation_sets = validation_sets\n for validation_set in validation_sets:\n assert(len(validation_set) == 3)\n self.epoch = []\n self.history = {}\n self.verbose = verbose\n self.batch_size = batch_size\n\n def on_train_begin(self, logs=None):\n self.epoch = []\n self.history = {}\n\n def on_epoch_end(self, epoch, logs=None):\n logs = logs or {}\n self.epoch.append(epoch)\n \n # Iterate over all the validation sets on which we should\n # record metrics, and store them in history\n for validation_set in self.validation_sets:\n validation_data, validation_target, set_name = validation_set\n \n results = self.model.evaluate(\n x = validation_data,\n y = validation_target,\n verbose = self.verbose,\n batch_size = self.batch_size\n )\n\n for i, result in enumerate(results):\n valuename = self.model.metrics[i].name\n self.history.setdefault(set_name, {}).setdefault(\n valuename, []\n ).append(result)\n \n class _EWC_Regularizer(tf.keras.regularizers.Regularizer):\n # This class represents the regularization function in EWC, i.e.\n # the penalty term associated with the current parameters based \n # on how much they differ from previously trained parameters for \n # different tasks.\n def __init__( self, \n constant = 1,\n ):\n self.constant = constant\n self.trained_parameters_per_task = []\n self.fisher_diagonal_per_task = []\n \n def __call__(self, weights):\n penalty = 0 \n # We want to add one penalty term for each previously learned\n # task, so we must iterate over them and compute sum the \n # individual penalties.\n for params, fisher in zip(\n self.trained_parameters_per_task, self.fisher_diagonal_per_task\n ):\n penalty += (\n (self.constant / 2) * tf.reduce_sum(\n tf.multiply(fisher, tf.square(weights - params))\n )\n )\n return penalty\n\n def set_parameters(self, parameters):\n self.trained_parameters_per_task = parameters\n\n def set_fisher(self, fisher):\n self.fisher_diagonal_per_task = fisher\n\n def get_constant(self):\n return self.constant\n \n def set_constant(self, constant):\n self.constant = constant\n\n def get_config(self):\n # Required for saving, e.g. as JSON.\n return {'constant': self.constant}\n\n @classmethod\n def from_config(cls, config):\n # Required for restoring.\n return cls(**config)\n\n\n def __init__(\n self,\n model, \n n_epochs, \n batch_size, \n ewc_lambda, # Tune this\n learning_rate = 1e-4,\n num_tasks_to_remember = -1, # Default is remember all tasks\n gradient_batch_size = 1,\n ):\n \"\"\"Constructor for the EWC Network\n\n Parameters:\n model: Sequential Keras model (not compiled - \n it will be recompiled with ADAM \n optimizer and metrics = ['accuracy'])\n n_epochs: Number of epochs to train each task\n batch_size: Batch size used in training\n ewc_lambda: Constant multiplier used on the EWC \n regularization term. Higher values mean\n an increased importance of remembering \n old tasks compared to performing well\n on new ones. This should be tuned for\n the user's purposes.\n learning_rate: Learning rate used in training,\n default 1e-4\n num_tasks_to_remember: Number old tasks to remember when \n training a new task (-1 for remembering \n everything, 0 for remembering nothing),\n default -1.\n \n gradient_batch_size: Batch size used when computing the \n gradient used for the Empirical Fisher \n Matrix, default 1.\n \n \"\"\"\n \n self.n_epochs = n_epochs \n self.batch_size = batch_size \n self.learning_rate = learning_rate \n\n self._tasks = [] \n self._model = model \n # Num_tasks_to_remember must be: -1 for all tasks, else > 0\n assert(num_tasks_to_remember >= -1) \n self._num_tasks_to_remember = num_tasks_to_remember\n\n self.ewc_lambda = ewc_lambda \n\n # A note on gradient_batch_size:\n # The Empirical Fisher Matrix (or in this case, its diagonal) is\n # based on computing gradients of the loss function. For computing\n # the empirical Fisher diagonal, the loss gradients for each data\n # point is squared and they are then added together (and normalized\n # by dividing with the number of data points). \n # Now, this gradient can be computed on batches of the input data. \n # This is essentially the average (or sum - this can be controlled \n # by the user) of the loss gradient for each data point in the batch. \n # This is a lot quicker and is fine for the purposes of EWC, as the \n # average gradient will still be representative of how \"important\" a \n # particular weight is for a given task. However, performing the \n # computation in batches will affect the magnitude of the computed \n # penalty, since \n # ((g_1 + ... + g_n)/n)**2 != (g_1**2 + ... + g_n**2).\n # this can be remedied *somewhat* by proper normalization. \n # Thus, tuning the constant used in the penalty calculation,\n # ewc_lambda, requires taking the gradient batch size into \n # consideration.\n #\n # I've opted for computing the Fisher matrix \"properly\" with a \n # default batch size of 1.\n\n self.gradient_batch_size = gradient_batch_size \n \n # Initialize a field to keep track of which layers use EWC\n self._determine_ewc_layers() \n\n # Set EWC regularization function for relevant layers\n self._set_regularization_functions()\n\n # Compile the model\n self._compile_model()\n\n print('Model built. Here is a summary:',end='\\n\\n')\n self._model.summary(print_fn=lambda s: print('\\t' + s))\n print()\n\n \n \n \n def _determine_ewc_layers(self):\n # Determine which layers should use the EWC regularizer.\n # For now, the user has no choice. All Dense and Conv2D \n # layers will use EWC.\n self._ewc_layer_indexes = []\n for idx, layer in enumerate(self._model.layers):\n if (\n isinstance(layer, tf.keras.layers.Conv2D) or \n isinstance(layer, tf.keras.layers.Dense)\n ):\n self._ewc_layer_indexes.append(idx)\n\n def _set_regularization_functions(self):\n for layer_idx in self._ewc_layer_indexes:\n layer = self._model.get_layer(index=layer_idx)\n layer.bias_regularizer = self._EWC_Regularizer(constant=self.ewc_lambda)\n layer.kernel_regularizer = self._EWC_Regularizer(constant=self.ewc_lambda)\n \n # \"Applying\" these changes is more complicated than you would expect, \n # because keras does not do it for you (even with model.compile()).\n # We have to perform the obtuse process of saving the model config (and \n # weights if we had any pre-set weights) and then reloading it... \n # Since we're only doing it at the beginning we may actually skip the \n # weights. If any user would like to incorporate a model with \n # pre-trained weights, they should un-comment and test the lines \n # associated with backing up and restoring weights.\n \n # This solution feels very ugly, but is apparently the way to do it. \n # At least we don't have to do it between tasks...\n # https://sthalles.github.io/keras-regularizer/\n self._reload_model()\n\n def _reload_model(self):\n # Save config\n model_json = self._model.to_json()\n # Save weights\n #tmp_weights_path = os.path.join(tempfile.gettempdir(), 'weights.h5')\n #self._model.save_weights(tmp_weights_path)\n\n # Reload from config & restore weights\n with tf.keras.utils.custom_object_scope(\n {\"_EWC_Regularizer\": self._EWC_Regularizer}\n ):\n self._model = tf.keras.models.model_from_json(model_json)\n #self._model.load_weights(tmp_weights_path, by_name=True)\n\n \n def _update_regularization_functions(self):\n # Update the regularization functions for each layer with the \n # parameters of the n latest learned tasks\n\n # Determine all tasks that have been learned so far\n available = [\n i for i, task in enumerate(self._tasks) \n if 'trained_parameters' in task\n ]\n\n # Determine which tasks should be remembered at this step\n # depending on num_tasks_to_remember\n if(\n self._num_tasks_to_remember == -1 or \n # This second check is actually not needed because \n # if a = [1, 2, 3] then e.g. a[-54] is still [1, 2, 3]\n self._num_tasks_to_remember > len(available) \n ):\n # Remember everything that has been learned\n tasks_to_remember = available\n elif self._num_tasks_to_remember == 0:\n tasks_to_remember = []\n else:\n # Only remember the latest num_tasks_to_remember tasks\n tasks_to_remember = available[-self._num_tasks_to_remember:]\n \n # Now add the learned parameters and fisher diagonal\n # for these tasks to each layer that uses EWC\n for idx in self._ewc_layer_indexes:\n layer = self._model.get_layer(index=idx)\n kernel_params = [\n self._tasks[task_id]['trained_parameters'][idx]['kernel']\n for task_id in tasks_to_remember\n ]\n kernel_fisher = [\n self._tasks[task_id]['fisher_diagonal'][idx]['kernel']\n for task_id in tasks_to_remember\n ]\n\n layer.kernel_regularizer.set_parameters(kernel_params)\n layer.kernel_regularizer.set_fisher(kernel_fisher)\n\n bias_params = [\n self._tasks[task_id]['trained_parameters'][idx]['bias']\n for task_id in tasks_to_remember\n ]\n bias_fisher = [\n self._tasks[task_id]['fisher_diagonal'][idx]['bias']\n for task_id in tasks_to_remember\n ]\n\n layer.bias_regularizer.set_parameters(bias_params)\n layer.bias_regularizer.set_fisher(bias_fisher)\n\n def _compile_model(self):\n # Compile the model with default optimizer and accuracy metrics\n # In a future version, the user could be given more control over this\n self._model.compile(\n optimizer = tf.keras.optimizers.Adam(\n learning_rate=self.learning_rate\n ),\n loss='categorical_crossentropy',\n metrics=['accuracy']\n )\n\n def train_model(self, record_history = False):\n \"\"\" Train the model on the tasks that have been added, (in that order).\n\n Parameters:\n record_history: If set to True, metrics will be recorded while\n training. Specifically, metrics are recorded\n for any task that is currently being trained,\n or has already been trained, on. Default is \n False.\n \"\"\"\n\n for i, task in enumerate(self._tasks):\n print( 'Training on', \n self._get_name_or_id(task), \n flush=True\n )\n\n if record_history:\n task['history'] = {}\n # We only care about the validation sets for the\n # datasets we have trained the model on so far\n validation_data = [\n (task['X_test'], task['Y_test'], self._get_name_or_id(task))\n for task in self._tasks[:i+1]\n ]\n history_callback = self._MultipleValidationSets(validation_data)\n callbacks = [history_callback]\n else:\n callbacks = None\n\n\n self._model.fit(\n task['X_train'], \n task['Y_train'],\n epochs = self.n_epochs,\n batch_size=self.batch_size,\n verbose=True,\n callbacks=callbacks\n )\n\n if record_history:\n # Save the results of all the relevant tasks\n for trained_task in self._tasks[:i+1]:\n history_from_trained_task = history_callback.history[\n self._get_name_or_id(trained_task)\n ]\n for metric, values in history_from_trained_task.items():\n trained_task['history'].setdefault(metric, []).extend(\n values\n )\n\n print('\\rTraining on', self._get_name_or_id(task), 'done!')\n\n # After training, we want to save these \"optimal\" parameters for\n # the task, as well as the (empirical) Fisher diagonal\n self._store_trained_parameters(task)\n self._compute_fisher(task)\n\n # After model has been trained, we need to update the regularization\n # functions for the EWC layer to take into account the most recently\n # trained task.\n self._update_regularization_functions()\n # Recompilation is needed after updating the regularization\n # function. It does not change the current weights.\n self._compile_model()\n\n def _store_trained_parameters(self, task):\n # Store the parameters that have been learned from a task\n # and associate them with the relevant layer\n\n parameters_per_layer = {}\n\n for idx in self._ewc_layer_indexes:\n weights = self._model.get_layer(index=idx).get_weights()\n kernel_weights = weights[0]\n bias_weights = weights[1]\n\n parameters_per_layer[idx] = {\n 'kernel': tf.convert_to_tensor(np.copy(kernel_weights)),\n 'bias': tf.convert_to_tensor(np.copy(bias_weights))\n }\n \n task['trained_parameters'] = parameters_per_layer\n\n def _compute_fisher(self, task):\n print('\\nComputing empirical Fisher - this may take a while')\n data = task['X_train']\n labels = task['Y_train']\n\n # Split the data based on the desired batch size\n num_splits = int(data.shape[0] / self.gradient_batch_size)\n data_split = np.array_split(data, num_splits)\n label_split = np.array_split(labels, num_splits)\n\n # Determine the shape of the returned gradient and initialize\n # data structure that will hold the cumulative gradients data\n # w.r.t. the batches \n layer_weights = {\n idx: {\n 'kernel': self._model.get_layer(index=idx).weights[0],\n 'bias': self._model.get_layer(index=idx).weights[1]\n }\n for idx in self._ewc_layer_indexes \n }\n\n sums = {\n idx: {\n 'kernel': np.zeros(shape = layer_weights[idx]['kernel'].shape),\n 'bias': np.zeros(shape = layer_weights[idx]['bias'].shape)\n }\n for idx in self._ewc_layer_indexes\n }\n\n # Compute gradient for each batch\n for i, (batch_data, batch_label) in enumerate(\n zip(data_split, label_split)\n ):\n progress = round(i / num_splits * 100)\n print( '\\r[{0}{1}] {2}%'.format('#'*int(progress/10), \n ' '*(10 - int(progress/10)), progress), \n end=''\n )\n with tf.GradientTape() as tape:\n predictions = self._model(batch_data)\n # Loss for the batch\n loss = tf.keras.losses.categorical_crossentropy(\n batch_label, \n predictions\n # If we want to control how the loss is calculated for \n # a batch we can do it here, for example using\n # reduction=tf.keras.losses.Reduction.SUM\n # or\n # reduction=tf.keras.losses.Reduction.NONE\n # see:\n # https://keras.io/api/losses/probabilistic_losses/#categoricalcrossentropy-class\n )\n\n # Using tape.gradients() directly on the loss tensor will\n # sum all the gradients together, which may not be ideal,\n # but speeds up computation. Another consequence of this\n # is that the batch size in the gradient computation\n # affects what the value lambda in the regularization \n # function should be. We try to avoid this by dividing the \n # gradient by the batch size, thus getting the \n # \"average gradient\" for that batch.\n # For an exact (empirical) Fisher, use a batch size of 1.\n gradient = tape.gradient(\n loss, \n layer_weights\n )\n\n for idx in self._ewc_layer_indexes:\n sums[idx]['kernel'] += gradient[idx]['kernel']**2\n sums[idx]['bias'] += gradient[idx]['bias']**2\n \n\n # Divide by number of splits to normalize, note that \n # this normalization will not yield comparable results \n # for different batch sizes (so it's somewhat pointless)!\n for idx in self._ewc_layer_indexes:\n sums[idx]['kernel'] /= num_splits\n sums[idx]['bias'] /= num_splits\n\n task['fisher_diagonal'] = sums\n print()\n\n def add_task(\n self, \n X_train, Y_train, X_test, Y_test, \n name = ''\n ):\n \"\"\"Add a task to the model.\n\n Parameters:\n X_train: Training data\n Y_train: Training labels\n X_test: Test data\n Y_test: Test labels\n name: reader-friendly name for the task\n \"\"\"\n self._tasks.append({\n 'X_train': X_train, # Training input\n 'Y_train': Y_train, # Training output\n 'X_test': X_test, # Testing input\n 'Y_test': Y_test, # Testing output\n\n 'meta': { # Info unrelated to network\n 'id': len(self._tasks), # Id of the task\n 'name': name # Human-readable task name\n }\n })\n\n def evaluate(self):\n \"\"\"Evaluate the network's performance sequentially on all tasks\"\"\"\n\n print('\\n##########################################')\n print('Evaluating all tasks\\n')\n\n for task in self._tasks:\n print('Evaluating', self._get_name_or_id(task), end='...\\n')\n loss, accuracy = self._model.evaluate(\n task['X_test'], \n task['Y_test'], \n verbose=False\n )\n print(\n '\\taccuracy:', round(accuracy, 4)\n )\n print(\n '\\tloss:', round(loss, 4)\n )\n print('##########################################\\n')\n\n def get_history(self):\n \"\"\"Get the recorded history for each task. If the network is trained\n with record_history = True, the history is recorded for each epoch\n that a task is either being trained, or has already been trained, on.\n\n Returns:\n A list of dictionaries with the following structure\n {\n name: ,\n history: {\n : \n }\n }\n\n If history has not been recorded for any task, an empty list\n is returned.\n \n Example output (two tasks are trained for three epochs each):\n [\n {\n name: \"First task\",\n history: {\n loss: [0.2, 0.1, 0.05, 0.04, 0.02, 0.01]\n accuracy: [0.88, 0.94, 0.96, 0.95, 0.92, 0.92]\n }\n },\n {\n name: \"Second task\",\n history: {\n loss: [0.45, 0.4, 0.32],\n accuracy: [0.84, 0.92, 0.95]\n }\n }\n ]\n \"\"\"\n ret_list = []\n for task in self._tasks:\n if 'history' in task:\n ret_list.append({\n 'name': self._get_name_or_id(task),\n 'history': task['history']\n })\n return ret_list\n\n def _get_name_or_id(self, task):\n if task['meta']['name']:\n return task['meta']['name']\n else:\n return 'task ' + task['meta']['id']\n","repo_name":"svantesorberg/ewc-project","sub_path":"code/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":23815,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"35104166288","text":"class Solution:\n def dividePlayers(self, skills: List[int]) -> int:\n skills.sort()\n print(skills)\n l=1\n r=len(skills)-2\n temp=skills[0]+skills[-1]\n ans=skills[0]*skills[-1]\n while l 5:\n messageUpdate(lcd, 'Please scan', 'your card.')\n elif user_credentials[3] == True:\n messageUpdate(lcd, \"Welcome Home\", str(user_credentials[1]) )\n \n else:\n messageUpdate(lcd, 'Unauthorized User', 'Please Wait')\n\n destroy(lcd)\n ","repo_name":"mdlopezme/Lab-6","sub_path":"Midterm/modules/LCD.py","file_name":"LCD.py","file_ext":"py","file_size_in_byte":1625,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"29873778393","text":"import numpy as np\r\nimport cv2 as cv\r\n\r\nhaar_cascade = cv.CascadeClassifier('haar_face.xml')\r\npeople = ['Akshay', 'Allu', 'Hrithik', 'Rashmika']\r\n\r\n# features = np.load('features.npy')\r\n# labels = np.load('labels.npy')\r\n\r\nface_recognizer = cv.face.LBPHFaceRecognizer_create()\r\nface_recognizer.read('face_trained.yml')\r\n\r\nimg = cv.imread(r'D:\\STUDY MATERIAL\\OpenCV\\Photos\\val\\Akshay\\download.jpg')\r\n\r\ngray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\r\ncv.imshow(\"Gray\", gray)\r\n\r\n# Detect the faces in the image\r\nfaces_rect = haar_cascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=3)\r\n\r\nfor (x,y,g,h) in faces_rect:\r\n faces_roi = gray[x:x+g, y:y+h]\r\n\r\n label, confidence = face_recognizer.predict(faces_roi)\r\n print(f'label = {people[label]} with a confidence of {confidence}')\r\n\r\n cv.putText(img, str(people[label]), (20,20), cv.FONT_HERSHEY_TRIPLEX, 1.0, (0,255,0), thickness=2)\r\n cv.rectangle(img, (x,y), (x+g,y+h), (0,255,0), thickness=2)\r\n\r\ncv.imshow(\"Detected Face\", img)\r\ncv.waitKey(0)","repo_name":"JagratSingh/Robotics-Projects","sub_path":"Projects/Vision system to Recognize Faces/2. Face Recognition.py","file_name":"2. Face Recognition.py","file_ext":"py","file_size_in_byte":1009,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"73872906501","text":"from django.shortcuts import render, redirect\nfrom .forms import DocumentForm\nfrom .utils import perform_data_cleaning\nfrom django.shortcuts import render\n\n\ndef model_form_upload(request):\n if request.method == 'POST':\n form = DocumentForm(request.POST, request.FILES)\n if form.is_valid():\n form.save()\n perform_data_cleaning(form.cleaned_data)\n return redirect('home')\n else:\n form = DocumentForm()\n return render(request, 'model_form_upload.html', {\n 'form': form\n })\n\n\ndef index_view(request):\n return render(request, 'home.html')\n\n","repo_name":"Adityaraj1711/mastercode","sub_path":"specs/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":611,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"7796894593","text":"from seis_proc_dl.apply_to_continuous import apply_detectors\nimport glob\nimport argparse\nimport datetime\nimport os\n\n# Path to the miniseed files\ndata_dir = \"/uufs/chpc.utah.edu/common/home/koper-group3/alysha/ys_data/downloaded_all_data\"\n# Path to the trained detector models\nmodels_path = \"/uufs/chpc.utah.edu/common/home/koper-group3/alysha/selected_models\"\n# Specific phase models\np_model = os.path.join(models_path, \"pDetectorMew_model_026.pt\")\nonec_p_model = os.path.join(models_path, \"oneCompPDetectorMEW_model_022.pt\")\ns_model = os.path.join(models_path, \"sDetector_model032.pt\")\n# The size of the detector input in samples\nwindow_length = 1008\n# The length of the sliding window in samples\nsliding_interval = 500\n# The samples +- the center of the detector output to return \ncenter_window = 250 \n# The number of samples on either side of the center window \nwindow_edge_npts = 254\n# The output directory for the posterior probs and the waveform metadata \noutdir = \"/uufs/chpc.utah.edu/common/home/koper-group3/alysha/ys_data/detector_output\"\n\ndef load_1c_detector():\n \"\"\"Initialize the 1C P Phase Detector for CHPC\n\n Returns:\n object: One Component P Phase Detector\n \"\"\"\n pd = apply_detectors.PhaseDetector(onec_p_model,\n 1,\n \"P\",\n min_presigmoid_value=-70,\n device=\"cpu\",\n num_torch_threads=2)\n return pd\n\ndef load_3c_detectors():\n \"\"\"Initialize the 3C P and S Phase Detectors for CHPC\n\n Returns:\n tuple: 3C P Detector, 3C S Detector\n \"\"\"\n p_pd = apply_detectors.PhaseDetector(p_model,\n 3,\n \"P\",\n min_presigmoid_value=-70,\n device=\"cpu\",\n num_torch_threads=2)\n \n s_pd = apply_detectors.PhaseDetector(s_model,\n 3,\n \"S\",\n min_presigmoid_value=-70,\n device=\"cpu\",\n num_torch_threads=2)\n \n return p_pd, s_pd\n\n### Handle user inputs for when to run the detector and for which station ###\nargParser = argparse.ArgumentParser()\nargParser.add_argument(\"-s\", \"--stat\", type=str, help=\"station code\")\nargParser.add_argument(\"-c\", \"--chan\", type=str, help=\"First two letter of the channel code\")\nargParser.add_argument(\"-y\", \"--year\", type=int, help=\"Start year\")\nargParser.add_argument(\"-m\", \"--month\", type=int, help=\"Start month\")\nargParser.add_argument(\"-d\", \"--day\", type=int, help=\"Start day\")\nargParser.add_argument(\"-n\", \"--n\", type=int, help=\"The number of days to analyze\")\n\nargs = argParser.parse_args()\n\nassert args.year >= 2002 and args.year <= 2022, \"Year is invalid\"\nassert args.month > 0 and args.month < 13, \"Month is invalid\"\nassert args.day > 0 and args.day <= 31, \"Day is invalid\"\nassert args.n > 0, \"Number of days is invalid\"\n\n### Set the start date and time increment of the files ###\ndate = datetime.date(args.year, args.month, args.day)\ndelta = datetime.timedelta(days=1)\n\n### Load the DataLoader and PhaseDetector(s), which only need to be initialized once ###\ndataloader = apply_detectors.DataLoader()\ns_detector = None\nif args.chan == \"EH\":\n p_detector = load_1c_detector()\n p_proc_func = dataloader.process_1c_P\nelse:\n p_detector, s_detector = load_3c_detectors()\n p_proc_func = dataloader.process_3c_P\n\n### Iterate over the specified number of days ###\nfor _ in range(args.n):\n ### The data files are organized Y/m/d, get the appropriate date/station files ###\n date_str = date.strftime(\"%Y/%m/%d\")\n files = glob.glob(os.path.join(data_dir, date_str, f'*{args.stat}*{args.chan}*'))\n\n ### Make the output dirs have the same structure as the data dirs ###\n date_outdir = os.path.join(outdir, date_str)\n\n ### If there are no files for that station/day, move to the next day ###\n if len(files) == 0:\n print(f'No data for {date_str}')\n continue\n\n ### There will always be a P detector, either 1c or 3c ###\n if args.chan == \"EH\":\n dataloader.load_1c_data(files[0])\n else:\n dataloader.load_3c_data(files[0], files[1], files[2])\n\n p_data, start_pad_npts, end_pad_npts = dataloader.format_continuous_for_unet(window_length,\n sliding_interval,\n p_proc_func,\n normalize=True\n )\n p_probs_outfile_name = p_detector.make_outfile_name(files[0], date_outdir)\n p_cont_post_probs = p_detector.get_continuous_post_probs(p_data, \n center_window,\n window_edge_npts,\n start_pad_npts=start_pad_npts, \n end_pad_npts=end_pad_npts)\n p_detector.save_post_probs(p_probs_outfile_name, p_cont_post_probs, dataloader.metadata)\n\n ### If 3C, run the S detector as well ###\n if s_detector is not None:\n s_data, _, _ = dataloader.format_continuous_for_unet(window_length,\n sliding_interval,\n dataloader.process_3c_S\n )\n s_probs_outfile_name = s_detector.make_outfile_name(files[0], date_outdir)\n\n s_cont_post_probs = s_detector.get_continuous_post_probs(s_data, \n center_window,\n window_edge_npts,\n start_pad_npts=start_pad_npts, \n end_pad_npts=end_pad_npts)\n s_detector.save_post_probs(s_probs_outfile_name, s_cont_post_probs, dataloader.metadata)\n\n ### Save the station meta info (including gaps) to a file in the same dir as the post probs. ###\n ### Only need one file per station/day pair ###\n meta_outfile_name = dataloader.make_outfile_name(files[0], date_outdir)\n dataloader.write_data_info(meta_outfile_name)\n\n ### Move on to the next day ###\n date += delta\n\n# print(date)\n\n# print(\"args=%s\" % args)\n\n# print(\"args.stat=%s\" % args.stat)","repo_name":"armstrong06/seis_proc_dl","sub_path":"seis_proc_dl/apply_to_continuous/run_apply_detectors.py","file_name":"run_apply_detectors.py","file_ext":"py","file_size_in_byte":6900,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"8"}
+{"seq_id":"46308456965","text":"def binary_search(arr, target):\n left_pointer = 0\n right_pointer = len(arr) - 1\n\n while left_pointer <= right_pointer:\n middle_pointer = int((left_pointer + right_pointer)/2)\n if target < arr[middle_pointer]:\n right_pointer = middle_pointer - 1\n elif target > arr[middle_pointer]:\n left_pointer = middle_pointer + 1\n else:\n return middle_pointer\n return -1\n\narr = [2, 3, 10, 200, 15, 16, 47, 29, 92, 82, 73, 210, 666, 1, 3, 4, 1, -2, -68]\n\narr.sort()\nprint(\"Input target: \")\ntarget = int(input()) # User inputs target value, assume within arr, but return error if DNE in array\n\nprint(\"Begin calculation...\")\nprint(binary_search(arr, target))","repo_name":"stevenliuxd/leetcode","sub_path":"misc/binary_search.py","file_name":"binary_search.py","file_ext":"py","file_size_in_byte":714,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"8"}
+{"seq_id":"1409739601","text":"#!/usr/bin/env python\n\nimport logging\nimport logging.handlers\nimport os\nimport sys\nimport time\n\ncwd = os.path.dirname(os.path.realpath(__file__))\npardir = os.path.abspath(os.path.join(cwd, os.pardir))\nLOG_PATH = os.path.join(pardir, \"logs\")\n\n\ndef get_logger(logger_name, log_level=\"INFO\", propagate=False):\n logger = logging.getLogger(logger_name)\n logger.setLevel(getattr(logging, log_level.upper(), 30))\n logger.propagate = propagate\n\n return logger\n\n\ndef clear_handlers(logger=None):\n if not logger:\n logger = logging.getLogger()\n\n for h in list(logger.handlers):\n logger.removeHandler(h)\n\n\ndef add_handlers(\n logger,\n log_to_console=True,\n log_to_file=True,\n log_path=LOG_PATH,\n log_file=None,\n file_log_level=\"INFO\",\n log_retention=7,\n console_log_level=\"INFO\",\n clear_first=True,\n):\n if clear_first:\n clear_handlers(logger)\n\n formatter = logging.Formatter(\n \"%(asctime)s :: %(levelname)8s :: %(threadName)s(%(thread)d) :: %(module)s(%(lineno)d) :: %(funcName)s :: %(message)s\",\n datefmt=\"%Y-%m-%d %I:%M:%S %p\",\n )\n\n # Set up file logging\n if log_to_file and log_file:\n for i in [2, 1, 0]:\n try:\n if not os.path.exists(log_path):\n os.makedirs(log_path)\n\n file_handler = logging.handlers.TimedRotatingFileHandler(\n os.path.join(log_path, log_file),\n when=\"midnight\",\n interval=1,\n backupCount=log_retention,\n )\n file_handler.setLevel(\n getattr(logging, file_log_level.upper(), 30)\n if file_log_level\n else logging.INFO\n )\n file_handler.setFormatter(formatter)\n logger.addHandler(file_handler)\n break\n except IOError as e:\n if i >= 1:\n print(\n \"Error creating file log handler! Will retry {} more time(s); sleeping 3 seconds... Error: {}\".format(\n i, e\n )\n )\n else:\n print(\n \"Could not create file log handler. There will be no logging to file. Continuing in 3 seconds... Error: {}\".format(\n e\n )\n )\n log_file = None\n log_to_file = False\n\n time.sleep(3)\n\n # Set up console logging\n if log_to_console:\n console_handler = logging.StreamHandler(sys.stdout)\n console_handler.setLevel(\n getattr(logging, console_log_level.upper(), 30)\n if console_log_level\n else logging.INFO\n )\n console_handler.setFormatter(formatter)\n logger.addHandler(console_handler)\n\n return True\n\n\ndef init_logger(\n logger_name=None,\n log_to_console=True,\n log_to_file=True,\n log_path=LOG_PATH,\n log_file=None,\n file_log_level=\"INFO\",\n log_retention=7,\n console_log_level=\"INFO\",\n clear_first=True,\n propagate=False,\n):\n log_to_console = (\n (log_to_console.lower() == \"true\")\n if isinstance(log_to_console, str)\n else log_to_console\n )\n log_to_file = (\n (log_to_file.lower() == \"true\") if isinstance(log_to_file, str) else log_to_file\n )\n propagate = (\n (propagate.lower() == \"true\") if isinstance(propagate, str) else propagate\n )\n\n logger = get_logger(\n logger_name=logger_name,\n log_level=(\n file_log_level\n if getattr(logging, file_log_level.upper(), 30)\n < getattr(logging, console_log_level.upper(), 30)\n else console_log_level\n ),\n propagate=propagate,\n )\n add_handlers(\n logger=logger,\n log_to_console=log_to_console,\n log_to_file=log_to_file,\n log_path=log_path,\n log_file=log_file,\n file_log_level=file_log_level,\n log_retention=log_retention,\n console_log_level=console_log_level,\n clear_first=clear_first,\n )\n\n if log_file and log_to_file:\n logger.info(\n \"Logging started! Log file: {}\".format(os.path.join(log_path, log_file))\n )\n else:\n logger.info(\"Logging started!\")\n\n return logger\n","repo_name":"toddrob99/redball","sub_path":"redball/logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":4420,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"8"}
+{"seq_id":"12307688271","text":"from typing import Optional, List\n\nimport mysql\nfrom pydantic import PositiveInt\n\nimport env as e\nfrom assets.database import datasource\nfrom models.clickboard import Clickboard, TempClickboard\n\n\ndef get_clickboard(ID: PositiveInt):\n ds = datasource()\n ds.connect()\n\n if ID is not None:\n ds.execute(\"\"\"\n SELECT CLICKBOARDS.*, USERDATA.NAME, USERDATA.LASTNAME FROM CLICKBOARDS JOIN USERDATA\n ON CLICKBOARDS.AUTHOR = USERDATA.ID\n WHERE CLICKBOARDS.ID = %s\n \"\"\", (ID,))\n\n data = ds.fetch_row()\n ds.close()\n\n if data is not None:\n return Clickboard(**data)\n else:\n return ValueError('No clickboard found')\n\n\ndef fetch_data(data):\n return Clickboard(\n ID=data[0],\n NAME=data[1],\n AUTHOR_ID=data[2],\n AUTHOR=str(data[9])+\" \"+str(data[10]),\n SHORT_DESCRIPTION=data[3],\n IMG_URL=data[4],\n DOK_URL=data[5],\n SCH_URL=data[6],\n BRD_URL=data[7],\n STP_URL=data[8]\n )\n\n\ndef push_data(c: Clickboard):\n ds = datasource()\n ds.connect()\n\n SQL = \"\"\"\n UPDATE CLICKBOARDS SET\n NAME = %s,\n AUTHOR = %s,\n SHORT_DESCRIPTION = %s,\n IMG_URL = %s,\n DOK_URL = %s,\n SCH_URL = %s,\n BRD_URL = %s,\n STP_URL = %s\n WHERE ID = %s\n \"\"\"\n\n PARAM = (\n c.NAME,\n c.AUTHOR,\n c.SHORT_DESCRIPTION,\n c.IMG_URL,\n c.DOK_URL,\n c.SCH_URL,\n c.BRD_URL,\n c.STP_URL\n )\n\n ds.execute(SQL, PARAM)\n ds.commit()\n ds.close()\n\n\ndef create_clickboard(c: TempClickboard):\n ds = datasource()\n ds.connect()\n\n SQL = \"\"\"\n INSERT INTO CLICKBOARDS (NAME, AUTHOR, SHORT_DESCRIPTION, IMG_URL, DOK_URL, SCH_URL, BRD_URL, STP_URL) \n VALUES (%s, %s, %s, %s, %s, %s, %s, %s)\n \"\"\"\n\n PARAM = (\n c.NAME,\n c.AUTHOR_ID,\n c.SHORT_DESCRIPTION,\n c.IMG_URL,\n c.DOK_URL,\n c.SCH_URL,\n c.BRD_URL,\n c.STP_URL\n )\n\n ds.execute(SQL, PARAM)\n ds.commit()\n ds.close()\n\n\ndef get_all_clickboards():\n ds = datasource()\n ds.connect()\n\n ds.execute(\"SELECT CLICKBOARDS.*, USERDATA.NAME, USERDATA.LASTNAME FROM CLICKBOARDS JOIN USERDATA ON CLICKBOARDS.AUTHOR = USERDATA.ID\")\n\n allClickboards = list()\n\n data = ds.fetch_all()\n ds.close()\n\n for x in data:\n allClickboards.append(fetch_data(x))\n\n return allClickboards\n","repo_name":"htltoolbox/api","sub_path":"functions/clickboard.py","file_name":"clickboard.py","file_ext":"py","file_size_in_byte":2411,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"8"}
+{"seq_id":"22257745322","text":"import json\nimport os\nimport sys\n\nsys.path.append(\"..\")\nfrom pathlib import Path\nfrom typing import Dict\n\nfrom flask import Flask, send_from_directory, request\nfrom flask_cors import CORS\nfrom service_streamer import ThreadedStreamer\nfrom werkzeug.utils import secure_filename\n\nfrom EyeDiseaseDetect.Models.ModelConstructor import ConstructModelPipe\nfrom EyeDiseaseDetect.Models.utils import change_status\nfrom EyeDiseaseDetect.Models.yolov5s import Yolov5s\nfrom EyeDiseaseDetect.Responses import code_0, internal_error, NotAllowed\nfrom EyeDiseaseDetect.utils import search_assets_structure\n\napp = Flask(__name__)\ncors = CORS(app, supports_credentials=True)\n\n# 异步预测\nRunning_Tasks = []\n\n\n# 每次添加模型都必须在这里配置！\n\n\n@app.route(\"/\")\ndef hello_world():\n return \"
Hello, World!
\"\n\n\n@app.route(\"/api/tree/list\")\ndef get_tree_data():\n if Tree is None:\n return internal_error(\"图片树未初始化\")\n return code_0(Tree)\n\n\n@app.route(\"/api/tree/update\")\ndef resync():\n global Tree\n Tree = search_assets_structure(data_path / \"assets\", data_path / \"assets\")\n return code_0(Tree)\n\n\n@app.route(\"/api/picture/\")\ndef send_report(path):\n return send_from_directory(str(data_path / \"assets\"), path)\n\n\n@app.route(\"/api/meta/\")\ndef get_meta(path):\n try:\n with (data_path / \"assets\" / path).with_suffix(\".json\").open(\"r\") as f:\n meta = json.load(f)\n return code_0(meta)\n except FileNotFoundError:\n return internal_error(f\"不存在 {path} 对应的meta文件，请考虑Update Tree List\")\n except Exception as e:\n return internal_error(e.__repr__())\n\n\n@app.route(\"/api/models/list\")\ndef model_list():\n return code_0(\n ModelInfo\n )\n\n\n@app.route(\"/api/task//\")\ndef submit(path, model):\n img_path = data_path / \"assets\" / path\n try:\n # 判断是否预测过\n with img_path.with_suffix(\".json\").open(\"r\") as f:\n meta = json.load(f)\n\n if meta[\"result\"][model][\"status\"] in [\"Waiting\", \"Predict\", \"Finish\"]:\n return NotAllowed(f\"{path} -> {model}模型已在队列中:{meta['result'][model]['status']}\")\n except KeyError:\n pass\n except Exception as e:\n return internal_error(e.__repr__())\n finally:\n del meta # 释放内存\n\n try:\n assert img_path.exists(), \"FileNotFound\"\n Running_Tasks.append(\n StreamerMap[model].submit(\n [img_path]\n )\n )\n print(f\"{path} to {model} 未被预测，添加到队列中...\")\n change_status([img_path], model, \"Waiting\")\n return code_0(None, msg=\"成功新建异步预测任务\")\n except Exception as e:\n return internal_error(e.__repr__())\n\n\n@app.route('/api/upload', methods=['GET', 'POST'])\ndef uploader():\n f = request.files['file']\n print(request.files)\n if f.filename.rsplit(\".\", 1)[-1] in [\"png\", \"jpg\"]:\n f.save(os.path.join(app.config['UPLOAD_FOLDER'], secure_filename(f.filename)))\n return code_0(\"文件成功上传\")\n else:\n return {\n \"code\": 405,\n \"data\": None,\n \"error\": f\"拓展名{f.filename.rsplit('.', 1)[-1]}似乎不是图片类型哦\",\n }\n\n\nif __name__ == '__main__':\n data_path = Path(r\"E:\\competition\\EyeDiseaseDetect\\data\")\n Tree = search_assets_structure(data_path / \"assets\", data_path / \"assets\")\n print(Tree)\n StreamerMap: Dict[str, ThreadedStreamer] = {\n \"Yolov5s\": ConstructModelPipe(Yolov5s(data_path / \"models\")),\n }\n ModelInfo: Dict[str, Dict[str, str]] = {\n \"Yolov5s\": {\"name\": \"测试模型\", \"model\": \"Yolov5s\", \"category\": \"disease\"}\n }\n app.config['UPLOAD_FOLDER'] = str(data_path / \"assets\")\n # 启动后台服务器\n app.run(port=21335)\n","repo_name":"InEase/EyeDiseaseDetect","sub_path":"EyeDiseaseDetect/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3840,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"40703376687","text":"from flask import make_response, jsonify\nfrom flask.views import MethodView\nfrom flask_login import login_required\n\n\nclass LogoutAPI(MethodView):\n \"\"\"\n Logout Resource\n \"\"\"\n\n @login_required\n def post(self):\n\n try:\n from app import db\n # insert the token\n from flask_login import current_user\n user = current_user\n db.session.add(user)\n db.session.commit()\n from flask_login import logout_user\n logout_user()\n\n responseObject = {\n 'status': 'success',\n 'message': 'Successfully logged out.'\n }\n\n return make_response(jsonify(responseObject)), 200\n except Exception as e:\n responseObject = {\n 'status': 'fail',\n 'message': e.message\n }\n return make_response(jsonify(responseObject)), 200","repo_name":"liranfar/Flask","sub_path":"app/blueprints/auth/views/logout.py","file_name":"logout.py","file_ext":"py","file_size_in_byte":929,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"23807586125","text":"import FWCore.ParameterSet.Config as cms\n\nprocess= cms.Process(\"TEST\")\n\nprocess.load('FWCore.MessageService.MessageLogger_cfi')\n\nprocess.source = cms.Source(\"PoolSource\",\n fileNames = cms.untracked.vstring('/store/relval/CMSSW_7_4_0/RelValTTbarLepton_13/MINIAODSIM/MCRUN2_74_V7_gensim_740pre7-v1/00000/603E7935-4EDD-E411-B16E-0025905A612E.root')\n)\n\nprocess.maxEvents = cms.untracked.PSet(\n input= cms.untracked.int32(100)\n)\n\nprocess.load('NtupleTools.InfoProducers.infoProducerSequence_cff')\nprocess.load('NtupleTools.Filters.DarkMatterFilter_cfi')\nprocess.dmPath = cms.Path( process.darkMatterFilter * process.infoProducerSequence )\n\nprocess.output = cms.OutputModule(\n \"PoolOutputModule\",\n SelectEvents = cms.untracked.PSet(SelectEvents = cms.vstring('dmPath')),\n fileName = cms.untracked.string('output.root'),\n)\nprocess.output_step = cms.EndPath(process.output)\n\nprocess.schedule = cms.Schedule(\n process.dmPath, \n process.output_step\n)\n\n# Spit out filter efficiency at the end.\nprocess.options = cms.untracked.PSet(wantSummary = cms.untracked.bool(True))\n","repo_name":"drkovalskyi/NtupleTools","sub_path":"InfoProducers/test/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1076,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"28653715274","text":"import os\nimport re\nimport pytest\nfrom flexmock import flexmock\nfrom typing import Dict, List, IO\nfrom algorithm_tester.concurrency_runners import Runner, Runners, BaseRunner, ConcurrentFilesRunner\nimport algorithm_tester.concurrency_runners as concurrency_runners\nfrom algorithm_tester_common.tester_dataclasses import Algorithm, AlgTesterContext, Parser\nfrom algorithm_tester.helpers import curr_time_millis\nfrom tests.test_internal.fixtures import create_dummy_context, create_dummy_algorithm, get_base_parsed_data, create_dummy_parser\nfrom algorithm_tester.plugins import Plugins\nfrom io import TextIOWrapper\n\n_runner: ConcurrentFilesRunner = ConcurrentFilesRunner()\n\ndef _get_input_files(path: str, mode: str = \"r\"):\n files_dict: Dict[str, IO] = dict()\n\n for root, _, files in os.walk(path):\n for filename in files:\n files_dict[filename] = open(f'{root}/{filename}', mode)\n\n return files_dict\n\ndef test_close_all_files(tmpdir):\n file_cnt = 10\n closed_files_cnt = 3\n\n currently_closed_files = 0\n files_dict: Dict[str, IO] = dict()\n for i in range(file_cnt):\n filename = f'file_{i}'\n files_dict[filename] = open(f'{tmpdir.strpath}/{filename}', 'w')\n\n if currently_closed_files < closed_files_cnt:\n files_dict[filename].close()\n currently_closed_files += 1\n\n _runner.close_all_files(files_dict)\n\n for file in files_dict.values():\n assert file.closed\n\n print\n\ndef test_get_base_instances():\n parser = create_dummy_parser()\n base_context: AlgTesterContext = create_dummy_context(parser=parser)\n base_context.num_of_instances = 500*2\n input_files = _get_input_files(base_context.input_dir)\n\n instance_cnt = 0\n for (instance, file) in _runner.get_base_instances(input_files, parser):\n assert instance is not None\n assert file is not None\n assert file.closed == False\n instance_cnt += 1\n\n assert instance_cnt == base_context.num_of_instances\n \n _runner.close_all_files(input_files)\n\ndef test_get_data_for_executor():\n parser = create_dummy_parser()\n algorithms = [create_dummy_algorithm(name=\"DummyAlg1\"), create_dummy_algorithm(name=\"DummyAlg2\")]\n base_context: AlgTesterContext = create_dummy_context(parser=parser)\n base_context.num_of_instances = 500*2*len(algorithms)\n input_files = _get_input_files(base_context.input_dir)\n\n instance_cnt = 0\n for (algorithm, data) in _runner.get_data_for_executor(base_context, input_files, parser, algorithms):\n assert algorithm in algorithms\n instance_cnt += 1\n\n assert instance_cnt == base_context.num_of_instances\n \n _runner.close_all_files(input_files)\n\ndef test_write_result(tmpdir):\n parser = create_dummy_parser()\n base_context: AlgTesterContext = create_dummy_context(parser=parser)\n base_context.output_dir = tmpdir.strpath\n output_files = {\n 'output_1.dat': open(f'{base_context.output_dir}/output_1.dat', \"w\")\n }\n\n flexmock(parser).should_receive(\"write_result_to_file\").times(3)\n\n _runner.write_result(base_context, parser, output_files, {\"output_filename\": \"output_1.dat\"})\n assert len(output_files.keys()) == 1\n\n _runner.write_result(base_context, parser, output_files, {\"output_filename\": \"output_2.dat\"})\n assert len(output_files.keys()) == 2\n\n _runner.write_result(base_context, parser, output_files, {\"output_filename\": \"output_1.dat\"})\n assert len(output_files.keys()) == 2\n\n _runner.close_all_files(output_files)\n print\n\n# def tests_run_tester_for_data(tmpdir):\n# parser = create_dummy_parser()\n# algorithms = [create_dummy_algorithm(name=\"DummyAlg1\"), create_dummy_algorithm(name=\"DummyAlg2\")]\n# base_context: AlgTesterContext = create_dummy_context(parser=parser)\n# base_context.num_of_instances = 500*2*len(algorithms)\n# base_context.output_dir = tmpdir.strpath\n# input_files_dict = _get_input_files(base_context.input_dir)\n# output_files_dict = dict()\n\n# (flexmock(_runner._base_runner).should_receive(\"notify_communicators\")\n# .and_return(None)\n# .times(base_context.num_of_instances + 1))\n\n# (flexmock(_runner).should_receive(\"write_result\")\n# .and_return(None)\n# .times(base_context.num_of_instances)\n# )\n\n# _runner.run_tester_for_data(base_context, algorithms, parser, list(), input_files_dict, output_files_dict)\n# print","repo_name":"budikpet/SemestralWork_Python","sub_path":"tests/test_internal/test_files_runner.py","file_name":"test_files_runner.py","file_ext":"py","file_size_in_byte":4420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"35922396650","text":"from tqdm import tqdm\nimport collections\nfrom typing import Dict, List, Optional, NamedTuple\nimport transformers.trainer_seq2seq\nfrom datasets.arrow_dataset import Dataset\nfrom transformers.trainer_utils import PredictionOutput, speed_metrics, EvalLoopOutput\nfrom datasets.metric import Metric\nfrom transformers import BeamSearchScorer, LogitsProcessorList, MinLengthLogitsProcessor\n\n# from model.utils.beam import RABeamScorer\nimport numpy as np\nimport torch\nimport time\nimport json\n\nimport numpy as np\n\ndef softmax(x, axis=-1):\n max = np.max(x,axis=axis,keepdims=True) #returns max of each row and keeps same dims\n e_x = np.exp(x - max) #subtracts each row with its max value\n sum = np.sum(e_x,axis=axis,keepdims=True) #returns sum of each row and keeps same dims\n f_x = e_x / sum \n return f_x\n\nclass EvalPrediction(NamedTuple):\n predictions: List[str]\n label_ids: np.ndarray\n metas: List[dict]\n\nclass T5SpiderTrainer(transformers.trainer_seq2seq.Seq2SeqTrainer):\n def __init__(\n self,\n metric: Metric,\n *args,\n eval_examples: Optional[Dataset] = None, \n ignore_pad_token_for_loss: bool = True,\n **kwargs,\n ) -> None:\n super().__init__(*args, **kwargs)\n self.metric = metric\n self.eval_examples = eval_examples\n self.compute_metrics = self._compute_metrics\n self.ignore_pad_token_for_loss = ignore_pad_token_for_loss\n self.beam_scorer = BeamSearchScorer(\n batch_size=1,\n num_beams=8,\n device=self.model.device,\n length_penalty=0.1\n )\n self.logits_processor = logits_processor = LogitsProcessorList(\n [\n MinLengthLogitsProcessor(16, eos_token_id=self.model.config.eos_token_id),\n ]\n )\n\n def _post_process_function(\n self, examples: Dataset, features: Dataset, predictions: np.ndarray, stage: str\n ) -> EvalPrediction:\n inputs = self.tokenizer.batch_decode([f[\"input_ids\"] for f in features], skip_special_tokens=True)\n \n label_ids = [f[\"labels\"] for f in features]\n\n max_label_length = max(len(label_id) for label_id in label_ids)\n padded_label_ids = []\n for label_id in label_ids:\n padded_label_id = label_id + [self.tokenizer.pad_token_id] * (max_label_length - len(label_id))\n padded_label_ids.append(padded_label_id)\n label_ids = np.array(padded_label_ids)\n if self.ignore_pad_token_for_loss:\n # Replace -100 in the labels as we can't decode them.\n label_ids = np.where(label_ids != -100, label_ids, self.tokenizer.pad_token_id)\n print(label_ids)\n\n decoded_label_ids = self.tokenizer.batch_decode(label_ids, skip_special_tokens=True)\n\n metas = [\n {\n \"query\": x[\"query\"],\n \"question\": x[\"question\"],\n \"context\": context,\n \"label\": label,\n \"db_id\": x[\"db_id\"],\n \"db_path\": x[\"db_path\"],\n \"db_table_names\": x[\"db_table_names\"],\n \"db_column_names\": x[\"db_column_names\"],\n \"db_foreign_keys\": x[\"db_foreign_keys\"],\n }\n for x, context, label in zip(examples, inputs, decoded_label_ids)\n ]\n\n padded_predictions = []\n prev_prediction = None\n for prediction in predictions:\n if prev_prediction == None:\n prev_prediction = prediction\n else:\n if torch.allclose(prediction, prev_prediction):\n print('same')\n prediction = prediction.cpu()\n padded_prediction = prediction.squeeze().tolist() + [self.tokenizer.pad_token_id] * (max_label_length - len(prediction))\n padded_predictions.append(padded_prediction)\n padded_predictions = np.array(padded_predictions)\n\n # predictions = predictions.argmax(axis=1)\n # predictions = softmax(predictions, axis=-1)\n # predictions = np.argmax(predictions, axis=-1)\n # print(predictions)\n # print(predictions.shape)\n\n decoded_predictions = self.tokenizer.batch_decode(padded_predictions, skip_special_tokens=True)\n \n assert len(metas) == len(decoded_predictions)\n with open(f\"{self.args.output_dir}/predictions_{stage}.json\", \"w\", encoding='utf-8') as f:\n json.dump(\n [dict(**{\"prediction\": prediction}, **meta) for prediction, meta in zip(decoded_predictions, metas)],\n f,\n indent=4,\n )\n return EvalPrediction(predictions=decoded_predictions, label_ids=label_ids, metas=metas)\n\n def _compute_metrics(self, eval_prediction: EvalPrediction) -> dict:\n predictions, label_ids, metas = eval_prediction\n # if self.target_with_db_id:\n # Remove database id from all predictions\n # predictions = [pred.split(\"|\", 1)[-1].strip() for pred in predictions]\n # TODO: using the decoded reference labels causes a crash in the spider evaluator\n # if self.ignore_pad_token_for_loss:\n # # Replace -100 in the labels as we can't decode them.\n # label_ids = np.where(label_ids != -100, label_ids, tokenizer.pad_token_id)\n # decoded_references = self.tokenizer.batch_decode(label_ids, skip_special_tokens=True)\n # references = [{**{\"query\": r}, **m} for r, m in zip(decoded_references, metas)]\n references = metas\n return self.metric.compute(predictions=predictions, references=references)\n\n\n def evaluate(\n self,\n eval_dataset: Optional[Dataset] = None,\n eval_examples: Optional[Dataset] = None,\n ignore_keys: Optional[List[str]] = None,\n metric_key_prefix: str = \"eval\",\n max_length: Optional[int] = None,\n max_time: Optional[int] = None,\n num_beams: Optional[int] = None,\n ) -> Dict[str, float]:\n self._max_length = max_length\n self._max_time = max_time\n self._num_beams = num_beams\n\n # memory metrics - must set up as early as possible\n self._memory_tracker.start()\n\n eval_dataset = self.eval_dataset if eval_dataset is None else eval_dataset\n if eval_dataset is not None and not isinstance(eval_dataset, collections.abc.Sized):\n raise ValueError(\"eval_dataset must implement __len__\")\n\n eval_dataloader = self.get_eval_dataloader(eval_dataset)\n eval_examples = self.eval_examples if eval_examples is None else eval_examples\n start_time = time.time()\n\n print(f'from seq/utils/trainer.py:- {eval_dataset}\\n {eval_examples}')\n\n # Temporarily disable metric computation, we will do it in the loop here.\n compute_metrics = self.compute_metrics\n self.compute_metrics = None\n try:\n output: EvalLoopOutput = self.evaluation_loop(\n eval_dataloader,\n description=\"Evaluation\",\n # No point gathering the predictions if there are no metrics, otherwise we defer to\n # self.args.prediction_loss_only\n prediction_loss_only=True if compute_metrics is None else None,\n ignore_keys=ignore_keys,\n metric_key_prefix=metric_key_prefix,\n ) # EvalLoopOutput(predictions=all_preds, label_ids=all_labels, metrics=metrics, num_samples=num_samples)\n\n init_input_ids = torch.ones((8, 1), device=self.model.device, dtype=torch.long)\n init_input_ids = init_input_ids * self.model.config.decoder_start_token_id\n\n predictions = []\n self.model.eval()\n for instance in tqdm(eval_dataloader):\n model_kwargs = {\n \"encoder_outputs\": self.model.get_encoder()(\n instance['input_ids'].repeat_interleave(8, dim=0).to(self.model.device), return_dict=True\n )\n }\n outputs = self.model.beam_search(\n init_input_ids.to(self.model.device), \n self.beam_scorer, \n logits_processor=self.logits_processor, \n **model_kwargs\n )\n del model_kwargs\n predictions.append(outputs[0])\n \n finally:\n self.compute_metrics = compute_metrics\n\n # We might have removed columns from the dataset so we put them back.\n if isinstance(eval_dataset, Dataset):\n eval_dataset.set_format(\n type=eval_dataset.format[\"type\"],\n columns=list(eval_dataset.features.keys()),\n )\n\n if eval_examples is not None and eval_dataset is not None and self.compute_metrics is not None:\n eval_preds = self._post_process_function(\n eval_examples,\n eval_dataset,\n predictions,\n \"eval_{}\".format(self.state.epoch),\n )\n output.metrics.update(self.compute_metrics(eval_preds))\n\n n_samples = len(eval_dataset if eval_dataset is not None else self.eval_dataset)\n output.metrics.update(speed_metrics(metric_key_prefix, start_time, n_samples))\n\n # Prefix all keys with metric_key_prefix + '_'\n for key in list(output.metrics.keys()):\n if not key.startswith(f\"{metric_key_prefix}_\"):\n output.metrics[f\"{metric_key_prefix}_{key}\"] = output.metrics.pop(key)\n\n self.log(output.metrics)\n\n self.control = self.callback_handler.on_evaluate(self.args, self.state, self.control, output.metrics)\n\n self._memory_tracker.stop_and_update_metrics(output.metrics)\n\n return output.metrics\n\n\n def _compute_metrics(self, eval_prediction: EvalPrediction) -> dict:\n predictions, label_ids, metas = eval_prediction\n # if self.target_with_db_id:\n # # Remove database id from all predictions\n # predictions = [pred.split(\"|\", 1)[-1].strip() for pred in predictions]\n # TODO: using the decoded reference labels causes a crash in the spider evaluator\n # if self.ignore_pad_token_for_loss:\n # # Replace -100 in the labels as we can't decode them.\n # label_ids = np.where(label_ids != -100, label_ids, tokenizer.pad_token_id)\n # decoded_references = self.tokenizer.batch_decode(label_ids, skip_special_tokens=True)\n # references = [{**{\"query\": r}, **m} for r, m in zip(decoded_references, metas)]\n references = metas\n return self.metric.compute(predictions=predictions, references=references)","repo_name":"AfrinDange/text2sql","sub_path":"model/seq2seq/trainer.py","file_name":"trainer.py","file_ext":"py","file_size_in_byte":10677,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"15141509118","text":"from PIL import Image\n\n\ndef load_images(image_paths):\n if isinstance(image_paths, str):\n image_paths = [image_paths]\n images_pil = []\n for image_path in image_paths:\n if isinstance(image_path, str):\n image_pil = Image.open(image_path).convert(\"RGB\")\n else:\n assert isinstance(image_path, Image)\n image_pil = image_path\n images_pil.append(image_pil)\n return images_pil\n\n\ndef image_grid(imgs, rows, cols):\n # copied from https://huggingface.co/blog/stable_diffusion\n assert len(imgs) == rows*cols\n\n w, h = imgs[0].size\n grid = Image.new('RGB', size=(cols*w, rows*h))\n grid_w, grid_h = grid.size\n \n for i, img in enumerate(imgs):\n grid.paste(img, box=(i%cols*w, i//cols*h))\n return grid\n\n\ndef find_token_indices(tokenizer, prompt, pattern, return_decoded=False):\n if pattern not in prompt:\n return [None]\n idx_in_str = prompt.index(pattern)\n tokens = tokenizer(prompt)['input_ids']\n pre_pattern_str = prompt[:idx_in_str]\n pre_pattern_tokens = tokenizer(pre_pattern_str)['input_ids']\n pattern_tokens = tokenizer(pattern)['input_ids']\n num_pattern_tokens = len(pattern_tokens) - 2\n token_indices = list(range(len(pre_pattern_tokens) - 2, len(pre_pattern_tokens) - 2 + num_pattern_tokens))\n token_indices = [i + 1 for i in token_indices]\n if return_decoded:\n token_idx_to_word = {idx: tokenizer.decode(t) for idx, t in enumerate(tokens) if 0 < idx < len(tokens) - 1}\n word = ' '.join([token_idx_to_word[i] for i in token_indices])\n return token_indices, word\n return token_indices\n\n\ndef find_token_indices_batch(tokenizer, prompts, patterns):\n # NOTE: hardcoded to return the first index for each entry in the batch\n if isinstance(prompts, str):\n prompts = [prompts]\n if isinstance(patterns, str):\n patterns = [patterns] * len(prompts)\n token_indices_batch = []\n for prompt, pattern in zip(prompts, patterns):\n token_indices = find_token_indices(tokenizer, prompt, pattern)\n token_indices_batch.append(token_indices[0])\n return token_indices_batch\n","repo_name":"magrcruz/InversionMethods","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2153,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"8"}
+{"seq_id":"10465974296","text":"from util.Matrix import Matrix\n\nmatrix_a = Matrix(\"input/a.txt\")\nmatrix_b = Matrix(\"input/b.txt\")\n\nmatrix_result = matrix_a + matrix_b\nmatrix_result2 = matrix_a * matrix_b\n\nmatrix_apb = Matrix(\"input/aplusb.txt\")\nmatrix_aob = Matrix(\"input/aorib.txt\")\n\nprint(matrix_result == matrix_apb)\nprint(matrix_result2 == matrix_aob)","repo_name":"andreimardare05/cn-2020","sub_path":"homework3/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":323,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"1673919119","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\"\"\"\n2021\n@author: amgg\n\nClase encargada de recoger los datos de la base de datos\norigen e introducirlos en formato JSON e introducirlos en\nla cola usando RabbitMQ.\n\n\"\"\"\n#Importamos las librerias\nfrom conn import*\nimport logging\nimport json\nimport pika\nfrom random import randint\nimport time\n\nclass Origen():\n \n #Constructor\n def __init__(self):\n logging.info('Empieza Origen') \n \n #Función que recoge los datos de la BD de origen\n def recogerDatos(self):\n #Creamos un objeto para la conexión \n conn=Conn()\n con=conn.Connex('localhost','chuguisapp','daw2021','chuguisapp',3306)\n \n #Creamos la consulta a la BD \n sql='SELECT id,name,mail,pass,pass2,picture FROM users'\n \n #Metemos en una tupla los datos \n info=conn.getData(sql) \n \n #Pasamos los datos a formato json\n datosJson=json.dumps(info,indent=6)\n return datosJson\n \n #Función para craer la cola con RabbitMQ\n def crearCola(self):\n #Establecemos la conexión con RabbitMQ\n con=pika.BlockingConnection(pika.ConnectionParameters(host='localhost'))\n ch=con.channel()\n \n #Declaramos la cola\n ch.queue_declare(queue='replicador')\n \n #Bucle infinito\n while True:\n #Tiempo aleatorio antes de volver a lanzar el mensaje\n time.sleep(randint(1,2))\n #Recogemos los datos json obtenidos de la BD origen\n dtJson=o.recogerDatos()\n \n #Introducimos los datos en la cola\n ch.basic_publish(exchange='', routing_key='replicador',body=dtJson)\n \n #Cerramos la conexión\n #con.close()\n \no=Origen()\no.crearCola() ","repo_name":"amggdeveloper/replicadorEscritorio","sub_path":"origen.py","file_name":"origen.py","file_ext":"py","file_size_in_byte":1826,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"73738414980","text":"import matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\n\n# Read the data for <111>{110} stacking fault energy.\nsfe_110_dft = pd.read_csv(\"../REF_DATA/sfe_110_dft.csv\",delimiter=\",\",decimal=\".\",header=0,names=[\"dis\",\"energyeV\"])\nsfe_110_dft2 = pd.read_csv(\"../REF_DATA/110_111.csv\",delimiter=\",\",decimal=\".\",header=0,names=[\"dis\",\"energyJ\"])\nsfe_110_gap21 = pd.read_csv(\"../data/sfe_110.csv\",delimiter=\" \",decimal=\".\",header=0,names=[\"dis\",\"energyeV\",\"energyJ\"])\n\n# Change energy units from eV/Å^2 to J/m^2.\n# Read the data for <112>{110} stacking fault energy.\nsfe_112_dft1 = pd.read_csv(\"../REF_DATA/dft2_112_111.csv\",delimiter=\",\",decimal=\".\",header=0,names=[\"dis\",\"energyeV\"])\nsfe_112_dft2 = pd.read_csv(\"../REF_DATA/112_111.csv\",delimiter=\",\",decimal=\".\",header=0,names=[\"dis\",\"energyJ\"])\nsfe_112_gap21 = pd.read_csv(\"../data/sfe_112.csv\",delimiter=\" \",decimal=\".\",header=0,names=[\"dis\",\"energyeV\",\"energyJ\"])\n\n# plot data\nfig, (ax, bx) = plt.subplots(nrows=1, ncols=2, figsize=(15,8))\nplt.rcParams['font.size'] = '20'\n\n# Plot for <111>{110}.\nax.grid(c='gainsboro',ls='--',lw=0.7)\nax.set_title('<111>{110}',fontsize=20)\nax.scatter(sfe_110_dft[\"dis\"]*2.834*np.sqrt(3)/2, sfe_110_dft[\"energyeV\"] * 0.001 * 16.0217733, s=60, marker='s', c='red', label='DFT')\nax.scatter(sfe_110_dft2[\"dis\"]*2.834*np.sqrt(3)/2, sfe_110_dft2[\"energyJ\"], s=60, marker='v', c='blue', label='DFT2')\nax.scatter(sfe_110_gap21[\"dis\"], sfe_110_gap21[\"energyJ\"], s=60, marker='o', c='orange', label='ML-IAP')\nax.plot(sfe_110_gap21[\"dis\"], sfe_110_gap21[\"energyJ\"], c='orange',lw=2,ls='--')\n\nax.set_xlim([0,2.45])\nax.set_xlabel('Displacements, [$\\AA$]', fontsize=20)\nax.set_ylabel('Stacking Fault Energy, [J/m$^2$])', fontsize=20)\nax.legend(loc='upper right',fontsize=20)\nax.text(0.6, 0.2, 'IAP='+str(np.round(max(sfe_110_gap21[\"energyJ\"]),4)), transform=ax.transAxes, fontsize='large', horizontalalignment='right',\n verticalalignment='bottom')\n\n# Plot for <111>{112}\nbx.grid(c='gainsboro',ls='--',lw=0.7)\nbx.set_title('<111>{112}',fontsize=20)\nbx.scatter(sfe_112_dft1[\"dis\"]*2.834*np.sqrt(3)/2, sfe_112_dft1[\"energyeV\"]* 0.001 * 16.0217733, s=60, marker='s', c='red', label='DFT1')\nbx.scatter(sfe_112_dft2[\"dis\"]*2.834*np.sqrt(3)/2, sfe_112_dft2[\"energyJ\"], s=60, marker='v', c='blue', label='DFT2')\nbx.scatter(sfe_112_gap21[\"dis\"], sfe_112_gap21[\"energyJ\"], s=60, marker='o', c='orange', label='ML-IAP')\nbx.plot(sfe_112_gap21[\"dis\"], sfe_112_gap21[\"energyJ\"],c='orange',lw=1.5,ls='--')\nbx.text(0.6, 0.2, 'IAP='+str(np.round(max(sfe_112_gap21[\"energyJ\"]),4)), transform=bx.transAxes, fontsize='large', horizontalalignment='right',\n verticalalignment='bottom')\n\nbx.set_xlim([0,2.45])\nbx.set_xlabel('Displacements, [$\\AA$]', fontsize=20)\nbx.set_ylabel('Stacking Fault Energy, [J/m$^2$])', fontsize=20)\nbx.legend(loc='upper right',fontsize=20)\n\nf = open('../data/results.txt','a')\nf.write('=========================================\\n')\nf.write('Max (110) SF energy\\n')\nf.write(str(max(sfe_110_gap21[\"energyJ\"]))+'\\n')\nf.write('Max (112) SF energy\\n')\nf.write(str(max(sfe_112_gap21[\"energyJ\"]))+'\\n')\n#plt.show()\nfig.savefig('sfe.png',dpi=300)\n","repo_name":"leiapple/Potential_benchmark_iron","sub_path":"pot_testing/py_pps/sfe.py","file_name":"sfe.py","file_ext":"py","file_size_in_byte":3152,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"44508836205","text":"#!/usr/bin/env python3\n# *-* coding:utf-8 *-*\n\n\"\"\"\n\n:mod:`lab_rest_client` -- interacting with a RESTful server\n=========================================\n\nLAB_REST_CLIENT Learning Objective: Learn to interact with RESTful APIs using requests library\n::\n\n a. Using requests, HTTP GET the /get_token path from the url given to you by the instructor. Store\n the token for use in the next steps.\n\n Note: For all the rest of the steps the server requires that you send a header with a key of\n \"X-Auth-Token\" and the value set to the token received in this step.\n\n b. Create a new \"car\" object using a POST to /cars path. The expected data items for the \"car\"\n object can be seen by viewing the /formats/car path. Note: you have to interact with the\n server via JSON, and you have to have values for each of the data items in the format.\n\n c. Get the list of cars from the server. Verify that the id for the car you created is in that\n list.\n\n d. Change the Model of the car you created using the PUT HTTP verb to the proper url /cars/\n\n e. Verify the change was registered by reading the information for the car id from /cars/\n\n f. Delete the car you created by sending the http DELETE verb to /cars/\n\n g. (Optional) Explore the other collections on the server. Create some appliances or pantry items,\n change them, delete them, and generally mess around with the objects. Check out /formats for\n the expected data format items.\n\n\"\"\"\n\nimport requests\nimport sys\n\n# Note: if you need to debug your HTTP connection info, call the following\n# function before you do any http calls with requests:\n\n\ndef debug_mode():\n import logging\n from http.client import HTTPConnection\n HTTPConnection.debuglevel = 1\n\n logging.basicConfig()\n logging.getLogger().setLevel(logging.DEBUG)\n requests_log = logging.getLogger(\"requests.packages.urllib3\")\n requests_log.setLevel(logging.DEBUG)\n requests_log.propagate = True\n\n\nif __name__ == '__main__':\n host = sys.argv[1]\n print('part a.')\n token_url = '{}/{}'.format(host, 'get_token')\n token_response = requests.get(token_url).json()\n my_token = token_response['X-Auth-Token']\n headers = {'X-Auth-Token': my_token}\n print('my token: {}'.format(my_token))\n\n print('part b.')\n cars_url = '/'.join([host, 'cars'])\n new_car = {'Make': 'Honda', 'Model': 'Fit', 'Color': 'Blue', 'Name': \"Dad's Car\",\n 'Year': '2012', 'PrimaryDriver': 'Jeremy'}\n car_response = requests.post(cars_url, json=new_car, headers=headers).json()\n my_car_id = car_response['car']\n print('new car id: {}'.format(my_car_id))\n\n print('part c.')\n cars_response = requests.get(cars_url, headers=headers).json()\n cars_list = cars_response['cars']\n if not my_car_id in cars_list:\n print('Error: {} not in {}'.format(my_car_id, cars_list))\n else:\n print('{} is in {} as expected'.format(my_car_id, cars_list))\n\n print('part d.')\n my_car_url = '/'.join([host, 'cars', my_car_id])\n car_change = {'Model': 'Civic'}\n change_response = requests.put(my_car_url, json=car_change, headers=headers).json()\n new_car_info = change_response['car']\n print('Changed car info: {}'.format(new_car_info))\n\n print('part e.')\n car_info = requests.get(my_car_url, headers=headers).json()\n if car_info['Model'] != 'Civic':\n print('Error: change did not work: {}'.format(car_info))\n else:\n print('Success! Car changed properly: {}'.format(car_info))\n\n print('part f.')\n del_response = requests.delete(my_car_url, headers=headers).json()\n print(del_response)\n","repo_name":"jeremyprice/RU_Python_IV","sub_path":"solutions/lab_rest_client.py","file_name":"lab_rest_client.py","file_ext":"py","file_size_in_byte":3623,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"8"}
+{"seq_id":"39966989188","text":"\n\n\nimport PyQt4\nfrom PyQt4.Qt import QLabel, QTextEdit, QWidget, QPixmap, QBoxLayout, \\\n QHBoxLayout\nfrom PyQt4.QtGui import QDialog, QPushButton, QGridLayout, QApplication, QTextBlock, QTextFormat\nimport commands\nimport os\nfrom os.path import join\nimport pip\nimport pkg_resources\nimport subprocess\n\nimport netifaces as ni\n\n\nclass ServiceControl(QDialog):\n SSHcnt =0\n HTTPcnt =0\n FTPcnt =0\n ip ='' \n\n \n def __init__(self, parent=None): \n \n \n self.ip=''\n \n super(ServiceControl, self).__init__(parent)\n try:\n self.ip = ni.ifaddresses('eth0')[2][0]['addr']\n \n \n except:\n self.ip = 'No Network '\n \n \n\n# servis etiketleri........................... \n SSHLabel = QLabel('
![](\"{}base/images/{}\")
\n \"\"\".format(static_url, image_name)\n\n@register.simple_tag\ndef include_leaflet(version=\"0.4.4\", cdn=False):\n static_url = settings.STATIC_URL+\"base/\"\n return \"\"\"\\\n \n \n \n \n \n \"\"\".format(\"http://cdn.leafletjs.com/\" if cdn else static_url, version)\n","repo_name":"flipsideofnullity/djenerator","sub_path":"base/templatetags/include_tags.py","file_name":"include_tags.py","file_ext":"py","file_size_in_byte":1390,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"91"}
+{"seq_id":"8035530023","text":"# https://wikidocs.net/7014\n\n# 111\nhi = input(\"입력: \")\nprint(hi*2)\n\n# 112\nnum = int(input('숫자를 입력하세요:'))\nprint(num+10)\n\n# 113\nnum = int(input('숫자를 입력하세요:'))\nif num % 2 == 0:\n print('짝수')\nelse:\n print('홀수')\n\n# 114\nnum = int(input('숫자를 입력하세요:'))\nnum = num + 20\nif num < 255:\n print(num)\nelse:\n print(255)\n\n# 115\n# 사용자로부터 하나의 값을 입력받은 후 해당 값에 20을 뺀 값을 출력하라. 단 출력 값의 범위는 0~255이다.\n# 예를 들어 결괏값이 0보다 작은 값이되는 경우 0을 출력하고 255보다 큰 값이 되는 경우 255를 출력해야 한다.\nnum = int(input('숫자를 입력하세요:'))\nnum = num - 20\nif num < 0:\n print(0)\nelif num > 255:\n print(255)\nelse:\n print(num)\n\n# 116\ntime = input(\"현재시간: \")\nif time[-2:] == \"00\":\n print(\"정각 입니다.\")\nelse:\n print(\"정각이 아닙니다.\")\n\n# 117\nfruit = [\"사과\", \"포도\", \"홍시\"]\nanswer = input('좋아하는 과일은?')\n\nif answer in fruit:\n print('정답입니다.')\nelse:\n print('오답입니다')\n\n# 118\nwarn_investment_list = [\"Microsoft\", \"Google\", \"Naver\", \"Kakao\", \"SAMSUNG\", \"LG\"]\nuser_investment = input('투자 종목을 입력해주세요: ')\nif user_investment in warn_investment_list:\n print('투자 경고 종목입니다.')\nelse:\n print('투자 경고 종목이 아닙니다.')\n\n# 119\nfruit = {\"봄\" : \"딸기\", \"여름\" : \"토마토\", \"가을\" : \"사과\"}\nuser_favorite_weather = input('제가 좋아하는 계절은: ')\nif user_favorite_weather in fruit.keys():\n print('정답입니다.')\nelse:\n print('오답입니다.')\n\n# 120\nfruit = {\"봄\" : \"딸기\", \"여름\" : \"토마토\", \"가을\" : \"사과\"}\nuser_favorite_weather = input('제가 좋아하는 계절은: ')\nif user_favorite_weather in fruit.values():\n print('정답입니다.')\nelse:\n print('오답입니다.')\n","repo_name":"bbster/PracticeAlgorithm","sub_path":"example/wikidocs_exam_111_120.py","file_name":"wikidocs_exam_111_120.py","file_ext":"py","file_size_in_byte":1919,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"16071905186","text":"import streamlit as st\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\n\nimport sys\nsys.path.insert(1, '../notebooks')\nimport preprocessing as pp\nimport letter_detection_utils as ld_util\nimport ressources as rss\nimport modele as mdl\n\nfrom tabs import st_lib\n\n\nmodels = [['tj_ctc_base_10epochs_LRe-4', 'Original dataset, 10 epochs, LR 1e-4'],\n ['tj_ctc_base_20epochs_LRe-4', 'Original dataset, 20 epochs, LR 1e-4'],\n ['tj_ctc_base_20epochs_LR-plateau', 'Original dataset, 20 epochs, LR on Plateau'],\n ['tj_ctc_augmented_20epochs_LR-plateau', 'Augmented dataset, 20 epochs, LR on Plateau']]\n\ndata_extract_words = '../images/data_extract/words'\n\nCANVAS_DEFAULT_FILENAME = \"tmp/canvas_file.png\"\n\nmodels_loaded={}\n\ndef show_data_extract():\n \n st.write(\"See below the predictions with 4 different models for a selection of images.\")\n if st.button(label='Load new predictions') == True:\n images = load_images_from_path(data_extract_words, 12)\n for model, title in models: \n desc = f\"\"\"Predictions using a model with : **{title}**\"\"\"\n st.write(desc)\n texts = get_predictions(model, images)\n show_predictions_images(images, texts)\n \n st.text(\"\") # saut de ligne\n st.text(\"\") # saut de ligne\n\n\n \n \ndef show_images_batch():\n #liste les fichiers\n images = load_images_from_path(data_extract_words, 12)\n \n for model, title in models: \n desc = f\"\"\"Predictions using a model with : **{title}**\"\"\"\n st.write(desc)\n texts = get_predictions(model, images)\n show_predictions_images(images, texts)\n \n st.text(\"\") # saut de ligne\n st.text(\"\") # saut de ligne\n \n\n \n \n \ndef show_local():\n st.write(\"Browse your local files to get an image prediction\")\n\n\n\ndef remove_white_space(image):\n # Convertir l'image en niveaux de gris\n # image_gray = tf.image.rgb_to_grayscale(image)\n image_gray = image\n \n\n # Inverser les couleurs de l'image\n image_inv = tf.math.subtract(255, image_gray)\n\n # Appliquer un filtre de seuillage\n threshold_value = 200\n image_thresh = tf.where(image_inv > threshold_value, 255, 0)\n\n # Recadrer l'image autour du texte en supprimant les zones blanches\n bbox = tf.image.crop_to_bounding_box(image_thresh, 0, 0, tf.shape(image_thresh)[0], tf.shape(image_thresh)[1])\n bbox = tf.cast(bbox, tf.uint8)\n \n # Inverser les couleurs de l'image\n bbox_inv = tf.math.subtract(255, bbox)\n\n return bbox_inv\n\n\n@tf.function \ndef is_empty_image(image):\n # Convertir l'image en niveaux de gris\n # image_gray = tf.image.rgb_to_grayscale(image)\n\n # Somme de tous les pixels de l'image\n sum_of_pixels = tf.reduce_sum(image)\n\n # Vérifier si l'image contient quelque chose ou non\n if sum_of_pixels > 0:\n return False\n else:\n return True\n\n@tf.function \ndef on_image_uploaded(filename):\n \n image = ld_util.load_image(filename) \n if is_empty_image(image): \n return\n \n # image = ld_util.crop_image(image.numpy()) \n # image = remove_white_space(image) \n \n image = ld_util.preprocess(image, img_size=rss.img_size, data_augmentation=False, is_threshold=False)\n image = tf.expand_dims([image], -1)\n image = tf.squeeze(image, [3])\n \n for i in range(len(models)): \n text = get_predictions(models[i][0], image) \n st.write(\"Utilisation du modele : \", models[i][1])\n st.write(\"Vous avez écrit le texte : \", text[0]) \n st.write(\"\")\n st.write(\"\")\n \n \n \ndef show_drawing(): \n st.write(\"Use your mouse to write and get a prediction by clicking the \\\"Send to Streamlit\\\" button\") \n \n st_lib.render_canvas(\n on_image_uploaded, \n filename = CANVAS_DEFAULT_FILENAME\n )\n\n\n\ndef get_predictions(model, images):\n loaded_model = load_model(\"../pickle/\"+model, {\"CTCLoss\": mdl.CTCLoss}) # tf.keras.models.load_model(\"../pickle/\"+model, custom_objects={\"CTCLoss\": mdl.CTCLoss})\n \n text_probs = loaded_model.predict(images) \n text = ld_util.greedy_decoder(text_probs, rss.charList)\n \n return text\n\n \ndef load_model(path, cust_obj):\n if path in models_loaded.keys():\n return models_loaded[path]\n else:\n model = tf.keras.models.load_model(path, custom_objects=cust_obj)\n models_loaded[path] = model\n return models_loaded[path]\n\ndef load_images_from_path(path, max_files=-1):\n \n #liste les fichiers\n images_path = pp.get_files(path, ext='png', sub=False)\n \n if max_files > 0:\n shuffled_word_imgs = tf.random.shuffle(images_path)\n images_path = shuffled_word_imgs[:max_files]\n \n word_imgs = tf.zeros([1, 32, 128, 1])\n \n for image_path in images_path:\n image = ld_util.load_image(image_path) \n image = ld_util.preprocess(image, img_size=rss.img_size, data_augmentation=False, is_threshold=True)\n image = tf.expand_dims([image], -1)\n image = tf.squeeze(image, [3])\n word_imgs = tf.concat([word_imgs, image], 0)\n \n \n\n # Removing extra img due to initialization\n word_imgs = word_imgs[1:]\n \n # if max_files > 0:\n # shuffled_word_imgs = tf.random.shuffle(word_imgs)\n # word_imgs = shuffled_word_imgs[:max_files]\n \n \n return word_imgs\n\n\ndef show_predictions_images(images, textes):\n fig = plt.figure(figsize=(20, 12))\n\n lin = 5\n col = 6\n \n # Parcourir les images et les étiquettes\n for i in range(len(textes)):\n img = images[i]\n label = textes[i]\n\n # Ajouter la sous-figure à la figure principale\n fig.add_subplot(lin, col, i+1)\n plt.imshow(img, cmap='gray')\n plt.title(label, fontsize=20)\n plt.axis('off')\n\n # Afficher la figure dans Streamlit\n st.pyplot(fig)","repo_name":"Montikore/OCRPyMan","sub_path":"streamlit_app/tabs/demo_text_reco_ressources.py","file_name":"demo_text_reco_ressources.py","file_ext":"py","file_size_in_byte":5951,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"15045503240","text":"from typing import Any, Tuple\r\n\r\nfrom django.db import connection\r\nfrom django.shortcuts import redirect, render\r\nfrom django.contrib import messages\r\n\r\nfrom DNDStocks.views import dictfetchall\r\n\r\nfrom .models import Party, Inventory, TradeHistory, TravelHistory\r\nfrom locations.models import Location, LocationResource, Resource\r\n\r\nfrom random import seed, random\r\n\r\n# Create your views here.\r\ndef party_page(request):\r\n return render(request, 'party.html')\r\n\r\ndef travel_page(request):\r\n party: Party = Party.objects.get(id=1)\r\n current_location = party.location\r\n latest_journey = party.latest_journey()\r\n latest_trade = party.latest_trade()\r\n all_locations = Location.objects.exclude(id=current_location.id).order_by('name')\r\n all_resources = Resource.objects.all()\r\n\r\n # Add party resources to inventory table if needed\r\n for r in all_resources:\r\n try:\r\n party.inventory.get(resource_id=r.id)\r\n except:\r\n party.inventory.add(r)\r\n\r\n # Trading calc and party inventory\r\n local_resources, inventory, local_specialities = get_stocks(party)\r\n\r\n context = {\r\n 'title': 'Travel Page',\r\n 'party': party,\r\n 'inventory': inventory,\r\n 'current_location': current_location,\r\n 'all_locations': all_locations,\r\n 'local_resources': local_resources,\r\n 'local_specialities': local_specialities,\r\n 'latest_trade': latest_trade,\r\n }\r\n\r\n return render(request, 'travel.html', context)\r\n\r\ndef get_stocks(party: Party) -> Tuple[list[Any], list[Any], list[int]]:\r\n \"\"\" Using the party journey count as the random seed,\r\n Return the local resources, party inventory, and local specialities in a tuple\r\n applying any necessary randomization.\r\n\r\n local_resources: resource id, resource name, buying price, selling price\r\n inventory: resource id, resource name, # resource in party inventory, resource base value\r\n local_specialities: resource id list for all special deals\"\"\"\r\n seed(party.journey_count)\r\n location_resources = LocationResource.objects.filter(location_id=party.location.id)\r\n all_resources: list[Resource] = Resource.objects.all()\r\n local_resources = []\r\n inventory = []\r\n local_specialities = []\r\n for r in all_resources:\r\n lr: LocationResource\r\n try:\r\n lr = location_resources.get(resource_id=r.id)\r\n except:\r\n lr = None\r\n r_set = party.get_resource(r) # get the entry in party personal resources for resource\r\n inventory.append({\r\n 'id': r.id,\r\n 'name': r.name,\r\n 'quantity': r_set.quantity,\r\n 'base_value': r.base_value\r\n })\r\n if lr is not None:\r\n local_specialities.append(r.id)\r\n tender_probability = lr.probability or 0.95\r\n else:\r\n tender_probability = 0.95\r\n\r\n if random() < tender_probability:\r\n price = get_price(r, lr)\r\n local_resources.append({\r\n 'id': r.id,\r\n 'name': r.name,\r\n 'buy_price': price * 1.15,\r\n 'sell_price': price * 0.9,\r\n })\r\n\r\n return local_resources, inventory, local_specialities\r\n\r\ndef get_price(r:Resource, lr:LocationResource) -> float:\r\n adjustment = random()\r\n try:\r\n return float((lr.base_value or r.base_value) * ( 1 + (lr.variance or r.variance) * (adjustment * 2 - 1) ))\r\n except:\r\n return float(r.base_value * ( 1 + r.variance * (adjustment * 2 - 1) ))\r\n\r\ndef new_travel(request):\r\n new_location_id = request.POST.get('change_location_select')\r\n if new_location_id is not None:\r\n party: Party = Party.objects.get(id=1)\r\n new_location = Location.objects.get(id=new_location_id)\r\n request = party.travel_to(request, new_location)\r\n return redirect('/party/travel/', request)\r\n\r\ndef undo_travel(request):\r\n \"\"\" Undo the last journey, returning the party to the previous location\r\n and reverting all of their trades between the last journey and now. \"\"\"\r\n party = Party.objects.get(id=1)\r\n request = party.revert_travel(request)\r\n return redirect('/party/travel/', request)\r\n\r\ndef trade_deal(request):\r\n resource_id = request.POST.get('resource_select') and int(request.POST.get('resource_select'))\r\n resource = Resource.objects.get(id=resource_id)\r\n b_amt = (request.POST.get('resource_buy_amount') and int(request.POST.get('resource_buy_amount'))) or 0\r\n s_amt = (request.POST.get('resource_sell_amount') and int(request.POST.get('resource_sell_amount'))) or 0\r\n buy_amt: float\r\n\r\n if resource and (b_amt or s_amt):\r\n buy_amt = b_amt - s_amt\r\n if buy_amt == 0:\r\n return redirect('/party/travel/', request)\r\n party: Party = Party.objects.get(id=1)\r\n local_resources, inventory, local_specialities = get_stocks(party)\r\n\r\n request = party.trade(request, resource, buy_amt, local_resources)\r\n else:\r\n messages.error(request, 'Resource or buy and sell amounts were missing.')\r\n\r\n return redirect('/party/travel/', request)\r\n\r\ndef undo_trade(request):\r\n \"\"\" Revert the party gold and inventory by refunding or restoring the last traded quantities. \"\"\"\r\n party: Party = Party.objects.get(id=1)\r\n request = party.revert_trade(request)\r\n try:\r\n return redirect(request.META.get('HTTP_REFERER').split(request.META.get('HTTP_HOST'))[1], request)\r\n except:\r\n return redirect('/party/travel/', request)\r\n\r\ndef history_page(request):\r\n party = Party.objects.first()\r\n latest_trade = party.latest_trade()\r\n history: dict[int, object] = {}\r\n with connection.cursor() as cursor:\r\n # get the travel history\r\n cursor.execute(f\"\"\"\r\n SELECT\r\n travel.id AS travel_id,\r\n loc.name AS location,\r\n res.name AS resource,\r\n trade.quantity_gained AS quantity,\r\n trade.gold_gained AS gold\r\n FROM locations_location AS loc\r\n LEFT join party_travelhistory AS travel ON loc.id=travel.location_id\r\n LEFT join party_tradehistory AS trade ON travel.id=trade.location_hist_id\r\n LEFT join locations_resource AS res ON trade.resource_id=res.id\r\n WHERE travel.party_id={party.id}\r\n ORDER BY travel.id DESC, trade.id DESC\r\n \"\"\")\r\n history = dictfetchall(cursor)\r\n context = {\r\n 'title': 'Party History',\r\n 'history': history,\r\n 'latest_trade': latest_trade,\r\n }\r\n return render(request, 'history.html', context)\r\n","repo_name":"LaytonGB/D-D-Stocks-System","sub_path":"DNDStocks/party/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6657,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"91"}
+{"seq_id":"1333026654","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# ### TASK1: Import panda libary\n# \n\n# In[106]:\n\n\nimport pandas as pd\nimport os\nimport matplotlib.pyplot as plt\nfrom itertools import combinations\nfrom collections import Counter\n\n\n# ### Task2: Collate monthly sales\n# \n\n# In[10]:\n\n\nfiles=[file for file in os.listdir('./Sales_Data')]\nallMonthsData= pd.DataFrame()\n\nfor file in files:\n df = pd.read_csv(\"./Sales_Data/\"+ file)\n allMonthsData=pd.concat([allMonthsData,df])\nallMonthsData.to_csv(\"collated_data.csv\", index=False) \n \n\n\n# In[12]:\n\n\ncollated_data=pd.read_csv(\"collated_data.csv\")\ncollated_data.head()\n\n\n# ### Task3: Cleaning the data\n\n# #### Drop NAN rows and Remove Or\n\n# In[61]:\n\n\nnan_df=collated_data[collated_data.isna().any(axis=1)]\nnan_df.head()\n\ncollated_data=collated_data.dropna(how='all')\ncollated_data.head(5)\n\ncollated_data=collated_data[collated_data['Order Date'].str[0:2]!='Or']\ncollated_data.head(20)\n\n\n# ### Add Months column\n# \n# \n\n# In[39]:\n\n\ncollated_data['Month']=collated_data['Order Date'].str[0:2]\n\ncollated_data['Month']=collated_data['Month'].astype('int32')\n#collated_data['Month']=pd.to_numeric(collated_data['Order Date'])#converted to int\n\n\n# # To find the Best sales month and amount\n# \n\n# In[69]:\n\n\ncollated_data['Quantity Ordered']=pd.to_numeric(collated_data['Quantity Ordered'])#make as int\ncollated_data['Price Each']=pd.to_numeric(collated_data['Price Each'])#make as float\n\ncollated_data['Sales']=collated_data['Quantity Ordered']*collated_data['Price Each']#sales per day in a month\n#collated_data.head(25)\nresults=collated_data.groupby('Month').sum()\nresults\n\n\n# ### Plot montly sales using plotlib\n\n# In[62]:\n\n\nmonths=range(1,13)\nplt.bar(months,results['Sales'])\n\nplt.xticks(months)\nplt.ylabel('Sales in $')\nplt.xlabel('Month in number')\nplt.show()\n\n\n# # To find the city with the maximum sales\n\n# ### Add City column and state to remove duplicacy\n\n# In[66]:\n\n\ndef get_city(address):\n return address.split(',')[1]\ndef get_state(address):\n return address.split(',')[2].split(' ')[1]\n\n\ncollated_data['City']=collated_data['Purchase Address'].apply(lambda x:f\"{get_city(x)},({get_state(x)})\")\ncollated_data.head(20)\n\n\n# #### To find city with highest number of sales and plot\n\n# In[76]:\n\n\nresults=collated_data.groupby('City').sum()\nresults\n#plotting the graph\n\ncities=[city for city, df in collated_data.groupby('City')]\n\nplt.bar(cities,results['Sales'])\nplt.xticks(cities, rotation='vertical', size=10)\nplt.ylabel('Sales in $')\nplt.xlabel('City Name')\nplt.show()\n\n\n# ### Best time of the day to advertise\n# \n\n# #### Seperating the hour of the day from the order time and date and putting a count\n\n# In[81]:\n\n\ncollated_data['Order Date']=pd.to_datetime(collated_data['Order Date'])\ncollated_data['Hour']=collated_data['Order Date'].dt.hour\ncollated_data['CountHour']=1\ncollated_data.head()\n\n\n# #### Plotting a graph to find the peak hours\n\n# In[91]:\n\n\n#Plotting graph to find the best time of the day for advertisement\n\nhours=[hour for hour, df in collated_data.groupby('Hour')]\n\nplt.plot(hours, collated_data.groupby(['Hour']).count())\nplt.grid()\nplt.xticks(hours)\nplt.ylabel('Count of Order')\nplt.xlabel('Hour of the Day')\nplt.show()\n\n#Peak at 11am and 7pm\n\n\n# # To find the products sold together most often\n\n# In[103]:\n\n\n#find the duplicate order Id to know which orders were made together\n\ndf=collated_data[collated_data['Order ID'].duplicated(keep=False)]\n#df.head(50)\ndf['Grouped']=df.groupby('Order ID')['Product'].transform(lambda x: ','.join(x))\n\ndf=df[['Order ID','Grouped']].drop_duplicates()\ndf.head(20)\n\n\n# In[107]:\n\n\n#counting the total number of products ordered together\n\ncount=Counter()\n\nfor row in df['Grouped']:\n row_list=row.split(',')\n count.update(Counter(combinations(row_list,2)))\n \nfor key, value in count.most_common(10):\n print(key, value)\n\n\n# # What product sold the most and why?\n\n# In[121]:\n\n\nproduct_group=collated_data.groupby('Product')\nquantity_ordered=product_group.sum()['Quantity Ordered']\n\n\n#plotting the graph\nproducts=[product for product, df in product_group]\nplt.bar(products, quantity_ordered)\nplt.xticks(products, rotation='vertical', size=10)\nplt.ylabel('Quantity Ordered')\nplt.xlabel('Products')\nplt.show()\n\n\n# # Overlaying the graphs to get better insights\n\n# In[123]:\n\n\n#mean of prices of each product sold\n\nprices=collated_data.groupby('Product').mean()['Price Each']\nprint (prices)\n\n\n# In[138]:\n\n\n#graph to plot pricevs product vs quantity ordered\n\nfig, ax1 = plt.subplots()\n\nax2 = ax1.twinx()\nax1.bar(products, quantity_ordered, color='g')\nax2.plot(products,prices,)\n\nax1.set_xlabel('Product Name')\nax1.set_ylabel('Quantity Ordered', color='g')\nax2.set_ylabel('Price($)', color='b')\nax1.set_xticklabels(products,rotation='vertical',size=10)\nplt.show()\n\n\n\n\n\n\n","repo_name":"02Debaditya-Mukhopadhyay/Sales-Analysis","sub_path":"Analysis.py","file_name":"Analysis.py","file_ext":"py","file_size_in_byte":4777,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"13780514604","text":"# mortgage.py\n#\n# Exercise 1.7\nprincipal = 500000.0\nrate = 0.05\npayment = 2684.11\ntotal_paid = 0.0\nextra_payment_start_month = int(input('Extra Payment Start Month?'))\nextra_payment_end_month = int(input('Extra Payment End Month?'))\nextra_payment = int(input('Extra Payment Amount Per Month?')) # per month\nmonths_total = 0\n\nwhile principal > 0:\n while months_total >= extra_payment_start_month and months_total <= extra_payment_end_month:\n principal = principal * (1+rate/12) - payment - extra_payment\n #principal = principal - extra_payment\n total_paid = total_paid + payment + extra_payment\n months_total += 1\n #print((months_total-1), total_paid, principal)\n print(f'Month={months_total-1} Total Paid: {round(total_paid,2)} Principal Remaining: {round(principal,2)}')\n principal = principal * (1+rate/12) - payment\n total_paid = total_paid + payment\n months_total += 1\n if principal < 0:\n principal -= principal\n #print((months_total-1), total_paid, principal)\n #print(f'{months_total-1}, {total_paid}, {principal}')\n print(f'Month={months_total-1} Total Paid: {round(total_paid,2)} Principal Remaining: {round(principal,2)}')\n\n#print('Total paid', total_paid)\n#print('Total Number of Months', months_total)\n\n","repo_name":"searnhardt/practical-python","sub_path":"Work/mortgage.py","file_name":"mortgage.py","file_ext":"py","file_size_in_byte":1292,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"91"}
+{"seq_id":"8999147289","text":"import sys\ninput = sys.stdin.readline\n\nN, M = map(int, input().split())\nnums = list(map(int, input().split()))\n\n\ndef devide(v):\n max_v = nums[0]\n min_v = nums[0]\n cnt = 1\n\n for i in range(1, N):\n max_v = max(max_v, nums[i])\n min_v = min(min_v, nums[i])\n\n if max_v - min_v > v:\n cnt += 1\n max_v = nums[i]\n min_v = nums[i]\n return cnt\n\n\nsp, ep = 0, max(nums)\nres = 0\n\nwhile sp <= ep:\n md = (sp + ep) // 2\n\n if devide(md) <= M:\n ep = md - 1\n res = md\n else:\n sp = md + 1\n\nprint(res)\n","repo_name":"Kim-Jaemin420/Algorithm-Training","sub_path":"백준알고리즘/이분 탐색/python/⭐️구간 나누기2.py","file_name":"⭐️구간 나누기2.py","file_ext":"py","file_size_in_byte":579,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"}
+{"seq_id":"29537143956","text":"import faceDetect\nimport panTiltControl as ptc\nfrom time import sleep\nimport cv2\nfrom imutils.video import VideoStream\n\n## Start video stream ##\nvs = VideoStream(usePiCamera=True).start()\nsleep(2) #camera warmup time\n\n##Define pan/tilt ranges and initial values\npanRange = [-45,45]\npanInit = 0 #Initial value. Servo will be moved there in a later line\ntiltRange = [80,140]\ntiltInit = 110 #Stepper defaults to 0 upon being turned on\n\n##Initialise variable to track motor status\nmotorStatus = 0\nmotorWarmup = 0\n\n##Define haarcascade file path\nhaarPath = cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'\n\n##Capture a test frame to establish frame size\nframe = vs.read()\nframeSize = frame.shape[:2]\ncentreX = frameSize[1]//2\ncentreY = frameSize[0]//2\n\n#Initialise PID controller\npidPan = ptc.PID(kP=0.1, kD = 0.1)\npidTilt = ptc.PID(kP=0.1)\npidPan.initialise()\npidTilt.initialise()\n\n#Initialise launcher object\nlauncher = ptc.Launcher()\nlauncher.connect()\nlauncher.pan(panInit)\npanAngle = panInit\nlauncher.tilt(tiltInit)\ntiltAngle = tiltInit\nlauncher.motor(motorStatus)\n\n##Infinite loop while tracking\nwhile True:\n #get a frame\n frame = vs.read()\n #convert frame to grayscale for use by haar cascade\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n #check frame for faces\n [faceX, faceY, faceData] = faceDetect.findFace(frame, haarPath)\n \n \n #display current frame\n # Draw a rectangle around the faces\n if faceData is not None:\n print(panAngle,tiltAngle)\n (x, y, w, h) = faceData\n cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255), 2)\n cv2.putText(frame, \"INTRUDER DETECTED!\", (10,30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0,255,0),2)\n \n #calculate difference between frame centre and face centre\n error = [centreX-faceX,centreY-faceY]\n #get pid outputs\n panErr = pidPan.update(error[0])\n tiltErr = pidTilt.update(error[1])\n print(\"Face coords: \" + str([faceX, faceY]))\n print(\"Goal coords: \" + str([centreX, centreY]))\n #print(\"Error: \"+ str([panErr,tiltErr]))\n newTilt = tiltAngle - tiltErr\n newPan = panAngle + panErr\n #move servos to that position\n if ptc.in_range(newTilt, tiltRange[0], tiltRange[1]):\n launcher.tilt(newTilt)\n tiltAngle = newTilt #update current tilt angle\n else: #target out of range. Return to default\n launcher.tilt(tiltInit)\n tiltAngle = tiltInit\n \n if ptc.in_range(newPan, panRange[0], panRange[1]): \n launcher.pan(newPan)\n panAngle = newPan #update current pan angle\n else: #return to initial position\n launcher.pan(panInit)\n \n ## Determine whether to turn on motors or shoot\n #Fire if face centre is within 4 pixels of centre (diagonal distance)\n if ((centreX-faceX)**2 + (centreY-faceY)**2) < 4**2 and motorWarmup > 2:\n #motorWarmup makes sure the motors have had enough time to get up to speed\n launcher.load()\n cv2.putText(frame, \"FIRE!\", (120,80), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255),2)\n #Turn on motors if a face is within 15 pixels of centre (diagonal distance)\n if ((centreX-faceX)**2 + (centreY-faceY)**2) < 15**2:\n motorWarmup += 1\n if motorStatus == 0:\n #Don't waste time sending motor on signal if already on\n launcher.motor(1)\n motorStatus = 1\n elif ((centreX-faceX)**2 + (centreY-faceY)**2) >= 15**2 and motorStatus == 1:\n launcher.motor(0)\n motorStatus = 0\n motorWarmup = 0 #Will need to warm up again when switched on\n else: #no face detected\n launcher.motor(0)\n motorStatus = 0\n motorWarmup = 0\n #The above accounts for when a face goes from centred to missing,\n #which can cause the motors to stay on\n\n #display output\n cv2.namedWindow(\"Vid stream\", cv2.WINDOW_NORMAL)\n cv2.resizeWindow('Vid stream', 600,600)\n cv2.imshow(\"Vid stream\", frame)\n cv2.waitKey(1)\n \n \n ","repo_name":"lmjd14/PingPongLauncher","sub_path":"Python Code/trackAndLaunch.py","file_name":"trackAndLaunch.py","file_ext":"py","file_size_in_byte":4139,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"74693293742","text":"from datetime import datetime, timedelta\nfrom django.http import HttpResponse\nfrom django.shortcuts import render,redirect\nfrom accounts.models import *\nfrom coupon.models import Coupon,CouponUser\nfrom .models import Order,OrderDetail,Returned\nfrom cart.models import Cart\nfrom django.utils import timezone\nfrom django.views.decorators.cache import never_cache\nfrom decimal import Decimal\nfrom xhtml2pdf import pisa\n\n\ndef adminOrders(request):\n order = Order.objects.all().order_by('-date_created')\n return render(request,'order/adminorder.html',{'order':order})\n\n@never_cache\ndef filter(request):\n if request.method == 'POST':\n search = request.POST.get('order_id')\n status = request.POST.get('order_status')\n if status:\n orders = Order.objects.filter(orderitems__order_status = status).distinct()\n else:\n orders = Order.objects.all()\n if search:\n element = orders.filter(order_num = search)\n else:\n element = orders\n return render(request,'order/adminorder.html',{'order':element,'select':status,'place':search})\n\ndef adminOrderDetail(request,id):\n order = Order.objects.get(id = id)\n choices = OrderDetail.order_choices\n return render(request,'order/adminorderdetail.html',{'order':order,'choices':choices})\n\ndef status_change(request,id):\n if request.method == 'POST':\n status = request.POST.get('orderstatus')\n order = OrderDetail.objects.get(id = id)\n if status == 'Order Delivered':\n order.date_delivered = timezone.now()\n if status == 'Order Returned':\n amount = Decimal(order.total_price)\n wallet = Wallet.objects.get(user = order.order.user)\n wallet.amount += amount\n wallet.save()\n return_ = order.returned.first()\n return_.date_returned = timezone.now()\n return_.save()\n if status == 'Order Cancelled':\n order.products_price = 0\n order.order_status = status\n order.save()\n return redirect('adminorderdetails',order.order.id)\n return redirect('adminorderdetails',order.order.id)\n\ndef order(request):\n user = Users.objects.get(username = request.session['user_exists'])\n if request.method == 'POST':\n user = user\n address = Address.objects.get(id = request.POST.get('address_radio'))\n order = Order.objects.create(user = user,address = address)\n payment = request.POST.get('payment_method')\n cart = Cart.objects.get(user = user)\n print(payment)\n if payment == 'wallet':\n order.payment_type ='FROM WALLET'\n wallet = Wallet.objects.get(user = user)\n wallet.amount -= Decimal(str(cart.total_price))\n wallet.save()\n elif payment == 'onlinepayment':\n order.payment_type = 'ONLINE PAYMENT'\n else:\n pass\n order.payed_amount = cart.last_price\n order.save()\n cartitems = cart.cartitems.all()\n for items in cartitems:\n OrderDetail.objects.create(order = order,products = items.products,products_price = items.products.price,quantity = items.quantity,varient = items.varient) \n items.products.save() \n if 'coupon' in request.session: \n coupon = Coupon.objects.get(id = request.session['coupon'])\n del request.session['coupon']\n CouponUser.objects.create(coupons = coupon , user = user) \n cart.delete() \n return redirect('ordersuccess',order.order_num) \n \ndef ordersuccess(request,id):\n user = request.session['user_exists']\n return render(request,'order/ordersuccess.html',{'order_id':id,'user_exists':user})\n\ndef orderpage(request):\n user = Users.objects.get(username = request.session['user_exists'])\n order_details = Order.objects.filter(user = user).order_by('-date_created')\n return render(request,'order/orderpage.html', {'order':order_details,'name':'Orders','user_exists':user.username})\n\ndef orderdetails(request,id):\n order = Order.objects.get(id = id)\n user_exists = request.session['user_exists']\n return render(request,'order/orderdetail.html',{'order':order,'user_exists':user_exists})\n\ndef order_cancel(request,id):\n order = OrderDetail.objects.get(id = id)\n main_id = Order.objects.get(orderitems = order).id\n order.order_status = 'Order Cancelled'\n order.save()\n return redirect('orderdetails',main_id)\n\ndef return_reason(request,id):\n order = OrderDetail.objects.get(id = id)\n return render(request,'order/return/returnconf.html',{'order':order})\n\ndef return_confirmation(request):\n if request.method == 'POST':\n order = OrderDetail.objects.get(id = request.POST.get('order_id'))\n reason = request.POST.get('reason')\n discription = request.POST.get('details')\n r_obj = Returned.objects.create(order = order,reason = reason,details = discription)\n r_obj.save()\n order.order_status = 'Order Pending'\n order.save()\n r_id = r_obj.return_num\n return render(request,'order/return/returnconfirm.html',{'r_id':r_id})\n\ndef report(request):\n orders = OrderDetail.objects.all().order_by('-order__date_created')\n earliest_date = OrderDetail.objects.earliest('order__date_created').order.date_created\n fromdate = earliest_date.strftime('%Y-%m-%d')\n todate = timezone.now().strftime('%Y-%m-%d')\n return render(request,'order/report/report.html',{'orders':orders,'fromdate':fromdate,'todate':todate})\n\ndef report_filter(request):\n date_format = '%Y-%m-%d'\n fromdate = request.POST.get('fromDate')\n todate = request.POST.get('toDate')\n todated = datetime.strptime(todate, date_format) + timedelta(days=1)\n orders = OrderDetail.objects.filter(order__date_created__range=(fromdate,todated)).order_by('-order__date_created')\n return render(request,'order/report/report.html',{'orders':orders,'fromdate':fromdate,'todate':todate})\n\ndef report_download(request):\n date_format = '%Y-%m-%d'\n fromdate = request.POST.get('fromDate')\n todate = request.POST.get('toDate')\n todated = datetime.strptime(todate, date_format) + timedelta(days=1)\n orders = OrderDetail.objects.filter(order__date_created__range=(fromdate,todated)).order_by('order__date_created')\n template = 'order/report/reportPDF.html'\n context = {'data': orders,'fromdate':fromdate,'todate':todate} # Replace with your actual context data\n html_string = render(request, template, context).content\n\n response = HttpResponse(content_type='application/pdf')\n response['Content-Disposition'] = 'filename=\"Report.pdf\"'\n\n pisa_status = pisa.CreatePDF(html_string, dest=response)\n if pisa_status.err:\n return HttpResponse('PDF generation error')\n\n return response\n # return render(request,'order/report/reportPDF.html',{'data':orders,'fromdate':fromdate,'todate':todate})\n\ndef invoice(request,id):\n order = Order.objects.get(id = id)\n context = {'order':order}\n template = 'order/invoice/invoice.html'\n html_string = render(request, template,context).content\n response = HttpResponse(content_type='application/pdf')\n pisa_status = pisa.CreatePDF(html_string, dest=response)\n if pisa_status.err:\n return HttpResponse('PDF generation error')\n return response\n\n\n \n\n \n \n\n","repo_name":"shyamkrishnank/-Trendz-e-commerce","sub_path":"order/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7378,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"}
+{"seq_id":"5847427614","text":"\"\"\"\nBezier Polynomial\n@author: l.pannocchi@gmail.com\n\"\"\"\nimport numpy as np\nfrom scipy.optimize import minimize\n#from scipy.optimize import LinearConstraint\n\nnp.set_printoptions(precision=2)\nnp.set_printoptions(suppress=True)\n\n\nimport sys\nimport os\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '.')))\n\nfrom cvxopt import matrix, solvers, spmatrix\n\nimport trjgen.trjgen_core as trj_core\n\n## =================================================\n## =================================================\n# Generate the Pascal Matrix\ndef genPascal(N):\n \"\"\"\n Generate the Pascal Matrix containing\n the coefficient of the binomial expansion\n \"\"\"\n P = np.eye(N, dtype=float)\n P[:, 0] = 1.0\n for i in range(2, N):\n for j in range(1, i):\n P[i,j] = P[i-1, j-1] + P[i-1, j]\n return P\n\ndef genBezierM(deg):\n \"\"\"\n Generate the matrix of binomial coefficients\n \"\"\"\n N = deg + 1\n PascalM = genPascal(N)\n BM = np.zeros((N,N), dtype=float)\n for j in range(N):\n for i in range(j, N):\n if ((i - j) % 2 == 0):\n BM[i, j] = PascalM[N-1, i] * PascalM[i, j]\n else:\n BM[i, j] = -1.0 * PascalM[N-1, i] * PascalM[i, j]\n return BM\n\n\ndef genBaseDM(Nctrp):\n DM = - np.eye(Nctrp - 1, Nctrp) + np.eye(Nctrp - 1, Nctrp, k = 1)\n return DM\n\ndef genDM(deg, der):\n Nctrp = deg + 1\n \n if (der == 0):\n return np.eye(Nctrp)\n\n # Starting matrix for the first derivative.\n DM = genBaseDM(Nctrp) * deg\n\n for d in range(1, der):\n #The range start from 1 to (der - 1)\n DM_ = genBaseDM(Nctrp - d)\n DM = np.matmul(DM_, DM) * (deg - d)\n\n return DM \n\ndef genConstrM(deg, der, s=1.0):\n \"\"\"\n Generate the matrix which maps position control points\n into velocity and acceleration control points\n \"\"\"\n \n O = genDM(deg, der) / s**der\n return O\n\n\n\ndef buildInterpolationProblem(X, deg, s=1.0):\n \"\"\"\n #Build the interpolation problem A * x = b\n\n Args\n X: Matrix of the constraints Nconstr x Nwaypoints\n deg; Degree of the polynomial to fit to describe the pieces\n\n Output:\n A: Matrix of the system\n b: Vector of the known terms\n \"\"\"\n\n nCoef = deg + 1 # Number of coefficient to describe each polynomial\n nConstr = X.shape[0] # Number of constraints (flat outputs)\n nWp = X.shape[1] # Number of waypoints\n nEq = nConstr * 2 # Number of equations\n\n # print(\"Degree of the Bezier curve = \" + str(deg))\n # print(\"Number of control points = \" + str(nCoef))\n # print(\"Number of equality constraints = \" + str(nEq))\n\n\n # Instantiate the output variables\n A = np.zeros((nEq, nCoef), dtype=float)\n b = np.zeros((nEq), dtype=float)\n\n # Define the time vector\n Dt = np.ones((nWp - 1), dtype=float)\n\n # The bezier polynomial can be represented using the\n # standard polynomial power basis [1 t t^2 ...].\n # In this form it is written as T * M, where T is the\n # power basis vector and M is a matrix with binomial\n # coefficients.\n\n M = genBezierM(deg)\n\n counter = 0;\n for i in range(nWp):\n for j in range(nConstr):\n if (i == 0):\n b[counter] = X[j, i]\n v = trj_core.polyder(trj_core.t_vec(0, deg), j) / s**j\n A[counter, :] = np.matmul(v, M)\n else:\n b[counter] = X[j,i]\n v = trj_core.polyder(trj_core.t_vec(Dt[i-1], deg), j) / s**j\n A[counter, :] = np.matmul(v, M)\n counter = counter + 1\n return (A,b)\n\n\n\ndef costFun0(x):\n return 1.0;\n\n\ndef bz_hess(x):\n deg = x.size - 1\n M = genBezierM(deg)\n Q = trj_core.genQ([1.0], deg, 4)\n return 2.0 * M.transpose() * Q * M\n\ndef call_CVXsolver(C, A, b, G, h, s):\n\n nConstr = G.shape[0]\n nCoeff = A.shape[1] - 1\n deg = nCoeff - 1\n\n Ccvx = matrix(C)\n Acvx = matrix(A)\n bcvx = matrix(b)\n Gcvx = matrix(G)\n hcvx = matrix(h)\n\n solvers.options[\"abstol\"] = 1e-6\n solvers.options[\"show_progress\"] = False\n #sol = solvers.qp(Qcvx, matrix(np.zeros(deg+1)) ,Gcvx , hcvx, Acvx, bcvx)\n sol = solvers.lp(c = Ccvx, G = Gcvx , h = hcvx, A = Acvx, b = bcvx)\n\n if sol[\"status\"] == 'optimal':\n #print(\"Solution: \")\n #print(sol['x'])\n\n #print(\"Printing Control Points of the solution\") \n x = sol['x'][0 : nCoeff]\n #for i in range(3):\n # print(np.matmul(genConstrM(deg, i, s), x))\n\n else:\n# print(sol['status'])\n# print(\"C = \")\n# print(C)\n#\n# print(\"A = \")\n# print(A)\n#\n# print(\"b = \")\n# print(b)\n#\n# print(\"G = \")\n# print(G)\n#\n# print(\"h = \")\n# print(h)\n sol[\"x\"] = np.matmul(np.linalg.pinv(A), b)\n# print(\"Min squared solution: \", sol[\"x\"])\n\n# print(\"Printing Control Points of the solution\")\n for i in range(3):\n x = sol['x'][0 : nCoeff]\n #print(np.matmul(genConstrM(deg, i, s), x))\n \n return sol \n\n\ndef genBezierCVX(wp, constr, Q, deg, s=1.0):\n nCoeff = deg + 1\n \n done = False\n iterations = 0;\n s0 = s\n\n # The solution vector is [ctrl_ps, t]\n while ( not done and iterations < 1): \n (A, b) = buildInterpolationProblem(wp, deg, s)\n if (b.any() == False):\n sol = dict()\n sol['x'] = np.zeros(nCoeff + 1)\n return (sol, A, b)\n\n A = np.concatenate((A, np.zeros((A.shape[0], 1))), axis=1)\n \n C = np.zeros(nCoeff + 1)\n C[nCoeff] = 1\n #Q_ = np.copy(Q)\n #Qcvx = matrix(Q_) \n\n G = np.empty(shape=(0, nCoeff + 1))\n h = [] \n CM = genConstrM(deg, 3, s) \n CM = np.concatenate((CM, -CM), axis=0)\n CM = np.concatenate((-CM, -np.ones((CM.shape[0], 1))), axis=1)\n G = np.vstack((G, CM)) \n\n h = np.concatenate((h, np.zeros(G.shape[0])))\n if (constr is not None):\n nConstr = constr.shape[0]\n for i in range(nConstr):\n CM = genConstrM(deg, i, s)\n CM = np.concatenate((CM, np.zeros((CM.shape[0], 1))), axis=1)\n G = np.vstack((G, -CM, CM)) \n\n clow = np.ones(CM.shape[0]) * constr[i, 0]\n cup = np.ones(CM.shape[0]) * constr[i, 1]\n h = np.concatenate((h, -clow, cup))\n else:\n G = np.zeros((nCoeff + 1, nCoeff + 1))\n h = np.zeros(nCoeff + 1)\n\n sol = call_CVXsolver(C, A, b, G, h, s)\n iterations = iterations + 1\n\n if (sol['status'] == 'optimal'):\n done = True\n\n return (sol, A, b)\n\n\n\ndef genBezier(wp, constr, costFun, jac, deg, s=1.0):\n \"\"\"\n wp: Matrix Nder x Npoints\n constr: Nder x [lb ub]\n \"\"\"\n nCoeff = deg + 1\n\n (A, b) = buildInterpolationProblem(wp, deg, s)\n\n # Build the constraint list\n lin_constr = []\n\n# # Interpolation Constraints\n# lin_constr = np.append(lin_constr, LinearConstraint(A, b, b))\n#\n# # Limit Constraints\n# if (constr is not None):\n# nConstr = constr.shape[0]\n# for i in range(nConstr):\n# CM = genConstrM(deg, i, s)\n# clow = np.ones(CM.shape[0]) * constr[i, 0]\n# cup = np.ones(CM.shape[0]) * constr[i, 1]\n# lin_constr = np.append(lin_constr, LinearConstraint(CM, clow, cup))\n#\n# x0 = np.zeros((nCoeff), dtype=float)\n# res = minimize(costFun, x0, method='trust-constr', jac=bz_jac, hess=bz_hess, constraints=lin_constr, options={'verbose':1})\n#\n # Interpolation Constraints\n eq_cons = {'type': 'eq',\n 'fun': lambda x: np.matmul(A,x) - b,\n 'jac': lambda x: A\n }\n\n #lin_constr = np.append(lin_constr, LinearConstraint(A, b, b))\n lin_constr = np.append(lin_constr, eq_cons)\n\n # Limit Constraints\n if (constr is not None):\n nConstr = constr.shape[0]\n for i in range(nConstr):\n CM = genConstrM(deg, i, s)\n clow = np.ones(CM.shape[0]) * constr[i, 0]\n cup = np.ones(CM.shape[0]) * constr[i, 1]\n #lin_constr = np.append(lin_constr, LinearConstraint(CM, clow, cup))\n ineq_cons_up = {'type': 'ineq',\n 'fun': lambda x: np.matmul(-1.0 * CM, x) + cup,\n 'jac': lambda x: -1.0 * CM,\n }\n ineq_cons_down = {'type': 'ineq',\n 'fun': lambda x: np.matmul(CM, x) - clow,\n 'jac': lambda x: CM,\n }\n lin_constr = np.append(lin_constr, ineq_cons_up)\n lin_constr = np.append(lin_constr, ineq_cons_down)\n\n\n x0 = np.linspace(0, 1, nCoeff) * wp[0,1]\n #x0 = np.random.rand(nCoeff) / 10.0\n res = minimize(costFun, x0, method='SLSQP', jac=jac,\n constraints=lin_constr, options={'disp':True})\n\n return (res, A, b)\n\n\ndef genCostM(deg, order, s=1.0):\n M = genBezierM(deg)\n Q = trj_core.genQ([s], deg, order)\n C = M.transpose() * Q * M\n return C\n\n\nclass Bezier :\n ## Constructor\n def __init__(self, cntp=None, waypoints=None, constraints=None, degree=None, s=1.0, opt_der=4):\n # Asking for interpolation\n self.duration = s\n self.opt_der = opt_der\n\n if (waypoints is not None and degree is not None):\n # Store the waypoints\n self.wp = waypoints\n\n # Store the constraints\n self.cnstr = constraints\n\n # Degree of the polynomial\n self.degree = degree\n\n # Timespan\n self.duration = s\n\n\n # This was the integral cost\n #M = genBezierM(self.degree)\n #Q = trj_core.genQ([1.0], degree, opt_der) / s**(2 * opt_der - 1)\n # print(\"Basic Const = \") \n # print(Q)\n #self.Q = M.transpose() * Q * M\n #self.Q = (self.Q / np.max(self.Q))\n \n D = genDM(degree, opt_der)\n self.Q = np.matmul(np.transpose(D), D)\n\n # Interpolation problem\n #(sol, A, b) = genBezier(self.wp, self.cnstr, self.costFun, self.bz_jac, self.degree, self.duration)\n (sol, A, b) = genBezierCVX(self.wp, self.cnstr, self.Q, self.degree, self.duration)\n \n # Control points of the bezier curve\n #self.cntp = np.array(sol.x)\n self.cntp = np.array(sol[\"x\"][0: self.degree + 1])\n\n elif (cntp is not None):\n self.cntp = cntp\n\n self.degree = len(cntp) - 1\n\n # Evaluate the Bezier polynomial\n def eval(self, t, der = [0]):\n \"\"\"\n Evaluate the Bezier polynomial\n\n Args:\n t time\n der derivative to evaluate\n \"\"\"\n\n t = t / self.duration\n if (t < 0.0) or (t > 1.0):\n print(\"Warning! to evaluate outside \" +\n \"the time support of the Bezier polynomial: \", t * self.duration)\n if (t < 0.0):\n t = 0.0\n else:\n t = 1.0\n # Create the time vector and the derivatives\n N_der = len(der)\n\n t_ = trj_core.t_vec(t, self.degree)\n\n M = genBezierM(self.degree)\n\n t_der = np.zeros((N_der, t_.size))\n yout = np.zeros(N_der)\n for d in range(N_der):\n t_der[d, :] = trj_core.polyder(t_, der[d]) / self.duration**der[d]\n yout[d] = np.matmul(np.matmul(t_der[d,:], M), self.cntp)\n\n return yout\n\n\n ## Function to retrieve valuesCOBYLACOBYLA\n\n def getWaypoints(self):\n \"\"\"\n Returns the waypoints of the piecewise polynomial\n \"\"\"\n wp = self.wp\n return np.array(wp)\n\n def getControlPts(self):\n \"\"\"\n Returns the control points of the Bezier polynomial\n \"\"\"\n coeff = self.cntp\n return np.array(coeff)\n\n def costFun(self, x):\n \"\"\"\n Generate the const function for the optimization problem\n \"\"\"\n cost = np.matmul(x, self.Q).dot(x)\n return cost\n\n def bz_jac(self, x):\n return 2.0 * np.matmul(x, self.Q)\n\n\n","repo_name":"rt-2pm2/trjgen","sub_path":"trjgen/class_bz.py","file_name":"class_bz.py","file_ext":"py","file_size_in_byte":12122,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"13940058591","text":"from panda3d.core import *\nfrom direct.showbase.ShowBase import ShowBase\nimport sys\nfrom direct.gui.OnscreenImage import OnscreenImage \nfrom direct.gui.OnscreenText import OnscreenText \n\nclass player():\n\tdef __init__(self):\n\n\t\tself.picker = CollisionTraverser()\n\t\tself.picker.showCollisions(render)\n\t\tself.pq = CollisionHandlerQueue() \n\n\t\tself.pickerNode = CollisionNode('mouseRay')\n\t\tself.pickerNP = camera.attachNewNode(self.pickerNode)\n\n\n\n\t\tself.pickerRay = CollisionRay()\n\t\tself.pickerNode.addSolid(self.pickerRay)\n\t\tself.picker.addCollider(self.pickerNP,self.pq)\n\n\t\tself.l = []\n\t\tself.step = 1\n\t\tself.set= 1\n\t\tself.player = 'white'\n\t\tself.text =OnscreenText(text = 'player - '+self.player, parent=base.a2dTopLeft, scale=0.06,\n pos=(0.05,-0.07,0), fg=(1, 1, 1, 1),\n shadow=(0, 0, 0, 0.5), align=TextNode.ALeft) \n\n\tdef addFigure(self):\n\t\tif base.mouseWatcherNode.hasMouse() and self.set == 1:\n\n\t\t\tmpos = base.mouseWatcherNode.getMouse()\n\t\t\tmw = base.mouseWatcherNode.getMouse()\n \n\t\t\tx, y = mw.getX(), mw.getY()\n\n\t\t\tself.pickerRay.setFromLens(base.camNode,mpos.getX(),mpos.getY())\n\n\t\t\tself.picker.traverse(render)\n\n\t\t\tif self.pq.getNumEntries() > 0 :\n\t\t\t\t\tself.pq.sortEntries()\n\t\t\t\t\tpickedObj = self.pq.getEntry(0).getIntoNodePath()\n\n\t\t\t\t\tx,y,z = self.pq.getEntry(0).getIntoNodePath().getPos()\n\t\t\t\t\t################ Если чёрный ################\n\t\t\t\t\tif pickedObj.getName()=='black' and pickedObj.getName()!='blackF' and pickedObj.getName() != 'whiteF' and y > 4:\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\tx,y,z = self.pq.getEntry(0).getIntoNodePath().getPos()\n\t\t\t\t\t\t\tcs = CollisionBox(0.5, 0.5, 0.5, 0.5)\n\t\t\t\t\t\t\tpcn1 = render.attachNewNode(CollisionNode('blackF'))\n\t\t\t\t\t\t\tpcn1.node().addSolid(cs)\n\n\t\t\t\t\t\t\tpcn1.show()\n\t\t\t\t\t\t\tg=loader.loadModel('models/box')\n\t\t\t\t\t\t\tg.setName('blackF')\n\t\t\t\t\t\t\tg.setScale(1,1,1)\n\n\t\t\t\t\t\t\ttex = loader.loadTexture(\"figures/blackF.png\")\n\t\t\t\t\t\t\tpcn1.setTexture(tex, 1) \n\t\t\t\t\t\t\tg.copyTo(pcn1)\n\t\t\t\t\t\t\tpcn1.setPos(int(x),int(y),int(1))\n\n\t\t\t\t\t################ Если белый ################\n\t\t\t\t\telif pickedObj.getName() == 'black' and pickedObj.getName() != 'whiteF' and pickedObj.getName() != 'blackF' and y <3:\n\t\t\t\t\t\t\tx,y,z = self.pq.getEntry(0).getIntoNodePath().getPos()\n\t\t\t\t\t\t\tcs = CollisionBox(0.5, 0.5, 0.5, 0.5)\n\t\t\t\t\t\t\tpcn1 = render.attachNewNode(CollisionNode('whiteF'))\n\t\t\t\t\t\t\tpcn1.node().addSolid(cs)\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\tpcn1.show()\n\t\t\t\t\t\t\tg=loader.loadModel('models/box')\n\t\t\t\t\t\t\tg.setName('whiteF')\n\t\t\t\t\t\t\tg.setScale(1,1,1)\n\n\t\t\t\t\t\t\ttex = loader.loadTexture(\"figures/whiteF.png\")\n\t\t\t\t\t\t\tpcn1.setTexture(tex, 1) \n\t\t\t\t\t\t\tg.copyTo(pcn1)\n\t\t\t\t\t\t\tpcn1.setPos(int(x),int(y),int(1))\n\n\n\n\tdef movement(self):\n\n\t\tif base.mouseWatcherNode.hasMouse():\n\n\t\t\tmpos = base.mouseWatcherNode.getMouse()\n\n\t\t\tself.pickerRay.setFromLens(base.camNode,mpos.getX(),mpos.getY())\n\n\t\t\tself.picker.traverse(render)\n\t\t\t\n\t\t\tif self.pq.getNumEntries() > 0:\n\t\t\t\t\tself.pq.sortEntries()\n\t\t\t\t\tpickedObj = self.pq.getEntry(0).getIntoNodePath()\n\n\t\t\t\t\t################ 1 клик ################\n\t\t\t\t\tif self.step == 1 and pickedObj.getZ() != 0: # если не кусок карты, а фигура\n\t\t\t\t\t\tpickedObj = self.pq.getEntry(0).getIntoNodePath()\n\n\t\t\t\t\t\tpickedObj = self.pq.getEntry(0).getIntoNodePath()\n\t\t\t\t\t\tself.x,self.y,self.z = pickedObj.getPos()\n\t\t\t\t\t\tself.Np = pickedObj\n\n\t\t\t\t\t\tself.figure = pickedObj.getName()\n\t\t\t\t\t\tself.step = 2\n\t\t\t\t\t################ 2 клик ################\n\t\t\t\t\telif self.step == 2:\n\n \n\t\t\t\t\t\tif self.pq.getEntry(0).getIntoNodePath().getZ() != 0:\n\n\n\t\t\t\t\t\t\tx,y,z = self.pq.getEntry(0).getIntoNodePath().getPos()\n\t\t\t\t\t\t\tif y > self.y and y < self.y+2 and self.figure == 'whiteF' and self.player == 'white': # если кликнули на белую фишку\n \n\t\t\t\t\t\t\t\tself.Np.setPos(int(x),int(y),1)\n\n\t\t\t\t\t\t\t\tself.player = 'black'\n\t\t\t\t\t\t\t\tif self.Np.getY() > 6:\n\t\t\t\t\t\t\t\t\tself.Np.setName('kingW')\n\n\t\t\t\t\t\t\t\t\ttex = loader.loadTexture(\"figures/white_king.png\")\n\t\t\t\t\t\t\t\t\tself.Np.setTexture(tex, 1) \n\t\t\t\t\t\t\telif y < self.y and y > self.y-2 and self.figure == 'blackF' and self.player == 'black':# если кликнули на чёрную фишку\n \t\n\n\t\t\t\t\t\t\t\tself.Np.setPos(int(x),int(y),1)\n\n\t\t\t\t\t\t\t\tself.player = 'white'\n\t\t\t\t\t\t\t\tif self.Np.getY() <1:\n\t\t\t\t\t\t\t\t\tself.Np.setName('kingB')\n\n\t\t\t\t\t\t\t\t\ttex = loader.loadTexture(\"figures/black_king.png\")\n\t\t\t\t\t\t\t\t\tself.Np.setTexture(tex, 1) \n\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\tself.step = 1\n\n\n########################### Eat figures ###########################\n\t\t\t\t\t\tif self.pq.getEntry(0).getIntoNodePath().getName() != 'white' and self.pq.getEntry(0).getIntoNodePath().getName() != 'black':\n\t\t\t\t\t\t\tx,y,z = self.pq.getEntry(0).getIntoNodePath().getPos()\n\t\t\t\t\t\t\tif y < self.y + 2 and y > self.y-2 and self.figure != self.pq.getEntry(0).getIntoNodePath().getName(): # если не фигура одного цвета\n\t\t\t\t\t\t\t\tif self.figure == 'whiteF' and self.player == 'white' and (self.x,self.y) != (x,y):# если кликнули на белую фишку\n\t\t\t\t\t\t\t\t\t\t\t\t\tif self.Np.getY() > 6:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.Np.setName('kingW')\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\ttex = loader.loadTexture(\"figures/white_king.png\")\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.Np.setTexture(tex, 1) \n\n\t\t\t\t\t\t\t\t\t\t\t\t\tg=loader.loadModel('models/box')\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tg.setScale(1,1,1)\n\t\t\t\t\t\t\t\t\t\t\t\t\tg.setPos(int(x+(x-self.Np.getX())),int(y+1),1)\n\t\t\t\t\t\t\t\t\t\t\t\t\tg.setName('GGGGGGGG')\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tfor i in render.findAllMatches(\"blackF\"):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.l.append(i.getPos())\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tif str(self.l).find(str(g.getPos())) < 0 and int(g.getY()) != 8 and int(g.getY()) != -1 and int(g.getX()) != 8 and int(g.getX())!= -1: # если впереди, куда я хожу нет фишки\n\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.Np.setPos(int(x+(x-self.Np.getX())),int(y+(y-self.Np.getY())),1)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.pq.getEntry(0).getIntoNodePath().remove()\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tg.remove()\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t#break\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.l = []\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.step = 1\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\telif self.figure == 'blackF' and self.player == 'black' and (self.x,self.y) != (x,y):# если кликнули на чёрную фишку\n\t\t\t\t\t\t\t\t\t\t\tif self.Np.getY() <1:\n\t\t\t\t\t\t\t\t\t\t\t\tself.Np.setName('kingB')\n\n\t\t\t\t\t\t\t\t\t\t\t\ttex = loader.loadTexture(\"figures/black_king.png\")\n\t\t\t\t\t\t\t\t\t\t\t\tself.Np.setTexture(tex, 1) \n\t\t\t\t\t\t\t\t\t\t\tg=loader.loadModel('models/box')\n\n\t\t\t\t\t\t\t\t\t\t\tg.setScale(1,1,1)\n\t\t\t\t\t\t\t\t\t\t\tg.setPos(int(x+(x-self.Np.getX())),int(y-1),1)\n\t\t\t\t\t\t\t\t\t\t\tg.setName('GGGGGGGG')\n\n\n\n\t\t\t\t\t\t\t\t\t\t\tfor i in render.findAllMatches(\"whiteF\"):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.l.append((i.getX(),i.getY()))\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tif str(self.l).find(str((g.getX(),g.getY()))) < 0 and int(g.getY()) != 8 and int(g.getY()) != -1 and int(g.getX()) != 8 and int(g.getX())!= -1: # если впереди, куда я хожу нет фишки\n\t\t\t\t\t\t\t\t\t\t\t\tself.Np.setPos(int(x+(x-self.Np.getX())),int(y+(y-self.Np.getY())),1)\n\t\t\t\t\t\t\t\t\t\t\t\tself.pq.getEntry(0).getIntoNodePath().remove()\n\n\t\t\t\t\t\t\t\t\t\t\t\tg.remove()\n\t\t\t\t\t\t\t\t\t\t\t\t#break\n\t\t\t\t\t\t\t\t\t\t\tself.l = []\n\t\t\t\t\t\t\t\t\t\t\tself.step = 1\n\n\n\n\n########################### Eat kings ###########################\n\n\t\t\t\t\tself.text.setText('player - '+self.player)\n","repo_name":"Arsbul-hub/3D-chessboard","sub_path":"player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":7187,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"71360703342","text":"class TaskDTO(object):\n\n def __init__(self, task_id, name, additional_info, priority, is_completed):\n self.task_id = task_id\n self.name = name\n self.additional_info = additional_info\n self.priority = priority\n self.is_completed = is_completed\n\n\nclass WorkerDTO(object):\n\n def __init__(self, worker_id, first_name, last_name, role, is_busy):\n self.worker_id = worker_id\n self.first_name = first_name\n self.last_name = last_name\n self.role = role\n self.is_busy = is_busy\n","repo_name":"St1r3S/teamTaskManagementProj","sub_path":"teamTaskManager/dtos.py","file_name":"dtos.py","file_ext":"py","file_size_in_byte":545,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"}
+{"seq_id":"12600968430","text":"import sqlalchemy\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy import Column, Integer, String\nfrom sqlalchemy.orm import sessionmaker\n\n# check version\n# print(sqlalchemy.__version__)\n# create connection; echo 表示是否输出日志\nengine = create_engine('sqlite:///:memory:', echo=False)\n# 创建ORM映射基类\nBase = declarative_base()\n\n\n# 创建ORM对象类\nclass User(Base):\n __tablename__ = 'users'\n id = Column(Integer, primary_key=True)\n name = Column(String)\n fullname = Column(String)\n nickname = Column(String)\n\n def __repr__(self):\n return \"